AIRLINE TICKET PRICING
Ayan Ghosh
21 December 2017
this is the Premium Economy Vs Economy Ticket Pricing by Airlines Project A short Description:-
DATA FIELD DESCRIPTION – PREMIUM ECONOMY VS ECONOMY
This dataset describes information about Premium Economy and Regular Economy Air Tickets.
-We collected data in May for hypothetical future travel in July, August, September and October. -We visited the websites of the following six airlines British Airways, Delta Airlines, Air France, Singapore Airlines, Virgin Airlines, Jet Airways. -We selected a variety of routes on which each airline flies (e.g. British Airways flying from London to Frankfurt). -We recorded the price of purchasing a Economy Ticket and a Premium Economy Ticket from the airline website. -We used the website Seat Guru to get additional information about the Aircraft being used by each airline on each route. -This generated a dataset having the following data fields:
DATA FIELD UNITS MEANING
Airline Factor Factor variable denoting the name of the There are 6 airlines in the data: British Airways Delta Airlines Air France Singapore Airlines Virgin Airlines Jet Airways
Aircraft Factor Manufacturer of the Airplane / Aircraft e.g.
Boeing
Airbus TravelMonth Factor Factor variable denoting the month Travel
Jul
Aug
Sep
OctFlightDuration Hours Flight Duration
IsInternational Factor International or Domestic Flight w.r.t. Airlines’ Home Country
SeatsEconomy Number Number of Economy Seats in the Aircraft
SeatsPremium Number Number of Premium Economy Seats in the Aircraft
PitchEconomy Number (Inches) Distance between two consecutive Economy Seats
PitchPremium Number (Inches) Distance between two consecutive Premium Seats
WidthEconomy Number (Inches) Width between armrests of an Economy Seat
WidthPremium Number (Inches) Width between armrests of a Premium Economy Seat
PriceEconomy Number (USD) Price of Economy Seat
PricePremium Number (USD) Price of Premium Economy Seat
PriceRelative (PricePremium - PriceEconomy) / PriceEconomy
SeatsTotal SeatsEconomy + SeatsPremium
PercentPremiumSeats (SeatsPremium / SeatsTotal) * 100
PitchDifference PitchPremium - PitchEconomy
WidthDifference WidthPremium - WidthEconomy
setwd("G:/IIEST 2K15-2K20/Intern/Winter Internship/Resources/Week 3/week 3 day 3")
airline <- read.csv(paste("SixAirlinesDataV2.csv", sep=""))
View(airline)
summary(airline)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 74 AirBus:151 Min. : 1.250 Aug:127
## British :175 Boeing:307 1st Qu.: 4.260 Jul: 75
## Delta : 46 Median : 7.790 Oct:127
## Jet : 61 Mean : 7.578 Sep:129
## Singapore: 40 3rd Qu.:10.620
## Virgin : 62 Max. :14.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 40 Min. : 78.0 Min. : 8.00 Min. :30.00
## International:418 1st Qu.:133.0 1st Qu.:21.00 1st Qu.:31.00
## Median :185.0 Median :36.00 Median :31.00
## Mean :202.3 Mean :33.65 Mean :31.22
## 3rd Qu.:243.0 3rd Qu.:40.00 3rd Qu.:32.00
## Max. :389.0 Max. :66.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 65
## 1st Qu.:38.00 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 413
## Median :38.00 Median :18.00 Median :19.00 Median :1242
## Mean :37.91 Mean :17.84 Mean :19.47 Mean :1327
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:1909
## Max. :40.00 Max. :19.00 Max. :21.00 Max. :3593
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86.0 Min. :0.0200 Min. : 98 Min. : 2.000
## 1st Qu.: 528.8 1st Qu.:0.1000 1st Qu.:166 1st Qu.: 6.000
## Median :1737.0 Median :0.3650 Median :227 Median : 7.000
## Mean :1845.3 Mean :0.4872 Mean :236 Mean : 6.688
## 3rd Qu.:2989.0 3rd Qu.:0.7400 3rd Qu.:279 3rd Qu.: 7.000
## Max. :7414.0 Max. :1.8900 Max. :441 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :0.000 Min. : 4.71
## 1st Qu.:1.000 1st Qu.:12.28
## Median :1.000 Median :13.21
## Mean :1.633 Mean :14.65
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :4.000 Max. :24.69library(psych)
describe(airline)## vars n mean sd median trimmed mad min
## Airline* 1 458 3.01 1.65 2.00 2.89 1.48 1.00
## Aircraft* 2 458 1.67 0.47 2.00 1.71 0.00 1.00
## FlightDuration 3 458 7.58 3.54 7.79 7.57 4.81 1.25
## TravelMonth* 4 458 2.56 1.17 3.00 2.58 1.48 1.00
## IsInternational* 5 458 1.91 0.28 2.00 2.00 0.00 1.00
## SeatsEconomy 6 458 202.31 76.37 185.00 194.64 85.99 78.00
## SeatsPremium 7 458 33.65 13.26 36.00 33.35 11.86 8.00
## PitchEconomy 8 458 31.22 0.66 31.00 31.26 0.00 30.00
## PitchPremium 9 458 37.91 1.31 38.00 38.05 0.00 34.00
## WidthEconomy 10 458 17.84 0.56 18.00 17.81 0.00 17.00
## WidthPremium 11 458 19.47 1.10 19.00 19.53 0.00 17.00
## PriceEconomy 12 458 1327.08 988.27 1242.00 1244.40 1159.39 65.00
## PricePremium 13 458 1845.26 1288.14 1737.00 1799.05 1845.84 86.00
## PriceRelative 14 458 0.49 0.45 0.36 0.42 0.41 0.02
## SeatsTotal 15 458 235.96 85.29 227.00 228.73 90.44 98.00
## PitchDifference 16 458 6.69 1.76 7.00 6.76 0.00 2.00
## WidthDifference 17 458 1.63 1.19 1.00 1.53 0.00 0.00
## PercentPremiumSeats 18 458 14.65 4.84 13.21 14.31 2.68 4.71
## max range skew kurtosis se
## Airline* 6.00 5.00 0.61 -0.95 0.08
## Aircraft* 2.00 1.00 -0.72 -1.48 0.02
## FlightDuration 14.66 13.41 -0.07 -1.12 0.17
## TravelMonth* 4.00 3.00 -0.14 -1.46 0.05
## IsInternational* 2.00 1.00 -2.91 6.50 0.01
## SeatsEconomy 389.00 311.00 0.72 -0.36 3.57
## SeatsPremium 66.00 58.00 0.23 -0.46 0.62
## PitchEconomy 33.00 3.00 -0.03 -0.35 0.03
## PitchPremium 40.00 6.00 -1.51 3.52 0.06
## WidthEconomy 19.00 2.00 -0.04 -0.08 0.03
## WidthPremium 21.00 4.00 -0.08 -0.31 0.05
## PriceEconomy 3593.00 3528.00 0.51 -0.88 46.18
## PricePremium 7414.00 7328.00 0.50 0.43 60.19
## PriceRelative 1.89 1.87 1.17 0.72 0.02
## SeatsTotal 441.00 343.00 0.70 -0.53 3.99
## PitchDifference 10.00 8.00 -0.54 1.78 0.08
## WidthDifference 4.00 4.00 0.84 -0.53 0.06
## PercentPremiumSeats 24.69 19.98 0.71 0.28 0.23Here we have six different airlines.First of all we divide all the airlines into 6 groups for better understanding.
British Airlines
Analyse all about British Airlines:-
British <- airline[ which(airline$Airline=='British'),]
View(British)
summary(British)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 47 Min. : 1.250 Aug:52
## British :175 Boeing:128 1st Qu.: 4.290 Jul:16
## Delta : 0 Median : 8.580 Oct:53
## Jet : 0 Mean : 7.855 Sep:54
## Singapore: 0 3rd Qu.:11.120
## Virgin : 0 Max. :13.830
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :122.0 Min. :24.00 Min. :31
## International:175 1st Qu.:122.0 1st Qu.:36.00 1st Qu.:31
## Median :243.0 Median :40.00 Median :31
## Mean :216.6 Mean :43.18 Mean :31
## 3rd Qu.:303.0 3rd Qu.:55.00 3rd Qu.:31
## Max. :312.0 Max. :56.00 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :38 Min. :18 Min. :19 Min. : 65.0
## 1st Qu.:38 1st Qu.:18 1st Qu.:19 1st Qu.: 528.5
## Median :38 Median :18 Median :19 Median :1444.0
## Mean :38 Mean :18 Mean :19 Mean :1293.5
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:19 3rd Qu.:1813.0
## Max. :38 Max. :18 Max. :19 Max. :3102.0
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86.0 Min. :0.0400 Min. :162.0 Min. :7
## 1st Qu.: 807.5 1st Qu.:0.2100 1st Qu.:162.0 1st Qu.:7
## Median :2049.0 Median :0.3600 Median :279.0 Median :7
## Mean :1937.0 Mean :0.4375 Mean :259.8 Mean :7
## 3rd Qu.:2982.0 3rd Qu.:0.5200 3rd Qu.:358.0 3rd Qu.:7
## Max. :7414.0 Max. :1.3900 Max. :367.0 Max. :7
## WidthDifference PercentPremiumSeats
## Min. :1 Min. :10.57
## 1st Qu.:1 1st Qu.:12.90
## Median :1 Median :15.36
## Mean :1 Mean :17.79
## 3rd Qu.:1 3rd Qu.:24.69
## Max. :1 Max. :24.69Check the all the means now all british aircrafts
mean(British$PriceEconomy)## [1] 1293.48mean(British$PricePremium)## [1] 1937.029mean(British$FlightDuration)## [1] 7.854971mean(British$PitchEconomy)## [1] 31mean(British$PitchPremium)## [1] 38mean(British$WidthEconomy)## [1] 18mean(British$WidthPremium)## [1] 19mean(British$PriceRelative)## [1] 0.4375429mean(British$PitchDifference)## [1] 7mean(British$WidthDifference)## [1] 1Now Analyse separately for Each Aircrafts in British Airlines i.e-Boeing and AirBus
Brboeing <- British[ which(British$Aircraft=='Boeing'),]
View(Brboeing)
summary(Brboeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 0 Min. : 1.250 Aug:39
## British :128 Boeing:128 1st Qu.: 6.810 Jul:10
## Delta : 0 Median : 8.910 Oct:39
## Jet : 0 Mean : 8.747 Sep:40
## Singapore: 0 3rd Qu.:11.410
## Virgin : 0 Max. :13.830
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :122.0 Min. :24.00 Min. :31
## International:128 1st Qu.:122.0 1st Qu.:36.00 1st Qu.:31
## Median :203.0 Median :40.00 Median :31
## Mean :184.8 Mean :38.84 Mean :31
## 3rd Qu.:243.0 3rd Qu.:40.00 3rd Qu.:31
## Max. :303.0 Max. :56.00 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :18 Min. :19 Min. : 65 Min. : 86
## 1st Qu.:38 1st Qu.:18 1st Qu.:19 1st Qu.:1126 1st Qu.:1539
## Median :38 Median :18 Median :19 Median :1609 Median :2236
## Mean :38 Mean :18 Mean :19 Mean :1515 Mean :2261
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:19 3rd Qu.:1813 3rd Qu.:2989
## Max. :38 Max. :18 Max. :19 Max. :3102 Max. :7414
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0400 Min. :162.0 Min. :7 Min. :1
## 1st Qu.:0.3300 1st Qu.:162.0 1st Qu.:7 1st Qu.:1
## Median :0.3800 Median :227.0 Median :7 Median :1
## Mean :0.4795 Mean :223.6 Mean :7 Mean :1
## 3rd Qu.:0.5625 3rd Qu.:279.0 3rd Qu.:7 3rd Qu.:1
## Max. :1.3900 Max. :358.0 Max. :7 Max. :1
## PercentPremiumSeats
## Min. :10.57
## 1st Qu.:12.90
## Median :18.73
## Mean :18.69
## 3rd Qu.:24.69
## Max. :24.69mean(Brboeing$PriceEconomy)## [1] 1515.328mean(Brboeing$PricePremium)## [1] 2260.586library(plotly)## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutx<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Brboeing$PriceEconomy,Brboeing$TravelMonth,mean))
y2<-c(by(Brboeing$PricePremium,Brboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1389.0 -356.3 178.7 373.6 1100.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 679.18 155.63 4.364 2.63e-05 ***
## FlightDuration 95.59 16.77 5.701 8.02e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 588.7 on 126 degrees of freedom
## Multiple R-squared: 0.205, Adjusted R-squared: 0.1987
## F-statistic: 32.5 on 1 and 126 DF, p-value: 8.021e-08Brboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 137
## [71] 109 77 77 69 65 1651 1651 2775 2230 2230 2230 2356 2356 2356
## [85] 2356 1562 1562 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609
## [99] 1609 1632 1632 1632 1140 1736 1736 1736 846 846 937 1485 891 1323
## [113] 1023 1023 757 533 3102 3102 3102 2166 2166 2166 649 575 575 797
## [127] 524 582fitted(fit)## 1 2 3 4 5 6 7
## 1850.1845 1850.1845 1850.1845 1850.1845 1459.2125 1459.2125 1459.2125
## 8 9 10 11 12 13 14
## 1300.5295 1300.5295 1778.4904 1778.4904 1778.4904 1778.4904 1786.1378
## 15 16 17 18 19 20 21
## 1786.1378 1786.1378 1554.8047 1554.8047 1554.8047 1324.4276 1324.4276
## 22 23 24 25 26 27 28
## 1324.4276 1315.8243 1315.8243 1315.8243 1515.6119 1515.6119 1515.6119
## 29 30 31 32 33 34 35
## 1148.5380 1148.5380 1148.5380 1045.2984 1045.2984 1045.2984 1045.2984
## 36 37 38 39 40 41 42
## 1969.6748 1969.6748 1969.6748 1045.2984 1045.2984 1045.2984 1045.2984
## 43 44 45 46 47 48 49
## 1196.3340 1196.3340 1196.3340 1467.8158 1467.8158 1467.8158 1897.9806
## 50 51 52 53 54 55 56
## 1897.9806 1897.9806 1300.5295 1738.3417 1738.3417 1738.3417 1260.3808
## 57 58 59 60 61 82 83
## 1260.3808 1260.3808 1874.0826 1831.0661 1874.0826 1332.0749 1332.0749
## 84 85 86 87 88 89 138
## 1332.0749 1332.0749 1403.7691 1403.7691 1403.7691 1403.7691 798.6706
## 144 147 148 149 151 240 241
## 798.6706 806.3180 806.3180 798.6706 806.3180 1674.2949 1674.2949
## 242 243 244 245 246 247 248
## 1674.2949 1730.6943 1730.6943 1730.6943 1626.4988 1626.4988 1626.4988
## 249 250 251 252 253 254 255
## 1626.4988 1499.3612 1499.3612 1499.3612 1769.8871 1769.8871 1769.8871
## 256 257 258 259 260 261 262
## 1769.8871 1571.0554 1571.0554 1571.0554 1530.9067 1507.0086 1507.0086
## 263 264 265 266 267 268 269
## 1507.0086 1372.2236 1372.2236 1372.2236 1530.9067 1355.9730 1355.9730
## 270 271 272 273 274 275 276
## 1355.9730 1769.8871 1769.8871 1769.8871 1769.8871 1530.9067 1738.3417
## 277 278 279 280 367 368 369
## 1738.3417 1738.3417 1355.9730 1738.3417 2001.2202 2001.2202 2001.2202
## 370 371 372 373 374 375 376
## 1953.4241 1953.4241 1953.4241 1530.9067 1530.9067 1530.9067 1594.9534
## 377 378
## 1594.9534 1594.9534cor(Brboeing$PriceEconomy,Brboeing$FlightDuration)## [1] 0.4528194fit<-lm(PriceEconomy~SeatsEconomy,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1310.60 -462.54 50.37 366.48 1608.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1733.7470 182.9411 9.477 <2e-16 ***
## SeatsEconomy -1.1820 0.9389 -1.259 0.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 656.1 on 126 degrees of freedom
## Multiple R-squared: 0.01242, Adjusted R-squared: 0.004583
## F-statistic: 1.585 on 1 and 126 DF, p-value: 0.2104Brboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 137
## [71] 109 77 77 69 65 1651 1651 2775 2230 2230 2230 2356 2356 2356
## [85] 2356 1562 1562 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609
## [99] 1609 1632 1632 1632 1140 1736 1736 1736 846 846 937 1485 891 1323
## [113] 1023 1023 757 533 3102 3102 3102 2166 2166 2166 649 575 575 797
## [127] 524 582fitted(fit)## 1 2 3 4 5 6 7 8
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 9 10 11 12 13 14 15 16
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 17 18 19 20 21 22 23 24
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 25 26 27 28 29 30 31 32
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 33 34 35 36 37 38 39 40
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 41 42 43 44 45 46 47 48
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 49 50 51 52 53 54 55 56
## 1589.544 1589.544 1589.544 1583.634 1583.634 1583.634 1583.634 1583.634
## 57 58 59 60 61 82 83 84
## 1583.634 1583.634 1583.634 1583.634 1583.634 1446.523 1446.523 1446.523
## 85 86 87 88 89 138 144 147
## 1446.523 1446.523 1446.523 1446.523 1446.523 1375.604 1375.604 1375.604
## 148 149 151 240 241 242 243 244
## 1375.604 1375.604 1375.604 1446.523 1446.523 1446.523 1446.523 1446.523
## 245 246 247 248 249 250 251 252
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 253 254 255 256 257 258 259 260
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 261 262 263 264 265 266 267 268
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 269 270 271 272 273 274 275 276
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 277 278 279 280 367 368 369 370
## 1446.523 1446.523 1446.523 1446.523 1493.803 1493.803 1493.803 1493.803
## 371 372 373 374 375 376 377 378
## 1493.803 1493.803 1493.803 1493.803 1493.803 1493.803 1493.803 1493.803cor(Brboeing$PriceEconomy,Brboeing$SeatsEconomy)## [1] -0.1114495fit<-lm(PriceEconomy~PriceRelative,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1430.07 -325.06 81.24 322.10 1400.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1417.3 107.8 13.15 <2e-16 ***
## PriceRelative 204.4 189.3 1.08 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 657.2 on 126 degrees of freedom
## Multiple R-squared: 0.009169, Adjusted R-squared: 0.001306
## F-statistic: 1.166 on 1 and 126 DF, p-value: 0.2823Brboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 137
## [71] 109 77 77 69 65 1651 1651 2775 2230 2230 2230 2356 2356 2356
## [85] 2356 1562 1562 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609
## [99] 1609 1632 1632 1632 1140 1736 1736 1736 846 846 937 1485 891 1323
## [113] 1023 1023 757 533 3102 3102 3102 2166 2166 2166 649 575 575 797
## [127] 524 582fitted(fit)## 1 2 3 4 5 6 7 8
## 1494.984 1494.984 1494.984 1494.984 1554.260 1554.260 1554.260 1627.844
## 9 10 11 12 13 14 15 16
## 1627.844 1570.612 1570.612 1531.776 1470.456 1523.600 1523.600 1523.600
## 17 18 19 20 21 22 23 24
## 1494.984 1494.984 1494.984 1486.808 1486.808 1486.808 1484.764 1484.764
## 25 26 27 28 29 30 31 32
## 1484.764 1488.852 1484.764 1484.764 1486.808 1486.808 1486.808 1503.160
## 33 34 35 36 37 38 39 40
## 1503.160 1503.160 1503.160 1550.172 1550.172 1550.172 1466.368 1466.368
## 41 42 43 44 45 46 47 48
## 1466.368 1466.368 1452.060 1452.060 1452.060 1433.664 1433.664 1433.664
## 49 50 51 52 53 54 55 56
## 1523.600 1523.600 1523.600 1627.844 1490.896 1490.896 1490.896 1486.808
## 57 58 59 60 61 82 83 84
## 1486.808 1486.808 1460.236 1460.236 1541.996 1636.020 1636.020 1636.020
## 85 86 87 88 89 138 144 147
## 1636.020 1499.072 1499.072 1499.072 1499.072 1470.456 1478.632 1476.588
## 148 149 151 240 241 242 243 244
## 1476.588 1499.072 1484.764 1648.284 1648.284 1470.456 1509.292 1509.292
## 245 246 247 248 249 250 251 252
## 1509.292 1490.896 1490.896 1490.896 1490.896 1617.624 1617.624 1617.624
## 253 254 255 256 257 258 259 260
## 1484.764 1484.764 1484.764 1484.764 1490.896 1490.896 1490.896 1648.284
## 261 262 263 264 265 266 267 268
## 1503.160 1503.160 1503.160 1499.072 1499.072 1499.072 1580.832 1431.620
## 269 270 271 272 273 274 275 276
## 1431.620 1431.620 1644.196 1644.196 1603.316 1458.192 1580.832 1452.060
## 277 278 279 280 367 368 369 370
## 1452.060 1452.060 1460.236 1533.820 1701.428 1701.428 1701.428 1445.928
## 371 372 373 374 375 376 377 378
## 1445.928 1445.928 1574.700 1515.424 1515.424 1425.488 1523.600 1492.940cor(Brboeing$PriceEconomy,Brboeing$PriceRelative)## [1] 0.09575739fit<-lm(PriceEconomy~PercentPremiumSeats,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1464.06 -367.54 72.62 308.85 1553.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1592.473 196.883 8.088 4.32e-13 ***
## PercentPremiumSeats -4.128 10.063 -0.410 0.682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 659.8 on 126 degrees of freedom
## Multiple R-squared: 0.001334, Adjusted R-squared: -0.006592
## F-statistic: 0.1683 on 1 and 126 DF, p-value: 0.6823Brboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 137
## [71] 109 77 77 69 65 1651 1651 2775 2230 2230 2230 2356 2356 2356
## [85] 2356 1562 1562 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609
## [99] 1609 1632 1632 1632 1140 1736 1736 1736 846 846 937 1485 891 1323
## [113] 1023 1023 757 533 3102 3102 3102 2166 2166 2166 649 575 575 797
## [127] 524 582fitted(fit)## 1 2 3 4 5 6 7 8
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 9 10 11 12 13 14 15 16
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 17 18 19 20 21 22 23 24
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 25 26 27 28 29 30 31 32
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 33 34 35 36 37 38 39 40
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 41 42 43 44 45 46 47 48
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 49 50 51 52 53 54 55 56
## 1490.542 1490.542 1490.542 1495.496 1495.496 1495.496 1495.496 1495.496
## 57 58 59 60 61 82 83 84
## 1495.496 1495.496 1495.496 1495.496 1495.496 1515.147 1515.147 1515.147
## 85 86 87 88 89 138 144 147
## 1515.147 1515.147 1515.147 1515.147 1515.147 1529.060 1529.060 1529.060
## 148 149 151 240 241 242 243 244
## 1529.060 1529.060 1529.060 1539.216 1539.216 1539.216 1539.216 1539.216
## 245 246 247 248 249 250 251 252
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 253 254 255 256 257 258 259 260
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 261 262 263 264 265 266 267 268
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 269 270 271 272 273 274 275 276
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 277 278 279 280 367 368 369 370
## 1539.216 1539.216 1539.216 1539.216 1548.835 1548.835 1548.835 1548.835
## 371 372 373 374 375 376 377 378
## 1548.835 1548.835 1548.835 1548.835 1548.835 1548.835 1548.835 1548.835cor(Brboeing$PriceEconomy,Brboeing$PercentPremiumSeats)## [1] -0.03652258fit<-lm(PricePremium~FlightDuration,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2178.0 -803.3 196.7 489.7 4276.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 751.97 285.45 2.634 0.00949 **
## FlightDuration 172.47 30.76 5.608 1.24e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1080 on 126 degrees of freedom
## Multiple R-squared: 0.1997, Adjusted R-squared: 0.1934
## F-statistic: 31.45 on 1 and 126 DF, p-value: 1.236e-07Brboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 172
## [71] 141 99 99 97 86 3509 3509 3509 3227 3227 3227 3200 3200 3200
## [85] 3200 3099 3099 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292
## [99] 2292 2278 2278 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550
## [113] 1199 1199 912 837 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [127] 797 797fitted(fit)## 1 2 3 4 5 6 7
## 2864.7507 2864.7507 2864.7507 2864.7507 2159.3394 2159.3394 2159.3394
## 8 9 10 11 12 13 14
## 1873.0355 1873.0355 2735.3966 2735.3966 2735.3966 2735.3966 2749.1944
## 15 16 17 18 19 20 21
## 2749.1944 2749.1944 2331.8116 2331.8116 2331.8116 1916.1535 1916.1535
## 22 23 24 25 26 27 28
## 1916.1535 1900.6310 1900.6310 1900.6310 2261.0980 2261.0980 2261.0980
## 29 30 31 32 33 34 35
## 1598.8046 1598.8046 1598.8046 1412.5346 1412.5346 1412.5346 1412.5346
## 36 37 38 39 40 41 42
## 3080.3410 3080.3410 3080.3410 1412.5346 1412.5346 1412.5346 1412.5346
## 43 44 45 46 47 48 49
## 1685.0407 1685.0407 1685.0407 2174.8619 2174.8619 2174.8619 2950.9869
## 50 51 52 53 54 55 56
## 2950.9869 2950.9869 1873.0355 2662.9582 2662.9582 2662.9582 1800.5971
## 57 58 59 60 61 82 83
## 1800.5971 1800.5971 2907.8688 2830.2563 2907.8688 1929.9513 1929.9513
## 84 85 86 87 88 89 138
## 1929.9513 1929.9513 2059.3055 2059.3055 2059.3055 2059.3055 967.5563
## 144 147 148 149 151 240 241
## 967.5563 981.3541 981.3541 967.5563 981.3541 2547.4019 2547.4019
## 242 243 244 245 246 247 248
## 2547.4019 2649.1605 2649.1605 2649.1605 2461.1657 2461.1657 2461.1657
## 249 250 251 252 253 254 255
## 2461.1657 2231.7777 2231.7777 2231.7777 2719.8741 2719.8741 2719.8741
## 256 257 258 259 260 261 262
## 2719.8741 2361.1319 2361.1319 2361.1319 2288.6935 2245.5755 2245.5755
## 263 264 265 266 267 268 269
## 2245.5755 2002.3896 2002.3896 2002.3896 2288.6935 1973.0693 1973.0693
## 270 271 272 273 274 275 276
## 1973.0693 2719.8741 2719.8741 2719.8741 2719.8741 2288.6935 2662.9582
## 277 278 279 280 367 368 369
## 2662.9582 2662.9582 1973.0693 2662.9582 3137.2569 3137.2569 3137.2569
## 370 371 372 373 374 375 376
## 3051.0208 3051.0208 3051.0208 2288.6935 2288.6935 2288.6935 2404.2499
## 377 378
## 2404.2499 2404.2499cor(Brboeing$PricePremium,Brboeing$FlightDuration)## [1] 0.4469114fit<-lm(PriceEconomy~SeatsEconomy,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1310.60 -462.54 50.37 366.48 1608.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1733.7470 182.9411 9.477 <2e-16 ***
## SeatsEconomy -1.1820 0.9389 -1.259 0.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 656.1 on 126 degrees of freedom
## Multiple R-squared: 0.01242, Adjusted R-squared: 0.004583
## F-statistic: 1.585 on 1 and 126 DF, p-value: 0.2104Brboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 172
## [71] 141 99 99 97 86 3509 3509 3509 3227 3227 3227 3200 3200 3200
## [85] 3200 3099 3099 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292
## [99] 2292 2278 2278 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550
## [113] 1199 1199 912 837 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [127] 797 797fitted(fit)## 1 2 3 4 5 6 7 8
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 9 10 11 12 13 14 15 16
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 17 18 19 20 21 22 23 24
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 25 26 27 28 29 30 31 32
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 33 34 35 36 37 38 39 40
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 41 42 43 44 45 46 47 48
## 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544 1589.544
## 49 50 51 52 53 54 55 56
## 1589.544 1589.544 1589.544 1583.634 1583.634 1583.634 1583.634 1583.634
## 57 58 59 60 61 82 83 84
## 1583.634 1583.634 1583.634 1583.634 1583.634 1446.523 1446.523 1446.523
## 85 86 87 88 89 138 144 147
## 1446.523 1446.523 1446.523 1446.523 1446.523 1375.604 1375.604 1375.604
## 148 149 151 240 241 242 243 244
## 1375.604 1375.604 1375.604 1446.523 1446.523 1446.523 1446.523 1446.523
## 245 246 247 248 249 250 251 252
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 253 254 255 256 257 258 259 260
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 261 262 263 264 265 266 267 268
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 269 270 271 272 273 274 275 276
## 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523 1446.523
## 277 278 279 280 367 368 369 370
## 1446.523 1446.523 1446.523 1446.523 1493.803 1493.803 1493.803 1493.803
## 371 372 373 374 375 376 377 378
## 1493.803 1493.803 1493.803 1493.803 1493.803 1493.803 1493.803 1493.803cor(Brboeing$PricePremium,Brboeing$SeatsEconomy)## [1] -0.03416268fit<-lm(PriceEconomy~SeatsPremium,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1331.0 -365.9 70.6 321.2 1247.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2404.163 302.234 7.955 8.88e-13 ***
## SeatsPremium -22.882 7.644 -2.993 0.00332 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 637.9 on 126 degrees of freedom
## Multiple R-squared: 0.0664, Adjusted R-squared: 0.05899
## F-statistic: 8.961 on 1 and 126 DF, p-value: 0.003321Brboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 172
## [71] 141 99 99 97 86 3509 3509 3509 3227 3227 3227 3200 3200 3200
## [85] 3200 3099 3099 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292
## [99] 2292 2278 2278 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550
## [113] 1199 1199 912 837 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [127] 797 797fitted(fit)## 1 2 3 4 5 6 7 8
## 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870
## 9 10 11 12 13 14 15 16
## 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870
## 17 18 19 20 21 22 23 24
## 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870
## 25 26 27 28 29 30 31 32
## 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870
## 33 34 35 36 37 38 39 40
## 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870
## 41 42 43 44 45 46 47 48
## 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870 1488.870
## 49 50 51 52 53 54 55 56
## 1488.870 1488.870 1488.870 1511.753 1511.753 1511.753 1511.753 1511.753
## 57 58 59 60 61 82 83 84
## 1511.753 1511.753 1511.753 1511.753 1511.753 1122.753 1122.753 1122.753
## 85 86 87 88 89 138 144 147
## 1122.753 1122.753 1122.753 1122.753 1122.753 1145.636 1145.636 1145.636
## 148 149 151 240 241 242 243 244
## 1145.636 1145.636 1145.636 1580.400 1580.400 1580.400 1580.400 1580.400
## 245 246 247 248 249 250 251 252
## 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400
## 253 254 255 256 257 258 259 260
## 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400
## 261 262 263 264 265 266 267 268
## 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400
## 269 270 271 272 273 274 275 276
## 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400 1580.400
## 277 278 279 280 367 368 369 370
## 1580.400 1580.400 1580.400 1580.400 1854.987 1854.987 1854.987 1854.987
## 371 372 373 374 375 376 377 378
## 1854.987 1854.987 1854.987 1854.987 1854.987 1854.987 1854.987 1854.987cor(Brboeing$PricePremium,Brboeing$SeatsPremium)## [1] -0.2492273fit<-lm(PriceEconomy~PriceRelative,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1430.07 -325.06 81.24 322.10 1400.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1417.3 107.8 13.15 <2e-16 ***
## PriceRelative 204.4 189.3 1.08 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 657.2 on 126 degrees of freedom
## Multiple R-squared: 0.009169, Adjusted R-squared: 0.001306
## F-statistic: 1.166 on 1 and 126 DF, p-value: 0.2823Brboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 172
## [71] 141 99 99 97 86 3509 3509 3509 3227 3227 3227 3200 3200 3200
## [85] 3200 3099 3099 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292
## [99] 2292 2278 2278 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550
## [113] 1199 1199 912 837 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [127] 797 797fitted(fit)## 1 2 3 4 5 6 7 8
## 1494.984 1494.984 1494.984 1494.984 1554.260 1554.260 1554.260 1627.844
## 9 10 11 12 13 14 15 16
## 1627.844 1570.612 1570.612 1531.776 1470.456 1523.600 1523.600 1523.600
## 17 18 19 20 21 22 23 24
## 1494.984 1494.984 1494.984 1486.808 1486.808 1486.808 1484.764 1484.764
## 25 26 27 28 29 30 31 32
## 1484.764 1488.852 1484.764 1484.764 1486.808 1486.808 1486.808 1503.160
## 33 34 35 36 37 38 39 40
## 1503.160 1503.160 1503.160 1550.172 1550.172 1550.172 1466.368 1466.368
## 41 42 43 44 45 46 47 48
## 1466.368 1466.368 1452.060 1452.060 1452.060 1433.664 1433.664 1433.664
## 49 50 51 52 53 54 55 56
## 1523.600 1523.600 1523.600 1627.844 1490.896 1490.896 1490.896 1486.808
## 57 58 59 60 61 82 83 84
## 1486.808 1486.808 1460.236 1460.236 1541.996 1636.020 1636.020 1636.020
## 85 86 87 88 89 138 144 147
## 1636.020 1499.072 1499.072 1499.072 1499.072 1470.456 1478.632 1476.588
## 148 149 151 240 241 242 243 244
## 1476.588 1499.072 1484.764 1648.284 1648.284 1470.456 1509.292 1509.292
## 245 246 247 248 249 250 251 252
## 1509.292 1490.896 1490.896 1490.896 1490.896 1617.624 1617.624 1617.624
## 253 254 255 256 257 258 259 260
## 1484.764 1484.764 1484.764 1484.764 1490.896 1490.896 1490.896 1648.284
## 261 262 263 264 265 266 267 268
## 1503.160 1503.160 1503.160 1499.072 1499.072 1499.072 1580.832 1431.620
## 269 270 271 272 273 274 275 276
## 1431.620 1431.620 1644.196 1644.196 1603.316 1458.192 1580.832 1452.060
## 277 278 279 280 367 368 369 370
## 1452.060 1452.060 1460.236 1533.820 1701.428 1701.428 1701.428 1445.928
## 371 372 373 374 375 376 377 378
## 1445.928 1445.928 1574.700 1515.424 1515.424 1425.488 1523.600 1492.940cor(Brboeing$PricePremium,Brboeing$PriceRelative)## [1] 0.5213261fit<-lm(PriceEconomy~PercentPremiumSeats,data = Brboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Brboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1464.06 -367.54 72.62 308.85 1553.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1592.473 196.883 8.088 4.32e-13 ***
## PercentPremiumSeats -4.128 10.063 -0.410 0.682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 659.8 on 126 degrees of freedom
## Multiple R-squared: 0.001334, Adjusted R-squared: -0.006592
## F-statistic: 0.1683 on 1 and 126 DF, p-value: 0.6823Brboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 172
## [71] 141 99 99 97 86 3509 3509 3509 3227 3227 3227 3200 3200 3200
## [85] 3200 3099 3099 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292
## [99] 2292 2278 2278 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550
## [113] 1199 1199 912 837 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [127] 797 797fitted(fit)## 1 2 3 4 5 6 7 8
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 9 10 11 12 13 14 15 16
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 17 18 19 20 21 22 23 24
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 25 26 27 28 29 30 31 32
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 33 34 35 36 37 38 39 40
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 41 42 43 44 45 46 47 48
## 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542 1490.542
## 49 50 51 52 53 54 55 56
## 1490.542 1490.542 1490.542 1495.496 1495.496 1495.496 1495.496 1495.496
## 57 58 59 60 61 82 83 84
## 1495.496 1495.496 1495.496 1495.496 1495.496 1515.147 1515.147 1515.147
## 85 86 87 88 89 138 144 147
## 1515.147 1515.147 1515.147 1515.147 1515.147 1529.060 1529.060 1529.060
## 148 149 151 240 241 242 243 244
## 1529.060 1529.060 1529.060 1539.216 1539.216 1539.216 1539.216 1539.216
## 245 246 247 248 249 250 251 252
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 253 254 255 256 257 258 259 260
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 261 262 263 264 265 266 267 268
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 269 270 271 272 273 274 275 276
## 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216 1539.216
## 277 278 279 280 367 368 369 370
## 1539.216 1539.216 1539.216 1539.216 1548.835 1548.835 1548.835 1548.835
## 371 372 373 374 375 376 377 378
## 1548.835 1548.835 1548.835 1548.835 1548.835 1548.835 1548.835 1548.835cor(Brboeing$PricePremium,Brboeing$PercentPremiumSeats)## [1] -0.1123491Brairbus <-British[ which(British$Aircraft=='AirBus'),]
View(Brairbus)
summary(Brairbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:47 Min. : 1.250 Aug:13
## British :47 Boeing: 0 1st Qu.: 2.410 Jul: 6
## Delta : 0 Median : 3.580 Oct:14
## Jet : 0 Mean : 5.426 Sep:14
## Singapore: 0 3rd Qu.:10.500
## Virgin : 0 Max. :13.080
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :303.0 Min. :55 Min. :31
## International:47 1st Qu.:303.0 1st Qu.:55 1st Qu.:31
## Median :303.0 Median :55 Median :31
## Mean :303.2 Mean :55 Mean :31
## 3rd Qu.:303.0 3rd Qu.:55 3rd Qu.:31
## Max. :312.0 Max. :55 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :18 Min. :19 Min. : 74.0 Min. : 97
## 1st Qu.:38 1st Qu.:18 1st Qu.:19 1st Qu.: 176.5 1st Qu.: 206
## Median :38 Median :18 Median :19 Median : 297.0 Median : 319
## Mean :38 Mean :18 Mean :19 Mean : 689.3 Mean :1056
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:19 3rd Qu.: 958.5 3rd Qu.:1634
## Max. :38 Max. :18 Max. :19 Max. :2384.0 Max. :3563
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0400 Min. :358.0 Min. :7 Min. :1
## 1st Qu.:0.1200 1st Qu.:358.0 1st Qu.:7 1st Qu.:1
## Median :0.1900 Median :358.0 Median :7 Median :1
## Mean :0.3232 Mean :358.2 Mean :7 Mean :1
## 3rd Qu.:0.4700 3rd Qu.:358.0 3rd Qu.:7 3rd Qu.:1
## Max. :1.2700 Max. :367.0 Max. :7 Max. :1
## PercentPremiumSeats
## Min. :14.99
## 1st Qu.:15.36
## Median :15.36
## Mean :15.35
## 3rd Qu.:15.36
## Max. :15.36mean(Brairbus$PriceEconomy)## [1] 689.2979mean(Brairbus$PricePremium)## [1] 1055.851library(plotly)
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Brairbus$PriceEconomy,Brairbus$TravelMonth,mean))
y4<-c(by(Brairbus$PricePremium,Brairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -961.8 -209.0 39.1 141.7 703.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -248.79 85.78 -2.90 0.00575 **
## FlightDuration 172.90 12.73 13.58 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 348.6 on 45 degrees of freedom
## Multiple R-squared: 0.8038, Adjusted R-squared: 0.7995
## F-statistic: 184.4 on 1 and 45 DF, p-value: < 2.2e-16Brairbus$PriceEconomy## [1] 2384 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198
## [15] 457 402 402 392 356 356 322 297 303 303 276 249 238 238
## [29] 228 231 203 201 207 207 182 171 168 140 147 138 126 126
## [43] 109 109 104 97 74fitted(fit)## 99 100 101 102 103 104
## 1680.80371 1680.80371 1680.80371 1680.80371 1566.68783 1566.68783
## 105 106 107 108 109 110
## 1566.68783 1566.68783 2012.77717 2012.77717 2012.77717 1680.80371
## 111 112 113 114 115 116
## 1680.80371 1680.80371 456.65158 370.20016 370.20016 167.90383
## 117 118 119 120 121 122
## 313.14222 313.14222 370.20016 211.12954 167.90383 167.90383
## 123 124 125 126 127 128
## 167.90383 313.14222 67.62018 370.20016 529.27077 167.90383
## 129 130 131 132 133 134
## 529.27077 67.62018 167.90383 370.20016 456.65158 240.52302
## 135 136 137 139 140 141
## 529.27077 -32.66347 456.65158 167.90383 67.62018 67.62018
## 142 143 145 146 150
## -32.66347 -32.66347 370.20016 240.52302 167.90383cor(Brairbus$PriceEconomy,Brairbus$FlightDuration)## [1] 0.8965569fit<-lm(PriceEconomy~SeatsEconomy,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -605.7 -511.2 -399.7 255.8 1681.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21868.03 26614.19 0.822 0.416
## SeatsEconomy -69.85 87.78 -0.796 0.430
##
## Residual standard error: 781.6 on 45 degrees of freedom
## Multiple R-squared: 0.01388, Adjusted R-squared: -0.008037
## F-statistic: 0.6333 on 1 and 45 DF, p-value: 0.4303Brairbus$PriceEconomy## [1] 2384 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198
## [15] 457 402 402 392 356 356 322 297 303 303 276 249 238 238
## [29] 228 231 203 201 207 207 182 171 168 140 147 138 126 126
## [43] 109 109 104 97 74fitted(fit)## 99 100 101 102 103 104 105 106
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 107 108 109 110 111 112 113 114
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 115 116 117 118 119 120 121 122
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 123 124 125 126 127 128 129 130
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 131 132 133 134 135 136 137 139
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 140 141 142 143 145 146 150
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 74.0000cor(Brairbus$PriceEconomy,Brairbus$SeatsEconomy)## [1] -0.1178013fit<-lm(PriceEconomy~PriceRelative,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1395.36 -328.30 -92.42 219.66 1443.64
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 202.9 133.5 1.519 0.136
## PriceRelative 1505.1 299.7 5.022 8.55e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 630 on 45 degrees of freedom
## Multiple R-squared: 0.3592, Adjusted R-squared: 0.3449
## F-statistic: 25.22 on 1 and 45 DF, p-value: 8.547e-06Brairbus$PriceEconomy## [1] 2384 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198
## [15] 457 402 402 392 356 356 322 297 303 303 276 249 238 238
## [29] 228 231 203 201 207 207 182 171 168 140 147 138 126 126
## [43] 109 109 104 97 74fitted(fit)## 99 100 101 102 103 104 105
## 940.3645 940.3645 940.3645 940.3645 1572.5146 1572.5146 1572.5146
## 106 107 108 109 110 111 112
## 1572.5146 910.2622 910.2622 910.2622 2114.3574 2114.3574 744.6991
## 113 114 115 116 117 118 119
## 293.1633 353.3681 353.3681 263.0610 368.4193 368.4193 323.2657
## 120 121 122 123 124 125 126
## 338.3169 278.1121 278.1121 368.4193 413.5729 458.7264 443.6752
## 127 128 129 130 131 132 133
## 428.6241 308.2145 458.7264 473.7776 413.5729 398.5217 443.6752
## 134 135 136 137 139 140 141
## 473.7776 473.7776 579.1360 503.8800 488.8288 549.0336 549.0336
## 142 143 145 146 150
## 654.3919 654.3919 579.1360 639.3407 669.4431cor(Brairbus$PriceEconomy,Brairbus$PriceRelative)## [1] 0.5993052fit<-lm(PriceEconomy~PercentPremiumSeats,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -605.7 -511.2 -399.7 255.8 1681.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -25396 32780 -0.775 0.443
## PercentPremiumSeats 1699 2135 0.796 0.430
##
## Residual standard error: 781.6 on 45 degrees of freedom
## Multiple R-squared: 0.01388, Adjusted R-squared: -0.008037
## F-statistic: 0.6333 on 1 and 45 DF, p-value: 0.4303Brairbus$PriceEconomy## [1] 2384 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198
## [15] 457 402 402 392 356 356 322 297 303 303 276 249 238 238
## [29] 228 231 203 201 207 207 182 171 168 140 147 138 126 126
## [43] 109 109 104 97 74fitted(fit)## 99 100 101 102 103 104 105 106
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 107 108 109 110 111 112 113 114
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 115 116 117 118 119 120 121 122
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 123 124 125 126 127 128 129 130
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 131 132 133 134 135 136 137 139
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739
## 140 141 142 143 145 146 150
## 702.6739 702.6739 702.6739 702.6739 702.6739 702.6739 74.0000fit<-lm(PricePremium~FlightDuration,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1109.46 -330.22 -19.61 259.79 986.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -540.84 138.28 -3.911 0.000307 ***
## FlightDuration 294.29 20.53 14.337 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 562 on 45 degrees of freedom
## Multiple R-squared: 0.8204, Adjusted R-squared: 0.8164
## F-statistic: 205.5 on 1 and 45 DF, p-value: < 2.2e-16Brairbus$PricePremium## [1] 3563 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634
## [15] 486 442 442 407 396 396 348 323 319 319 306 285 278 276
## [29] 263 247 238 237 237 234 211 201 198 175 175 165 156 156
## [43] 141 141 131 125 97fitted(fit)## 99 100 101 102 103 104
## 2743.463322 2743.463322 2743.463322 2743.463322 2549.230121 2549.230121
## 105 106 107 108 109 110
## 2549.230121 2549.230121 3308.505360 3308.505360 3308.505360 2743.463322
## 111 112 113 114 115 116
## 2743.463322 2743.463322 659.870806 512.724442 512.724442 168.401949
## 117 118 119 120 121 122
## 415.607841 415.607841 512.724442 241.975132 168.401949 168.401949
## 123 124 125 126 127 128
## 168.401949 415.607841 -2.287833 512.724442 783.473752 168.401949
## 129 130 131 132 133 134
## 783.473752 -2.287833 168.401949 512.724442 659.870806 292.004895
## 135 136 137 139 140 141
## 783.473752 -172.977615 659.870806 168.401949 -2.287833 -2.287833
## 142 143 145 146 150
## -172.977615 -172.977615 512.724442 292.004895 168.401949cor(Brairbus$PricePremium,Brairbus$FlightDuration)## [1] 0.905756fit<-lm(PricePremium~SeatsEconomy,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -951.7 -854.2 -757.7 557.3 2486.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34059.8 44884.7 0.759 0.452
## SeatsEconomy -108.9 148.0 -0.735 0.466
##
## Residual standard error: 1318 on 45 degrees of freedom
## Multiple R-squared: 0.01187, Adjusted R-squared: -0.01009
## F-statistic: 0.5407 on 1 and 45 DF, p-value: 0.466Brairbus$PricePremium## [1] 3563 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634
## [15] 486 442 442 407 396 396 348 323 319 319 306 285 278 276
## [29] 263 247 238 237 237 234 211 201 198 175 175 165 156 156
## [43] 141 141 131 125 97fitted(fit)## 99 100 101 102 103 104 105 106
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 107 108 109 110 111 112 113 114
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 115 116 117 118 119 120 121 122
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 123 124 125 126 127 128 129 130
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 131 132 133 134 135 136 137 139
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 140 141 142 143 145 146 150
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 97.000cor(Brairbus$PricePremium,Brairbus$SeatsEconomy)## [1] -0.1089611fit<-lm(PricePremium~SeatsPremium,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -958.9 -849.9 -736.9 578.1 2507.2
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1055.9 191.3 5.519 1.51e-06 ***
## SeatsPremium NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1312 on 46 degrees of freedomBrairbus$PricePremium## [1] 3563 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634
## [15] 486 442 442 407 396 396 348 323 319 319 306 285 278 276
## [29] 263 247 238 237 237 234 211 201 198 175 175 165 156 156
## [43] 141 141 131 125 97fitted(fit)## 99 100 101 102 103 104 105 106
## 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851
## 107 108 109 110 111 112 113 114
## 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851
## 115 116 117 118 119 120 121 122
## 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851
## 123 124 125 126 127 128 129 130
## 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851
## 131 132 133 134 135 136 137 139
## 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851
## 140 141 142 143 145 146 150
## 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851 1055.851cor(Brairbus$PricePremium,Brairbus$SeatsPremium)## Warning in cor(Brairbus$PricePremium, Brairbus$SeatsPremium): the standard
## deviation is zero## [1] NAfit<-lm(PricePremium~PriceRelative,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2283.8 -456.6 -105.4 346.3 2002.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 78.93 196.64 0.401 0.69
## PriceRelative 3022.74 441.37 6.849 1.71e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 927.9 on 45 degrees of freedom
## Multiple R-squared: 0.5104, Adjusted R-squared: 0.4995
## F-statistic: 46.9 on 1 and 45 DF, p-value: 1.71e-08Brairbus$PricePremium## [1] 3563 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634
## [15] 486 442 442 407 396 396 348 323 319 319 306 285 278 276
## [29] 263 247 238 237 237 234 211 201 198 175 175 165 156 156
## [43] 141 141 131 125 97fitted(fit)## 99 100 101 102 103 104 105
## 1560.0706 1560.0706 1560.0706 1560.0706 2829.6234 2829.6234 2829.6234
## 106 107 108 109 110 111 112
## 2829.6234 1499.6157 1499.6157 1499.6157 3917.8115 3917.8115 1167.1138
## 113 114 115 116 117 118 119
## 260.2904 381.2002 381.2002 199.8355 411.4276 411.4276 320.7453
## 120 121 122 123 124 125 126
## 350.9727 230.0629 230.0629 411.4276 502.1100 592.7923 562.5649
## 127 128 129 130 131 132 133
## 532.3374 290.5178 592.7923 623.0197 502.1100 471.8825 562.5649
## 134 135 136 137 139 140 141
## 623.0197 623.0197 834.6119 683.4746 653.2472 774.1570 774.1570
## 142 143 145 146 150
## 985.7491 985.7491 834.6119 955.5217 1015.9766cor(Brairbus$PricePremium,Brairbus$PriceRelative)## [1] 0.7143888fit<-lm(PricePremium~PercentPremiumSeats,data = Brairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = Brairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -951.7 -854.2 -757.7 557.3 2486.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -39594 55283 -0.716 0.478
## PercentPremiumSeats 2648 3601 0.735 0.466
##
## Residual standard error: 1318 on 45 degrees of freedom
## Multiple R-squared: 0.01187, Adjusted R-squared: -0.01009
## F-statistic: 0.5407 on 1 and 45 DF, p-value: 0.466Brairbus$PricePremium## [1] 3563 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634
## [15] 486 442 442 407 396 396 348 323 319 319 306 285 278 276
## [29] 263 247 238 237 237 234 211 201 198 175 175 165 156 156
## [43] 141 141 131 125 97fitted(fit)## 99 100 101 102 103 104 105 106
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 107 108 109 110 111 112 113 114
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 115 116 117 118 119 120 121 122
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 123 124 125 126 127 128 129 130
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 131 132 133 134 135 136 137 139
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696
## 140 141 142 143 145 146 150
## 1076.696 1076.696 1076.696 1076.696 1076.696 1076.696 97.000cor(Brairbus$PricePremium,Brairbus$PercentPremiumSeats)## [1] 0.1089611Now We Should Analyse the international aircrafts of British Airlines
Brint <- British[ which(British$IsInternational=='International'),]
View(Brint)
summary(Brint)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 47 Min. : 1.250 Aug:52
## British :175 Boeing:128 1st Qu.: 4.290 Jul:16
## Delta : 0 Median : 8.580 Oct:53
## Jet : 0 Mean : 7.855 Sep:54
## Singapore: 0 3rd Qu.:11.120
## Virgin : 0 Max. :13.830
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :122.0 Min. :24.00 Min. :31
## International:175 1st Qu.:122.0 1st Qu.:36.00 1st Qu.:31
## Median :243.0 Median :40.00 Median :31
## Mean :216.6 Mean :43.18 Mean :31
## 3rd Qu.:303.0 3rd Qu.:55.00 3rd Qu.:31
## Max. :312.0 Max. :56.00 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :38 Min. :18 Min. :19 Min. : 65.0
## 1st Qu.:38 1st Qu.:18 1st Qu.:19 1st Qu.: 528.5
## Median :38 Median :18 Median :19 Median :1444.0
## Mean :38 Mean :18 Mean :19 Mean :1293.5
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:19 3rd Qu.:1813.0
## Max. :38 Max. :18 Max. :19 Max. :3102.0
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86.0 Min. :0.0400 Min. :162.0 Min. :7
## 1st Qu.: 807.5 1st Qu.:0.2100 1st Qu.:162.0 1st Qu.:7
## Median :2049.0 Median :0.3600 Median :279.0 Median :7
## Mean :1937.0 Mean :0.4375 Mean :259.8 Mean :7
## 3rd Qu.:2982.0 3rd Qu.:0.5200 3rd Qu.:358.0 3rd Qu.:7
## Max. :7414.0 Max. :1.3900 Max. :367.0 Max. :7
## WidthDifference PercentPremiumSeats
## Min. :1 Min. :10.57
## 1st Qu.:1 1st Qu.:12.90
## Median :1 Median :15.36
## Mean :1 Mean :17.79
## 3rd Qu.:1 3rd Qu.:24.69
## Max. :1 Max. :24.69mean(Brint$PriceEconomy)## [1] 1293.48mean(Brint$PricePremium)## [1] 1937.029library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Brint$PriceEconomy,Brint$TravelMonth,mean))
y2<-c(by(Brint$PricePremium,Brint$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1492.9 -302.3 143.0 485.0 1111.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 156.69 102.25 1.532 0.127
## FlightDuration 144.72 11.79 12.273 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 573 on 173 degrees of freedom
## Multiple R-squared: 0.4654, Adjusted R-squared: 0.4623
## F-statistic: 150.6 on 1 and 173 DF, p-value: < 2.2e-16Brint$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7
## 1929.5390 1929.5390 1929.5390 1929.5390 1337.6245 1337.6245 1337.6245
## 8 9 10 11 12 13 14
## 1097.3853 1097.3853 1820.9972 1820.9972 1820.9972 1820.9972 1832.5750
## 15 16 17 18 19 20 21
## 1832.5750 1832.5750 1482.3468 1482.3468 1482.3468 1133.5659 1133.5659
## 22 23 24 25 26 27 28
## 1133.5659 1120.5409 1120.5409 1120.5409 1423.0107 1423.0107 1423.0107
## 29 30 31 32 33 34 35
## 867.2767 867.2767 867.2767 710.9766 710.9766 710.9766 710.9766
## 36 37 38 39 40 41 42
## 2110.4420 2110.4420 2110.4420 710.9766 710.9766 710.9766 710.9766
## 43 44 45 46 47 48 49
## 939.6379 939.6379 939.6379 1350.6495 1350.6495 1350.6495 2001.9002
## 50 51 52 53 54 55 56
## 2001.9002 2001.9002 1097.3853 1760.2138 1760.2138 1760.2138 1036.6019
## 57 58 59 60 61 82 83
## 1036.6019 1036.6019 1965.7196 1900.5945 1965.7196 1145.1437 1145.1437
## 84 85 86 87 88 89 99
## 1145.1437 1145.1437 1253.6855 1253.6855 1253.6855 1253.6855 1771.7916
## 100 101 102 103 104 105 106
## 1771.7916 1771.7916 1771.7916 1676.2748 1676.2748 1676.2748 1676.2748
## 107 108 109 110 111 112 113
## 2049.6586 2049.6586 2049.6586 1771.7916 1771.7916 1771.7916 747.1571
## 114 115 116 117 118 119 120
## 674.7960 674.7960 505.4708 627.0376 627.0376 674.7960 541.6514
## 121 122 123 124 125 126 127
## 505.4708 505.4708 505.4708 627.0376 421.5318 674.7960 807.9405
## 128 129 130 131 132 133 134
## 505.4708 807.9405 421.5318 505.4708 674.7960 747.1571 566.2542
## 135 136 137 138 139 140 141
## 807.9405 337.5928 747.1571 337.5928 505.4708 421.5318 421.5318
## 142 143 144 145 146 147 148
## 337.5928 337.5928 337.5928 674.7960 566.2542 349.1706 349.1706
## 149 150 151 240 241 242 243
## 337.5928 505.4708 349.1706 1663.2498 1663.2498 1663.2498 1748.6360
## 244 245 246 247 248 249 250
## 1748.6360 1748.6360 1590.8886 1590.8886 1590.8886 1590.8886 1398.4079
## 251 252 253 254 255 256 257
## 1398.4079 1398.4079 1807.9722 1807.9722 1807.9722 1807.9722 1506.9496
## 258 259 260 261 262 263 264
## 1506.9496 1506.9496 1446.1662 1409.9857 1409.9857 1409.9857 1205.9271
## 265 266 267 268 269 270 271
## 1205.9271 1205.9271 1446.1662 1181.3243 1181.3243 1181.3243 1807.9722
## 272 273 274 275 276 277 278
## 1807.9722 1807.9722 1807.9722 1446.1662 1760.2138 1760.2138 1760.2138
## 279 280 367 368 369 370 371
## 1181.3243 1760.2138 2158.2004 2158.2004 2158.2004 2085.8392 2085.8392
## 372 373 374 375 376 377 378
## 2085.8392 1446.1662 1446.1662 1446.1662 1543.1302 1543.1302 1543.1302cor(Brint$PriceEconomy,Brint$FlightDuration)## [1] 0.6822227fit<-lm(PriceEconomy~SeatsEconomy,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1169.0 -639.2 -46.1 524.1 1753.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2174.0297 167.9032 12.948 < 2e-16 ***
## SeatsEconomy -4.0655 0.7331 -5.546 1.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 722.1 on 173 degrees of freedom
## Multiple R-squared: 0.1509, Adjusted R-squared: 0.146
## F-statistic: 30.75 on 1 and 173 DF, p-value: 1.079e-07Brint$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 8 9 10 11 12 13 14
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 15 16 17 18 19 20 21
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 22 23 24 25 26 27 28
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 29 30 31 32 33 34 35
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 36 37 38 39 40 41 42
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 43 44 45 46 47 48 49
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 50 51 52 53 54 55 56
## 1678.0337 1678.0337 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060
## 57 58 59 60 61 82 83
## 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060 1186.1033 1186.1033
## 84 85 86 87 88 89 99
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 942.1708
## 100 101 102 103 104 105 106
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 107 108 109 110 111 112 113
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 114 115 116 117 118 119 120
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 121 122 123 124 125 126 127
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 128 129 130 131 132 133 134
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 135 136 137 138 139 140 141
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 142 143 144 145 146 147 148
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 149 150 151 240 241 242 243
## 942.1708 905.5809 942.1708 1186.1033 1186.1033 1186.1033 1186.1033
## 244 245 246 247 248 249 250
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 251 252 253 254 255 256 257
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 258 259 260 261 262 263 264
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 265 266 267 268 269 270 271
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 272 273 274 275 276 277 278
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 279 280 367 368 369 370 371
## 1186.1033 1186.1033 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249
## 372 373 374 375 376 377 378
## 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249cor(Brint$PriceEconomy,Brint$SeatsEconomy)## [1] -0.3885088fit<-lm(PriceEconomy~PriceRelative,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1226.20 -709.99 85.17 481.68 1620.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 950.93 96.29 9.876 < 2e-16 ***
## PriceRelative 782.89 178.69 4.381 2.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 743.5 on 173 degrees of freedom
## Multiple R-squared: 0.09987, Adjusted R-squared: 0.09467
## F-statistic: 19.2 on 1 and 173 DF, p-value: 2.041e-05Brint$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7
## 1248.4304 1248.4304 1248.4304 1248.4304 1475.4679 1475.4679 1475.4679
## 8 9 10 11 12 13 14
## 1757.3075 1757.3075 1538.0989 1538.0989 1389.3502 1154.4839 1358.0347
## 15 16 17 18 19 20 21
## 1358.0347 1358.0347 1248.4304 1248.4304 1248.4304 1217.1149 1217.1149
## 22 23 24 25 26 27 28
## 1217.1149 1209.2860 1209.2860 1209.2860 1224.9438 1209.2860 1209.2860
## 29 30 31 32 33 34 35
## 1217.1149 1217.1149 1217.1149 1279.7459 1279.7459 1279.7459 1279.7459
## 36 37 38 39 40 41 42
## 1459.8101 1459.8101 1459.8101 1138.8261 1138.8261 1138.8261 1138.8261
## 43 44 45 46 47 48 49
## 1084.0240 1084.0240 1084.0240 1013.5641 1013.5641 1013.5641 1358.0347
## 50 51 52 53 54 55 56
## 1358.0347 1358.0347 1757.3075 1232.7726 1232.7726 1232.7726 1217.1149
## 57 58 59 60 61 82 83
## 1217.1149 1217.1149 1115.3395 1115.3395 1428.4946 1788.6230 1788.6230
## 84 85 86 87 88 89 99
## 1788.6230 1788.6230 1264.0882 1264.0882 1264.0882 1264.0882 1334.5481
## 100 101 102 103 104 105 106
## 1334.5481 1334.5481 1334.5481 1663.3609 1663.3609 1663.3609 1663.3609
## 107 108 109 110 111 112 113
## 1318.8903 1318.8903 1318.8903 1945.2005 1945.2005 1232.7726 997.9063
## 114 115 116 117 118 119 120
## 1029.2218 1029.2218 982.2486 1037.0507 1037.0507 1013.5641 1021.3929
## 121 122 123 124 125 126 127
## 990.0774 990.0774 1037.0507 1060.5373 1084.0240 1076.1951 1068.3662
## 128 129 130 131 132 133 134
## 1005.7352 1084.0240 1091.8528 1060.5373 1052.7085 1076.1951 1091.8528
## 135 136 137 138 139 140 141
## 1091.8528 1146.6550 1107.5106 1154.4839 1099.6817 1130.9972 1130.9972
## 142 143 144 145 146 147 148
## 1185.7994 1185.7994 1185.7994 1146.6550 1177.9705 1177.9705 1177.9705
## 149 150 151 240 241 242 243
## 1264.0882 1193.6283 1209.2860 1835.5962 1835.5962 1154.4839 1303.2325
## 244 245 246 247 248 249 250
## 1303.2325 1303.2325 1232.7726 1232.7726 1232.7726 1232.7726 1718.1631
## 251 252 253 254 255 256 257
## 1718.1631 1718.1631 1209.2860 1209.2860 1209.2860 1209.2860 1232.7726
## 258 259 260 261 262 263 264
## 1232.7726 1232.7726 1835.5962 1279.7459 1279.7459 1279.7459 1264.0882
## 265 266 267 268 269 270 271
## 1264.0882 1264.0882 1577.2433 1005.7352 1005.7352 1005.7352 1819.9385
## 272 273 274 275 276 277 278
## 1819.9385 1663.3609 1107.5106 1577.2433 1084.0240 1084.0240 1084.0240
## 279 280 367 368 369 370 371
## 1115.3395 1397.1791 2039.1471 2039.1471 2039.1471 1060.5373 1060.5373
## 372 373 374 375 376 377 378
## 1060.5373 1553.7566 1326.7192 1326.7192 982.2486 1358.0347 1240.6015cor(Brint$PriceEconomy,Brint$PriceRelative)## [1] 0.3160274fit<-lm(PriceEconomy~PercentPremiumSeats,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1188.4 -665.2 110.1 509.3 1927.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1000.23 210.95 4.741 4.41e-06 ***
## PercentPremiumSeats 16.48 11.39 1.448 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 779 on 173 degrees of freedom
## Multiple R-squared: 0.01197, Adjusted R-squared: 0.006258
## F-statistic: 2.096 on 1 and 173 DF, p-value: 0.1495Brint$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7 8
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 9 10 11 12 13 14 15 16
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 17 18 19 20 21 22 23 24
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 25 26 27 28 29 30 31 32
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 33 34 35 36 37 38 39 40
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 41 42 43 44 45 46 47 48
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 49 50 51 52 53 54 55 56
## 1407.202 1407.202 1407.202 1387.422 1387.422 1387.422 1387.422 1387.422
## 57 58 59 60 61 82 83 84
## 1387.422 1387.422 1387.422 1387.422 1387.422 1308.962 1308.962 1308.962
## 85 86 87 88 89 99 100 101
## 1308.962 1308.962 1308.962 1308.962 1308.962 1253.414 1253.414 1253.414
## 102 103 104 105 106 107 108 109
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 110 111 112 113 114 115 116 117
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 118 119 120 121 122 123 124 125
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 126 127 128 129 130 131 132 133
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 134 135 136 137 138 139 140 141
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 142 143 144 145 146 147 148 149
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 150 151 240 241 242 243 244 245
## 1247.315 1253.414 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 246 247 248 249 250 251 252 253
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 254 255 256 257 258 259 260 261
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 262 263 264 265 266 267 268 269
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 270 271 272 273 274 275 276 277
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 278 279 280 367 368 369 370 371
## 1212.865 1212.865 1212.865 1174.459 1174.459 1174.459 1174.459 1174.459
## 372 373 374 375 376 377 378
## 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459cor(Brint$PriceEconomy,Brint$PercentPremiumSeats)## [1] 0.1094026fit<-lm(PricePremium~FlightDuration,data = Brint)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2347.1 -602.7 116.3 678.4 4032.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.52 179.30 0.215 0.83
## FlightDuration 241.70 20.68 11.689 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1005 on 173 degrees of freedom
## Multiple R-squared: 0.4413, Adjusted R-squared: 0.4381
## F-statistic: 136.6 on 1 and 173 DF, p-value: < 2.2e-16Brint$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 2999.2858 2999.2858 2999.2858 2999.2858 2010.7525 2010.7525 2010.7525
## 8 9 10 11 12 13 14
## 1609.5385 1609.5385 2818.0144 2818.0144 2818.0144 2818.0144 2837.3500
## 15 16 17 18 19 20 21
## 2837.3500 2837.3500 2252.4477 2252.4477 2252.4477 1669.9623 1669.9623
## 22 23 24 25 26 27 28
## 1669.9623 1648.2097 1648.2097 1648.2097 2153.3527 2153.3527 2153.3527
## 29 30 31 32 33 34 35
## 1225.2432 1225.2432 1225.2432 964.2124 964.2124 964.2124 964.2124
## 36 37 38 39 40 41 42
## 3301.4048 3301.4048 3301.4048 964.2124 964.2124 964.2124 964.2124
## 43 44 45 46 47 48 49
## 1346.0907 1346.0907 1346.0907 2032.5051 2032.5051 2032.5051 3120.1334
## 50 51 52 53 54 55 56
## 3120.1334 3120.1334 1609.5385 2716.5025 2716.5025 2716.5025 1508.0265
## 57 58 59 60 61 82 83
## 1508.0265 1508.0265 3059.7096 2950.9468 3059.7096 1689.2979 1689.2979
## 84 85 86 87 88 89 99
## 1689.2979 1689.2979 1870.5693 1870.5693 1870.5693 1870.5693 2735.8381
## 100 101 102 103 104 105 106
## 2735.8381 2735.8381 2735.8381 2576.3192 2576.3192 2576.3192 2576.3192
## 107 108 109 110 111 112 113
## 3199.8928 3199.8928 3199.8928 2735.8381 2735.8381 2735.8381 1024.6362
## 114 115 116 117 118 119 120
## 903.7886 903.7886 621.0052 824.0291 824.0291 903.7886 681.4290
## 121 122 123 124 125 126 127
## 621.0052 621.0052 621.0052 824.0291 480.8220 903.7886 1126.1481
## 128 129 130 131 132 133 134
## 621.0052 1126.1481 480.8220 621.0052 903.7886 1024.6362 722.5172
## 135 136 137 138 139 140 141
## 1126.1481 340.6388 1024.6362 340.6388 621.0052 480.8220 480.8220
## 142 143 144 145 146 147 148
## 340.6388 340.6388 340.6388 903.7886 722.5172 359.9744 359.9744
## 149 150 151 240 241 242 243
## 340.6388 621.0052 359.9744 2554.5667 2554.5667 2554.5667 2697.1668
## 244 245 246 247 248 249 250
## 2697.1668 2697.1668 2433.7191 2433.7191 2433.7191 2433.7191 2112.2645
## 251 252 253 254 255 256 257
## 2112.2645 2112.2645 2796.2619 2796.2619 2796.2619 2796.2619 2293.5359
## 258 259 260 261 262 263 264
## 2293.5359 2293.5359 2192.0239 2131.6001 2131.6001 2131.6001 1790.8099
## 265 266 267 268 269 270 271
## 1790.8099 1790.8099 2192.0239 1749.7217 1749.7217 1749.7217 2796.2619
## 272 273 274 275 276 277 278
## 2796.2619 2796.2619 2796.2619 2192.0239 2716.5025 2716.5025 2716.5025
## 279 280 367 368 369 370 371
## 1749.7217 2716.5025 3381.1642 3381.1642 3381.1642 3260.3166 3260.3166
## 372 373 374 375 376 377 378
## 3260.3166 2192.0239 2192.0239 2192.0239 2353.9597 2353.9597 2353.9597cor(Brint$PricePremium,Brint$FlightDuration)## [1] 0.664292fit<-lm(PriceEconomy~SeatsEconomy,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1169.0 -639.2 -46.1 524.1 1753.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2174.0297 167.9032 12.948 < 2e-16 ***
## SeatsEconomy -4.0655 0.7331 -5.546 1.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 722.1 on 173 degrees of freedom
## Multiple R-squared: 0.1509, Adjusted R-squared: 0.146
## F-statistic: 30.75 on 1 and 173 DF, p-value: 1.079e-07Brint$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 8 9 10 11 12 13 14
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 15 16 17 18 19 20 21
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 22 23 24 25 26 27 28
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 29 30 31 32 33 34 35
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 36 37 38 39 40 41 42
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 43 44 45 46 47 48 49
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 50 51 52 53 54 55 56
## 1678.0337 1678.0337 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060
## 57 58 59 60 61 82 83
## 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060 1186.1033 1186.1033
## 84 85 86 87 88 89 99
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 942.1708
## 100 101 102 103 104 105 106
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 107 108 109 110 111 112 113
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 114 115 116 117 118 119 120
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 121 122 123 124 125 126 127
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 128 129 130 131 132 133 134
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 135 136 137 138 139 140 141
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 142 143 144 145 146 147 148
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 149 150 151 240 241 242 243
## 942.1708 905.5809 942.1708 1186.1033 1186.1033 1186.1033 1186.1033
## 244 245 246 247 248 249 250
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 251 252 253 254 255 256 257
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 258 259 260 261 262 263 264
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 265 266 267 268 269 270 271
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 272 273 274 275 276 277 278
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 279 280 367 368 369 370 371
## 1186.1033 1186.1033 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249
## 372 373 374 375 376 377 378
## 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249cor(Brint$PricePremium,Brint$SeatsEconomy)## [1] -0.3006343fit<-lm(PriceEconomy~SeatsPremium,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1513.48 -572.66 -10.06 396.07 1548.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2968.309 241.589 12.29 < 2e-16 ***
## SeatsPremium -38.785 5.463 -7.10 3.1e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 689.7 on 173 degrees of freedom
## Multiple R-squared: 0.2256, Adjusted R-squared: 0.2212
## F-statistic: 50.41 on 1 and 173 DF, p-value: 3.101e-11Brint$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 8 9 10 11 12 13 14
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 15 16 17 18 19 20 21
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 22 23 24 25 26 27 28
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 29 30 31 32 33 34 35
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 36 37 38 39 40 41 42
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 43 44 45 46 47 48 49
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 50 51 52 53 54 55 56
## 1416.9258 1416.9258 1455.7103 1455.7103 1455.7103 1455.7103 1455.7103
## 57 58 59 60 61 82 83
## 1455.7103 1455.7103 1455.7103 1455.7103 1455.7103 796.3725 796.3725
## 84 85 86 87 88 89 99
## 796.3725 796.3725 796.3725 796.3725 796.3725 796.3725 835.1571
## 100 101 102 103 104 105 106
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 107 108 109 110 111 112 113
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 114 115 116 117 118 119 120
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 121 122 123 124 125 126 127
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 128 129 130 131 132 133 134
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 135 136 137 138 139 140 141
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 142 143 144 145 146 147 148
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 149 150 151 240 241 242 243
## 835.1571 835.1571 835.1571 1572.0641 1572.0641 1572.0641 1572.0641
## 244 245 246 247 248 249 250
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 251 252 253 254 255 256 257
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 258 259 260 261 262 263 264
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 265 266 267 268 269 270 271
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 272 273 274 275 276 277 278
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 279 280 367 368 369 370 371
## 1572.0641 1572.0641 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790
## 372 373 374 375 376 377 378
## 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790cor(Brint$PricePremium,Brint$SeatsPremium)## [1] -0.4260349fit<-lm(PriceEconomy~PriceRelative,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1226.20 -709.99 85.17 481.68 1620.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 950.93 96.29 9.876 < 2e-16 ***
## PriceRelative 782.89 178.69 4.381 2.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 743.5 on 173 degrees of freedom
## Multiple R-squared: 0.09987, Adjusted R-squared: 0.09467
## F-statistic: 19.2 on 1 and 173 DF, p-value: 2.041e-05Brint$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 1248.4304 1248.4304 1248.4304 1248.4304 1475.4679 1475.4679 1475.4679
## 8 9 10 11 12 13 14
## 1757.3075 1757.3075 1538.0989 1538.0989 1389.3502 1154.4839 1358.0347
## 15 16 17 18 19 20 21
## 1358.0347 1358.0347 1248.4304 1248.4304 1248.4304 1217.1149 1217.1149
## 22 23 24 25 26 27 28
## 1217.1149 1209.2860 1209.2860 1209.2860 1224.9438 1209.2860 1209.2860
## 29 30 31 32 33 34 35
## 1217.1149 1217.1149 1217.1149 1279.7459 1279.7459 1279.7459 1279.7459
## 36 37 38 39 40 41 42
## 1459.8101 1459.8101 1459.8101 1138.8261 1138.8261 1138.8261 1138.8261
## 43 44 45 46 47 48 49
## 1084.0240 1084.0240 1084.0240 1013.5641 1013.5641 1013.5641 1358.0347
## 50 51 52 53 54 55 56
## 1358.0347 1358.0347 1757.3075 1232.7726 1232.7726 1232.7726 1217.1149
## 57 58 59 60 61 82 83
## 1217.1149 1217.1149 1115.3395 1115.3395 1428.4946 1788.6230 1788.6230
## 84 85 86 87 88 89 99
## 1788.6230 1788.6230 1264.0882 1264.0882 1264.0882 1264.0882 1334.5481
## 100 101 102 103 104 105 106
## 1334.5481 1334.5481 1334.5481 1663.3609 1663.3609 1663.3609 1663.3609
## 107 108 109 110 111 112 113
## 1318.8903 1318.8903 1318.8903 1945.2005 1945.2005 1232.7726 997.9063
## 114 115 116 117 118 119 120
## 1029.2218 1029.2218 982.2486 1037.0507 1037.0507 1013.5641 1021.3929
## 121 122 123 124 125 126 127
## 990.0774 990.0774 1037.0507 1060.5373 1084.0240 1076.1951 1068.3662
## 128 129 130 131 132 133 134
## 1005.7352 1084.0240 1091.8528 1060.5373 1052.7085 1076.1951 1091.8528
## 135 136 137 138 139 140 141
## 1091.8528 1146.6550 1107.5106 1154.4839 1099.6817 1130.9972 1130.9972
## 142 143 144 145 146 147 148
## 1185.7994 1185.7994 1185.7994 1146.6550 1177.9705 1177.9705 1177.9705
## 149 150 151 240 241 242 243
## 1264.0882 1193.6283 1209.2860 1835.5962 1835.5962 1154.4839 1303.2325
## 244 245 246 247 248 249 250
## 1303.2325 1303.2325 1232.7726 1232.7726 1232.7726 1232.7726 1718.1631
## 251 252 253 254 255 256 257
## 1718.1631 1718.1631 1209.2860 1209.2860 1209.2860 1209.2860 1232.7726
## 258 259 260 261 262 263 264
## 1232.7726 1232.7726 1835.5962 1279.7459 1279.7459 1279.7459 1264.0882
## 265 266 267 268 269 270 271
## 1264.0882 1264.0882 1577.2433 1005.7352 1005.7352 1005.7352 1819.9385
## 272 273 274 275 276 277 278
## 1819.9385 1663.3609 1107.5106 1577.2433 1084.0240 1084.0240 1084.0240
## 279 280 367 368 369 370 371
## 1115.3395 1397.1791 2039.1471 2039.1471 2039.1471 1060.5373 1060.5373
## 372 373 374 375 376 377 378
## 1060.5373 1553.7566 1326.7192 1326.7192 982.2486 1358.0347 1240.6015cor(Brint$PricePremium,Brint$PriceRelative)## [1] 0.602946fit<-lm(PriceEconomy~PercentPremiumSeats,data = Brint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Brint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1188.4 -665.2 110.1 509.3 1927.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1000.23 210.95 4.741 4.41e-06 ***
## PercentPremiumSeats 16.48 11.39 1.448 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 779 on 173 degrees of freedom
## Multiple R-squared: 0.01197, Adjusted R-squared: 0.006258
## F-statistic: 2.096 on 1 and 173 DF, p-value: 0.1495Brint$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7 8
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 9 10 11 12 13 14 15 16
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 17 18 19 20 21 22 23 24
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 25 26 27 28 29 30 31 32
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 33 34 35 36 37 38 39 40
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 41 42 43 44 45 46 47 48
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 49 50 51 52 53 54 55 56
## 1407.202 1407.202 1407.202 1387.422 1387.422 1387.422 1387.422 1387.422
## 57 58 59 60 61 82 83 84
## 1387.422 1387.422 1387.422 1387.422 1387.422 1308.962 1308.962 1308.962
## 85 86 87 88 89 99 100 101
## 1308.962 1308.962 1308.962 1308.962 1308.962 1253.414 1253.414 1253.414
## 102 103 104 105 106 107 108 109
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 110 111 112 113 114 115 116 117
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 118 119 120 121 122 123 124 125
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 126 127 128 129 130 131 132 133
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 134 135 136 137 138 139 140 141
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 142 143 144 145 146 147 148 149
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 150 151 240 241 242 243 244 245
## 1247.315 1253.414 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 246 247 248 249 250 251 252 253
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 254 255 256 257 258 259 260 261
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 262 263 264 265 266 267 268 269
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 270 271 272 273 274 275 276 277
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 278 279 280 367 368 369 370 371
## 1212.865 1212.865 1212.865 1174.459 1174.459 1174.459 1174.459 1174.459
## 372 373 374 375 376 377 378
## 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459cor(Brint$PricePremium,Brint$PercentPremiumSeats)## [1] 0.03194159Now It’s time for comparison-
par(mfrow=c(1, 2))
main="Boeing vs AirBus"
library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Brboeing$PriceEconomy,Brboeing$TravelMonth,mean))
y2<-c(by(Brboeing$PricePremium,Brboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Brairbus$PriceEconomy,Brairbus$TravelMonth,mean))
y4<-c(by(Brairbus$PricePremium,Brairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(main="mean prices of economy & premium tickets in Boeing",data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Boeing)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'plot_ly(main="mean prices of economy & premium tickets in Airbus"
,data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Airbus)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'short Analysis of British Airlines
mean(British$PriceEconomy)## [1] 1293.48mean(British$PricePremium)## [1] 1937.029library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(British$PriceEconomy,British$TravelMonth,mean))
y2<-c(by(British$PricePremium,British$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1492.9 -302.3 143.0 485.0 1111.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 156.69 102.25 1.532 0.127
## FlightDuration 144.72 11.79 12.273 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 573 on 173 degrees of freedom
## Multiple R-squared: 0.4654, Adjusted R-squared: 0.4623
## F-statistic: 150.6 on 1 and 173 DF, p-value: < 2.2e-16British$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7
## 1929.5390 1929.5390 1929.5390 1929.5390 1337.6245 1337.6245 1337.6245
## 8 9 10 11 12 13 14
## 1097.3853 1097.3853 1820.9972 1820.9972 1820.9972 1820.9972 1832.5750
## 15 16 17 18 19 20 21
## 1832.5750 1832.5750 1482.3468 1482.3468 1482.3468 1133.5659 1133.5659
## 22 23 24 25 26 27 28
## 1133.5659 1120.5409 1120.5409 1120.5409 1423.0107 1423.0107 1423.0107
## 29 30 31 32 33 34 35
## 867.2767 867.2767 867.2767 710.9766 710.9766 710.9766 710.9766
## 36 37 38 39 40 41 42
## 2110.4420 2110.4420 2110.4420 710.9766 710.9766 710.9766 710.9766
## 43 44 45 46 47 48 49
## 939.6379 939.6379 939.6379 1350.6495 1350.6495 1350.6495 2001.9002
## 50 51 52 53 54 55 56
## 2001.9002 2001.9002 1097.3853 1760.2138 1760.2138 1760.2138 1036.6019
## 57 58 59 60 61 82 83
## 1036.6019 1036.6019 1965.7196 1900.5945 1965.7196 1145.1437 1145.1437
## 84 85 86 87 88 89 99
## 1145.1437 1145.1437 1253.6855 1253.6855 1253.6855 1253.6855 1771.7916
## 100 101 102 103 104 105 106
## 1771.7916 1771.7916 1771.7916 1676.2748 1676.2748 1676.2748 1676.2748
## 107 108 109 110 111 112 113
## 2049.6586 2049.6586 2049.6586 1771.7916 1771.7916 1771.7916 747.1571
## 114 115 116 117 118 119 120
## 674.7960 674.7960 505.4708 627.0376 627.0376 674.7960 541.6514
## 121 122 123 124 125 126 127
## 505.4708 505.4708 505.4708 627.0376 421.5318 674.7960 807.9405
## 128 129 130 131 132 133 134
## 505.4708 807.9405 421.5318 505.4708 674.7960 747.1571 566.2542
## 135 136 137 138 139 140 141
## 807.9405 337.5928 747.1571 337.5928 505.4708 421.5318 421.5318
## 142 143 144 145 146 147 148
## 337.5928 337.5928 337.5928 674.7960 566.2542 349.1706 349.1706
## 149 150 151 240 241 242 243
## 337.5928 505.4708 349.1706 1663.2498 1663.2498 1663.2498 1748.6360
## 244 245 246 247 248 249 250
## 1748.6360 1748.6360 1590.8886 1590.8886 1590.8886 1590.8886 1398.4079
## 251 252 253 254 255 256 257
## 1398.4079 1398.4079 1807.9722 1807.9722 1807.9722 1807.9722 1506.9496
## 258 259 260 261 262 263 264
## 1506.9496 1506.9496 1446.1662 1409.9857 1409.9857 1409.9857 1205.9271
## 265 266 267 268 269 270 271
## 1205.9271 1205.9271 1446.1662 1181.3243 1181.3243 1181.3243 1807.9722
## 272 273 274 275 276 277 278
## 1807.9722 1807.9722 1807.9722 1446.1662 1760.2138 1760.2138 1760.2138
## 279 280 367 368 369 370 371
## 1181.3243 1760.2138 2158.2004 2158.2004 2158.2004 2085.8392 2085.8392
## 372 373 374 375 376 377 378
## 2085.8392 1446.1662 1446.1662 1446.1662 1543.1302 1543.1302 1543.1302cor(British$PriceEconomy,British$FlightDuration)## [1] 0.6822227fit<-lm(PriceEconomy~SeatsEconomy,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1169.0 -639.2 -46.1 524.1 1753.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2174.0297 167.9032 12.948 < 2e-16 ***
## SeatsEconomy -4.0655 0.7331 -5.546 1.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 722.1 on 173 degrees of freedom
## Multiple R-squared: 0.1509, Adjusted R-squared: 0.146
## F-statistic: 30.75 on 1 and 173 DF, p-value: 1.079e-07British$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 8 9 10 11 12 13 14
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 15 16 17 18 19 20 21
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 22 23 24 25 26 27 28
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 29 30 31 32 33 34 35
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 36 37 38 39 40 41 42
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 43 44 45 46 47 48 49
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 50 51 52 53 54 55 56
## 1678.0337 1678.0337 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060
## 57 58 59 60 61 82 83
## 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060 1186.1033 1186.1033
## 84 85 86 87 88 89 99
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 942.1708
## 100 101 102 103 104 105 106
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 107 108 109 110 111 112 113
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 114 115 116 117 118 119 120
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 121 122 123 124 125 126 127
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 128 129 130 131 132 133 134
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 135 136 137 138 139 140 141
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 142 143 144 145 146 147 148
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 149 150 151 240 241 242 243
## 942.1708 905.5809 942.1708 1186.1033 1186.1033 1186.1033 1186.1033
## 244 245 246 247 248 249 250
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 251 252 253 254 255 256 257
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 258 259 260 261 262 263 264
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 265 266 267 268 269 270 271
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 272 273 274 275 276 277 278
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 279 280 367 368 369 370 371
## 1186.1033 1186.1033 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249
## 372 373 374 375 376 377 378
## 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249cor(British$PriceEconomy,British$SeatsEconomy)## [1] -0.3885088fit<-lm(PriceEconomy~PriceRelative,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1226.20 -709.99 85.17 481.68 1620.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 950.93 96.29 9.876 < 2e-16 ***
## PriceRelative 782.89 178.69 4.381 2.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 743.5 on 173 degrees of freedom
## Multiple R-squared: 0.09987, Adjusted R-squared: 0.09467
## F-statistic: 19.2 on 1 and 173 DF, p-value: 2.041e-05British$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7
## 1248.4304 1248.4304 1248.4304 1248.4304 1475.4679 1475.4679 1475.4679
## 8 9 10 11 12 13 14
## 1757.3075 1757.3075 1538.0989 1538.0989 1389.3502 1154.4839 1358.0347
## 15 16 17 18 19 20 21
## 1358.0347 1358.0347 1248.4304 1248.4304 1248.4304 1217.1149 1217.1149
## 22 23 24 25 26 27 28
## 1217.1149 1209.2860 1209.2860 1209.2860 1224.9438 1209.2860 1209.2860
## 29 30 31 32 33 34 35
## 1217.1149 1217.1149 1217.1149 1279.7459 1279.7459 1279.7459 1279.7459
## 36 37 38 39 40 41 42
## 1459.8101 1459.8101 1459.8101 1138.8261 1138.8261 1138.8261 1138.8261
## 43 44 45 46 47 48 49
## 1084.0240 1084.0240 1084.0240 1013.5641 1013.5641 1013.5641 1358.0347
## 50 51 52 53 54 55 56
## 1358.0347 1358.0347 1757.3075 1232.7726 1232.7726 1232.7726 1217.1149
## 57 58 59 60 61 82 83
## 1217.1149 1217.1149 1115.3395 1115.3395 1428.4946 1788.6230 1788.6230
## 84 85 86 87 88 89 99
## 1788.6230 1788.6230 1264.0882 1264.0882 1264.0882 1264.0882 1334.5481
## 100 101 102 103 104 105 106
## 1334.5481 1334.5481 1334.5481 1663.3609 1663.3609 1663.3609 1663.3609
## 107 108 109 110 111 112 113
## 1318.8903 1318.8903 1318.8903 1945.2005 1945.2005 1232.7726 997.9063
## 114 115 116 117 118 119 120
## 1029.2218 1029.2218 982.2486 1037.0507 1037.0507 1013.5641 1021.3929
## 121 122 123 124 125 126 127
## 990.0774 990.0774 1037.0507 1060.5373 1084.0240 1076.1951 1068.3662
## 128 129 130 131 132 133 134
## 1005.7352 1084.0240 1091.8528 1060.5373 1052.7085 1076.1951 1091.8528
## 135 136 137 138 139 140 141
## 1091.8528 1146.6550 1107.5106 1154.4839 1099.6817 1130.9972 1130.9972
## 142 143 144 145 146 147 148
## 1185.7994 1185.7994 1185.7994 1146.6550 1177.9705 1177.9705 1177.9705
## 149 150 151 240 241 242 243
## 1264.0882 1193.6283 1209.2860 1835.5962 1835.5962 1154.4839 1303.2325
## 244 245 246 247 248 249 250
## 1303.2325 1303.2325 1232.7726 1232.7726 1232.7726 1232.7726 1718.1631
## 251 252 253 254 255 256 257
## 1718.1631 1718.1631 1209.2860 1209.2860 1209.2860 1209.2860 1232.7726
## 258 259 260 261 262 263 264
## 1232.7726 1232.7726 1835.5962 1279.7459 1279.7459 1279.7459 1264.0882
## 265 266 267 268 269 270 271
## 1264.0882 1264.0882 1577.2433 1005.7352 1005.7352 1005.7352 1819.9385
## 272 273 274 275 276 277 278
## 1819.9385 1663.3609 1107.5106 1577.2433 1084.0240 1084.0240 1084.0240
## 279 280 367 368 369 370 371
## 1115.3395 1397.1791 2039.1471 2039.1471 2039.1471 1060.5373 1060.5373
## 372 373 374 375 376 377 378
## 1060.5373 1553.7566 1326.7192 1326.7192 982.2486 1358.0347 1240.6015cor(British$PriceEconomy,British$PriceRelative)## [1] 0.3160274fit<-lm(PriceEconomy~PercentPremiumSeats,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1188.4 -665.2 110.1 509.3 1927.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1000.23 210.95 4.741 4.41e-06 ***
## PercentPremiumSeats 16.48 11.39 1.448 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 779 on 173 degrees of freedom
## Multiple R-squared: 0.01197, Adjusted R-squared: 0.006258
## F-statistic: 2.096 on 1 and 173 DF, p-value: 0.1495British$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 2384
## [71] 2384 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457
## [85] 402 402 392 356 356 322 297 303 303 276 249 238 238 228
## [99] 231 203 201 207 207 182 171 168 140 147 137 138 126 126
## [113] 109 109 109 104 97 77 77 69 74 65 1651 1651 2775 2230
## [127] 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281 2281 2281 1813
## [141] 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736 1736 1736 846
## [155] 846 937 1485 891 1323 1023 1023 757 533 3102 3102 3102 2166 2166
## [169] 2166 649 575 575 797 524 582fitted(fit)## 1 2 3 4 5 6 7 8
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 9 10 11 12 13 14 15 16
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 17 18 19 20 21 22 23 24
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 25 26 27 28 29 30 31 32
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 33 34 35 36 37 38 39 40
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 41 42 43 44 45 46 47 48
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 49 50 51 52 53 54 55 56
## 1407.202 1407.202 1407.202 1387.422 1387.422 1387.422 1387.422 1387.422
## 57 58 59 60 61 82 83 84
## 1387.422 1387.422 1387.422 1387.422 1387.422 1308.962 1308.962 1308.962
## 85 86 87 88 89 99 100 101
## 1308.962 1308.962 1308.962 1308.962 1308.962 1253.414 1253.414 1253.414
## 102 103 104 105 106 107 108 109
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 110 111 112 113 114 115 116 117
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 118 119 120 121 122 123 124 125
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 126 127 128 129 130 131 132 133
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 134 135 136 137 138 139 140 141
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 142 143 144 145 146 147 148 149
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 150 151 240 241 242 243 244 245
## 1247.315 1253.414 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 246 247 248 249 250 251 252 253
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 254 255 256 257 258 259 260 261
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 262 263 264 265 266 267 268 269
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 270 271 272 273 274 275 276 277
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 278 279 280 367 368 369 370 371
## 1212.865 1212.865 1212.865 1174.459 1174.459 1174.459 1174.459 1174.459
## 372 373 374 375 376 377 378
## 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459cor(British$PriceEconomy,British$PercentPremiumSeats)## [1] 0.1094026fit<-lm(PricePremium~FlightDuration,data = British)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2347.1 -602.7 116.3 678.4 4032.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.52 179.30 0.215 0.83
## FlightDuration 241.70 20.68 11.689 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1005 on 173 degrees of freedom
## Multiple R-squared: 0.4413, Adjusted R-squared: 0.4381
## F-statistic: 136.6 on 1 and 173 DF, p-value: < 2.2e-16British$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 2999.2858 2999.2858 2999.2858 2999.2858 2010.7525 2010.7525 2010.7525
## 8 9 10 11 12 13 14
## 1609.5385 1609.5385 2818.0144 2818.0144 2818.0144 2818.0144 2837.3500
## 15 16 17 18 19 20 21
## 2837.3500 2837.3500 2252.4477 2252.4477 2252.4477 1669.9623 1669.9623
## 22 23 24 25 26 27 28
## 1669.9623 1648.2097 1648.2097 1648.2097 2153.3527 2153.3527 2153.3527
## 29 30 31 32 33 34 35
## 1225.2432 1225.2432 1225.2432 964.2124 964.2124 964.2124 964.2124
## 36 37 38 39 40 41 42
## 3301.4048 3301.4048 3301.4048 964.2124 964.2124 964.2124 964.2124
## 43 44 45 46 47 48 49
## 1346.0907 1346.0907 1346.0907 2032.5051 2032.5051 2032.5051 3120.1334
## 50 51 52 53 54 55 56
## 3120.1334 3120.1334 1609.5385 2716.5025 2716.5025 2716.5025 1508.0265
## 57 58 59 60 61 82 83
## 1508.0265 1508.0265 3059.7096 2950.9468 3059.7096 1689.2979 1689.2979
## 84 85 86 87 88 89 99
## 1689.2979 1689.2979 1870.5693 1870.5693 1870.5693 1870.5693 2735.8381
## 100 101 102 103 104 105 106
## 2735.8381 2735.8381 2735.8381 2576.3192 2576.3192 2576.3192 2576.3192
## 107 108 109 110 111 112 113
## 3199.8928 3199.8928 3199.8928 2735.8381 2735.8381 2735.8381 1024.6362
## 114 115 116 117 118 119 120
## 903.7886 903.7886 621.0052 824.0291 824.0291 903.7886 681.4290
## 121 122 123 124 125 126 127
## 621.0052 621.0052 621.0052 824.0291 480.8220 903.7886 1126.1481
## 128 129 130 131 132 133 134
## 621.0052 1126.1481 480.8220 621.0052 903.7886 1024.6362 722.5172
## 135 136 137 138 139 140 141
## 1126.1481 340.6388 1024.6362 340.6388 621.0052 480.8220 480.8220
## 142 143 144 145 146 147 148
## 340.6388 340.6388 340.6388 903.7886 722.5172 359.9744 359.9744
## 149 150 151 240 241 242 243
## 340.6388 621.0052 359.9744 2554.5667 2554.5667 2554.5667 2697.1668
## 244 245 246 247 248 249 250
## 2697.1668 2697.1668 2433.7191 2433.7191 2433.7191 2433.7191 2112.2645
## 251 252 253 254 255 256 257
## 2112.2645 2112.2645 2796.2619 2796.2619 2796.2619 2796.2619 2293.5359
## 258 259 260 261 262 263 264
## 2293.5359 2293.5359 2192.0239 2131.6001 2131.6001 2131.6001 1790.8099
## 265 266 267 268 269 270 271
## 1790.8099 1790.8099 2192.0239 1749.7217 1749.7217 1749.7217 2796.2619
## 272 273 274 275 276 277 278
## 2796.2619 2796.2619 2796.2619 2192.0239 2716.5025 2716.5025 2716.5025
## 279 280 367 368 369 370 371
## 1749.7217 2716.5025 3381.1642 3381.1642 3381.1642 3260.3166 3260.3166
## 372 373 374 375 376 377 378
## 3260.3166 2192.0239 2192.0239 2192.0239 2353.9597 2353.9597 2353.9597cor(British$PricePremium,British$FlightDuration)## [1] 0.664292fit<-lm(PriceEconomy~SeatsEconomy,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1169.0 -639.2 -46.1 524.1 1753.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2174.0297 167.9032 12.948 < 2e-16 ***
## SeatsEconomy -4.0655 0.7331 -5.546 1.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 722.1 on 173 degrees of freedom
## Multiple R-squared: 0.1509, Adjusted R-squared: 0.146
## F-statistic: 30.75 on 1 and 173 DF, p-value: 1.079e-07British$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 8 9 10 11 12 13 14
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 15 16 17 18 19 20 21
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 22 23 24 25 26 27 28
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 29 30 31 32 33 34 35
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 36 37 38 39 40 41 42
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 43 44 45 46 47 48 49
## 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337 1678.0337
## 50 51 52 53 54 55 56
## 1678.0337 1678.0337 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060
## 57 58 59 60 61 82 83
## 1657.7060 1657.7060 1657.7060 1657.7060 1657.7060 1186.1033 1186.1033
## 84 85 86 87 88 89 99
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 942.1708
## 100 101 102 103 104 105 106
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 107 108 109 110 111 112 113
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 114 115 116 117 118 119 120
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 121 122 123 124 125 126 127
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 128 129 130 131 132 133 134
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 135 136 137 138 139 140 141
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 142 143 144 145 146 147 148
## 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708 942.1708
## 149 150 151 240 241 242 243
## 942.1708 905.5809 942.1708 1186.1033 1186.1033 1186.1033 1186.1033
## 244 245 246 247 248 249 250
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 251 252 253 254 255 256 257
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 258 259 260 261 262 263 264
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 265 266 267 268 269 270 271
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 272 273 274 275 276 277 278
## 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033 1186.1033
## 279 280 367 368 369 370 371
## 1186.1033 1186.1033 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249
## 372 373 374 375 376 377 378
## 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249 1348.7249cor(British$PricePremium,British$SeatsEconomy)## [1] -0.3006343fit<-lm(PriceEconomy~SeatsPremium,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1513.48 -572.66 -10.06 396.07 1548.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2968.309 241.589 12.29 < 2e-16 ***
## SeatsPremium -38.785 5.463 -7.10 3.1e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 689.7 on 173 degrees of freedom
## Multiple R-squared: 0.2256, Adjusted R-squared: 0.2212
## F-statistic: 50.41 on 1 and 173 DF, p-value: 3.101e-11British$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 8 9 10 11 12 13 14
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 15 16 17 18 19 20 21
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 22 23 24 25 26 27 28
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 29 30 31 32 33 34 35
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 36 37 38 39 40 41 42
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 43 44 45 46 47 48 49
## 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258 1416.9258
## 50 51 52 53 54 55 56
## 1416.9258 1416.9258 1455.7103 1455.7103 1455.7103 1455.7103 1455.7103
## 57 58 59 60 61 82 83
## 1455.7103 1455.7103 1455.7103 1455.7103 1455.7103 796.3725 796.3725
## 84 85 86 87 88 89 99
## 796.3725 796.3725 796.3725 796.3725 796.3725 796.3725 835.1571
## 100 101 102 103 104 105 106
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 107 108 109 110 111 112 113
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 114 115 116 117 118 119 120
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 121 122 123 124 125 126 127
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 128 129 130 131 132 133 134
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 135 136 137 138 139 140 141
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 142 143 144 145 146 147 148
## 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571 835.1571
## 149 150 151 240 241 242 243
## 835.1571 835.1571 835.1571 1572.0641 1572.0641 1572.0641 1572.0641
## 244 245 246 247 248 249 250
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 251 252 253 254 255 256 257
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 258 259 260 261 262 263 264
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 265 266 267 268 269 270 271
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 272 273 274 275 276 277 278
## 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641 1572.0641
## 279 280 367 368 369 370 371
## 1572.0641 1572.0641 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790
## 372 373 374 375 376 377 378
## 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790 2037.4790cor(British$PricePremium,British$SeatsPremium)## [1] -0.4260349fit<-lm(PriceEconomy~PriceRelative,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1226.20 -709.99 85.17 481.68 1620.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 950.93 96.29 9.876 < 2e-16 ***
## PriceRelative 782.89 178.69 4.381 2.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 743.5 on 173 degrees of freedom
## Multiple R-squared: 0.09987, Adjusted R-squared: 0.09467
## F-statistic: 19.2 on 1 and 173 DF, p-value: 2.041e-05British$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7
## 1248.4304 1248.4304 1248.4304 1248.4304 1475.4679 1475.4679 1475.4679
## 8 9 10 11 12 13 14
## 1757.3075 1757.3075 1538.0989 1538.0989 1389.3502 1154.4839 1358.0347
## 15 16 17 18 19 20 21
## 1358.0347 1358.0347 1248.4304 1248.4304 1248.4304 1217.1149 1217.1149
## 22 23 24 25 26 27 28
## 1217.1149 1209.2860 1209.2860 1209.2860 1224.9438 1209.2860 1209.2860
## 29 30 31 32 33 34 35
## 1217.1149 1217.1149 1217.1149 1279.7459 1279.7459 1279.7459 1279.7459
## 36 37 38 39 40 41 42
## 1459.8101 1459.8101 1459.8101 1138.8261 1138.8261 1138.8261 1138.8261
## 43 44 45 46 47 48 49
## 1084.0240 1084.0240 1084.0240 1013.5641 1013.5641 1013.5641 1358.0347
## 50 51 52 53 54 55 56
## 1358.0347 1358.0347 1757.3075 1232.7726 1232.7726 1232.7726 1217.1149
## 57 58 59 60 61 82 83
## 1217.1149 1217.1149 1115.3395 1115.3395 1428.4946 1788.6230 1788.6230
## 84 85 86 87 88 89 99
## 1788.6230 1788.6230 1264.0882 1264.0882 1264.0882 1264.0882 1334.5481
## 100 101 102 103 104 105 106
## 1334.5481 1334.5481 1334.5481 1663.3609 1663.3609 1663.3609 1663.3609
## 107 108 109 110 111 112 113
## 1318.8903 1318.8903 1318.8903 1945.2005 1945.2005 1232.7726 997.9063
## 114 115 116 117 118 119 120
## 1029.2218 1029.2218 982.2486 1037.0507 1037.0507 1013.5641 1021.3929
## 121 122 123 124 125 126 127
## 990.0774 990.0774 1037.0507 1060.5373 1084.0240 1076.1951 1068.3662
## 128 129 130 131 132 133 134
## 1005.7352 1084.0240 1091.8528 1060.5373 1052.7085 1076.1951 1091.8528
## 135 136 137 138 139 140 141
## 1091.8528 1146.6550 1107.5106 1154.4839 1099.6817 1130.9972 1130.9972
## 142 143 144 145 146 147 148
## 1185.7994 1185.7994 1185.7994 1146.6550 1177.9705 1177.9705 1177.9705
## 149 150 151 240 241 242 243
## 1264.0882 1193.6283 1209.2860 1835.5962 1835.5962 1154.4839 1303.2325
## 244 245 246 247 248 249 250
## 1303.2325 1303.2325 1232.7726 1232.7726 1232.7726 1232.7726 1718.1631
## 251 252 253 254 255 256 257
## 1718.1631 1718.1631 1209.2860 1209.2860 1209.2860 1209.2860 1232.7726
## 258 259 260 261 262 263 264
## 1232.7726 1232.7726 1835.5962 1279.7459 1279.7459 1279.7459 1264.0882
## 265 266 267 268 269 270 271
## 1264.0882 1264.0882 1577.2433 1005.7352 1005.7352 1005.7352 1819.9385
## 272 273 274 275 276 277 278
## 1819.9385 1663.3609 1107.5106 1577.2433 1084.0240 1084.0240 1084.0240
## 279 280 367 368 369 370 371
## 1115.3395 1397.1791 2039.1471 2039.1471 2039.1471 1060.5373 1060.5373
## 372 373 374 375 376 377 378
## 1060.5373 1553.7566 1326.7192 1326.7192 982.2486 1358.0347 1240.6015cor(British$PricePremium,British$PriceRelative)## [1] 0.602946fit<-lm(PriceEconomy~PercentPremiumSeats,data = British)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = British)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1188.4 -665.2 110.1 509.3 1927.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1000.23 210.95 4.741 4.41e-06 ***
## PercentPremiumSeats 16.48 11.39 1.448 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 779 on 173 degrees of freedom
## Multiple R-squared: 0.01197, Adjusted R-squared: 0.006258
## F-statistic: 2.096 on 1 and 173 DF, p-value: 0.1495British$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 3563
## [71] 3563 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486
## [85] 442 442 407 396 396 348 323 319 319 306 285 278 276 263
## [99] 247 238 237 237 234 211 201 198 175 175 172 165 156 156
## [113] 141 141 141 131 125 99 99 97 97 86 3509 3509 3509 3227
## [127] 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025 3025 3025 2472
## [141] 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866 1866 1866 1784
## [155] 1784 1784 1784 1603 1550 1199 1199 912 837 7414 7414 7414 2470 2470
## [169] 2470 1152 853 853 826 797 797fitted(fit)## 1 2 3 4 5 6 7 8
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 9 10 11 12 13 14 15 16
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 17 18 19 20 21 22 23 24
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 25 26 27 28 29 30 31 32
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 33 34 35 36 37 38 39 40
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 41 42 43 44 45 46 47 48
## 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202 1407.202
## 49 50 51 52 53 54 55 56
## 1407.202 1407.202 1407.202 1387.422 1387.422 1387.422 1387.422 1387.422
## 57 58 59 60 61 82 83 84
## 1387.422 1387.422 1387.422 1387.422 1387.422 1308.962 1308.962 1308.962
## 85 86 87 88 89 99 100 101
## 1308.962 1308.962 1308.962 1308.962 1308.962 1253.414 1253.414 1253.414
## 102 103 104 105 106 107 108 109
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 110 111 112 113 114 115 116 117
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 118 119 120 121 122 123 124 125
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 126 127 128 129 130 131 132 133
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 134 135 136 137 138 139 140 141
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 142 143 144 145 146 147 148 149
## 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414 1253.414
## 150 151 240 241 242 243 244 245
## 1247.315 1253.414 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 246 247 248 249 250 251 252 253
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 254 255 256 257 258 259 260 261
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 262 263 264 265 266 267 268 269
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 270 271 272 273 274 275 276 277
## 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865 1212.865
## 278 279 280 367 368 369 370 371
## 1212.865 1212.865 1212.865 1174.459 1174.459 1174.459 1174.459 1174.459
## 372 373 374 375 376 377 378
## 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459 1174.459cor(British$PricePremium,British$PercentPremiumSeats)## [1] 0.03194159Virgin Airlines
Analyse all about Virgin Airlines:-
Virgin <- airline[ which(airline$Airline=='Virgin'),]
View(Virgin)
summary(Virgin)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:33 Min. : 6.580 Aug:16
## British : 0 Boeing:29 1st Qu.: 7.473 Jul:14
## Delta : 0 Median : 8.830 Oct:16
## Jet : 0 Mean : 9.250 Sep:16
## Singapore: 0 3rd Qu.:10.830
## Virgin :62 Max. :12.580
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :185.0 Min. :35.00 Min. :31
## International:62 1st Qu.:198.0 1st Qu.:35.00 1st Qu.:31
## Median :198.0 Median :38.00 Median :31
## Mean :230.2 Mean :42.53 Mean :31
## 3rd Qu.:233.0 3rd Qu.:48.00 3rd Qu.:31
## Max. :375.0 Max. :66.00 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :18 Min. :21 Min. : 540 Min. : 594
## 1st Qu.:38 1st Qu.:18 1st Qu.:21 1st Qu.:1434 1st Qu.:2499
## Median :38 Median :18 Median :21 Median :1774 Median :2973
## Mean :38 Mean :18 Mean :21 Mean :1604 Mean :2722
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:21 3rd Qu.:1903 3rd Qu.:3128
## Max. :38 Max. :18 Max. :21 Max. :2445 Max. :3694
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.1000 Min. :233.0 Min. :7 Min. :3
## 1st Qu.:0.4000 1st Qu.:233.0 1st Qu.:7 1st Qu.:3
## Median :0.7300 Median :233.0 Median :7 Median :3
## Mean :0.7606 Mean :272.7 Mean :7 Mean :3
## 3rd Qu.:1.0150 3rd Qu.:271.0 3rd Qu.:7 3rd Qu.:3
## Max. :1.8200 Max. :441.0 Max. :7 Max. :3
## PercentPremiumSeats
## Min. :14.02
## 1st Qu.:14.02
## Median :15.02
## Mean :15.75
## 3rd Qu.:15.02
## Max. :20.60Check the all the means now all Virgin aircrafts
mean(Virgin$PriceEconomy)## [1] 1603.532mean(Virgin$PricePremium)## [1] 2721.694mean(Virgin$FlightDuration)## [1] 9.250484mean(Virgin$PitchEconomy)## [1] 31mean(Virgin$PitchPremium)## [1] 38mean(Virgin$WidthEconomy)## [1] 18mean(Virgin$WidthPremium)## [1] 21mean(Virgin$PriceRelative)## [1] 0.7606452mean(Virgin$PitchDifference)## [1] 7mean(Virgin$WidthDifference)## [1] 3Now Analyse separately for Each Aircrafts in Virgin Airlines
Vrboeing <- Virgin[ which(Virgin$Aircraft=='Boeing'),]
View(Vrboeing)
summary(Vrboeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 0 Min. : 7.66 Aug:7
## British : 0 Boeing:29 1st Qu.: 9.91 Jul:6
## Delta : 0 Median :10.83 Oct:9
## Jet : 0 Mean :10.67 Sep:7
## Singapore: 0 3rd Qu.:11.33
## Virgin :29 Max. :12.58
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :198.0 Min. :35.00 Min. :31
## International:29 1st Qu.:198.0 1st Qu.:35.00 1st Qu.:31
## Median :198.0 Median :35.00 Median :31
## Mean :246.8 Mean :43.55 Mean :31
## 3rd Qu.:375.0 3rd Qu.:66.00 3rd Qu.:31
## Max. :375.0 Max. :66.00 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :18 Min. :21 Min. : 574 Min. :1465
## 1st Qu.:38 1st Qu.:18 1st Qu.:21 1st Qu.:1086 1st Qu.:2531
## Median :38 Median :18 Median :21 Median :1580 Median :2964
## Mean :38 Mean :18 Mean :21 Mean :1551 Mean :2803
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:21 3rd Qu.:1811 3rd Qu.:3509
## Max. :38 Max. :18 Max. :21 Max. :2445 Max. :3694
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.2600 Min. :233.0 Min. :7 Min. :3
## 1st Qu.:0.5100 1st Qu.:233.0 1st Qu.:7 1st Qu.:3
## Median :0.9100 Median :233.0 Median :7 Median :3
## Mean :0.9538 Mean :290.4 Mean :7 Mean :3
## 3rd Qu.:1.3800 3rd Qu.:441.0 3rd Qu.:7 3rd Qu.:3
## Max. :1.8200 Max. :441.0 Max. :7 Max. :3
## PercentPremiumSeats
## Min. :14.97
## 1st Qu.:14.97
## Median :15.02
## Mean :15.01
## 3rd Qu.:15.02
## Max. :15.02mean(Vrboeing$PriceEconomy)## [1] 1550.621mean(Vrboeing$PricePremium)## [1] 2802.69library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Vrboeing$PriceEconomy,Vrboeing$TravelMonth,mean))
y2<-c(by(Vrboeing$PricePremium,Vrboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -878.1 -250.1 56.7 101.6 930.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3360.46 755.41 4.449 0.000134 ***
## FlightDuration -169.64 70.18 -2.417 0.022662 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 541 on 27 degrees of freedom
## Multiple R-squared: 0.1779, Adjusted R-squared: 0.1475
## F-statistic: 5.843 on 1 and 27 DF, p-value: 0.02266Vrboeing$PriceEconomy## [1] 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580
## [15] 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811
## [29] 1356fitted(fit)## 156 157 158 159 160 161 162 163
## 1452.056 1452.056 1452.056 1452.056 1311.258 1311.258 1311.258 1311.258
## 164 165 166 167 168 169 170 171
## 1679.369 1679.369 1679.369 1679.369 1523.303 1523.303 1523.303 1523.303
## 172 173 174 175 176 177 178 179
## 1594.551 1226.440 1536.874 1536.874 1536.874 1536.874 1226.440 1438.485
## 180 181 182 183 184
## 2061.050 2061.050 2061.050 2061.050 1226.440cor(Vrboeing$PriceEconomy,Vrboeing$FlightDuration)## [1] -0.4218011fit<-lm(PriceEconomy~SeatsEconomy,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -762.38 -332.00 19.62 332.00 1032.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 467.621 289.599 1.615 0.117999
## SeatsEconomy 4.388 1.117 3.927 0.000537 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476 on 27 degrees of freedom
## Multiple R-squared: 0.3635, Adjusted R-squared: 0.34
## F-statistic: 15.42 on 1 and 27 DF, p-value: 0.0005366Vrboeing$PriceEconomy## [1] 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580
## [15] 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811
## [29] 1356fitted(fit)## 156 157 158 159 160 161 162 163
## 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381
## 164 165 166 167 168 169 170 171
## 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381 1336.381 1336.381
## 172 173 174 175 176 177 178 179
## 1336.381 1336.381 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381
## 180 181 182 183 184
## 1336.381 1336.381 1336.381 1336.381 1336.381cor(Vrboeing$PriceEconomy,Vrboeing$SeatsEconomy)## [1] 0.6029359fit<-lm(PriceEconomy~PriceRelative,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -945.78 -191.35 -6.34 243.60 532.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2328.6 151.8 15.339 7.48e-15 ***
## PriceRelative -815.7 139.3 -5.858 3.08e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 395.9 on 27 degrees of freedom
## Multiple R-squared: 0.5596, Adjusted R-squared: 0.5433
## F-statistic: 34.31 on 1 and 27 DF, p-value: 3.08e-06Vrboeing$PriceEconomy## [1] 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580
## [15] 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811
## [29] 1356fitted(fit)## 156 157 158 159 160 161 162
## 844.0523 844.0523 844.0523 844.0523 917.4657 917.4657 917.4657
## 163 164 165 166 167 168 169
## 1202.9620 1537.4007 1537.4007 1537.4007 1537.4007 1586.3429 1586.3429
## 170 171 172 173 174 175 176
## 1586.3429 1586.3429 1643.4422 1871.8393 1912.6245 1912.6245 1912.6245
## 177 178 179 180 181 182 183
## 1912.6245 1920.7815 1928.9386 2002.3519 2002.3519 2002.3519 2002.3519
## 184
## 2116.5505cor(Vrboeing$PriceEconomy,Vrboeing$PriceRelative)## [1] -0.7480927fit<-lm(PriceEconomy~PercentPremiumSeats,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -762.38 -332.00 19.62 332.00 1032.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234633 59353 3.953 0.000501 ***
## PercentPremiumSeats -15532 3955 -3.927 0.000537 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476 on 27 degrees of freedom
## Multiple R-squared: 0.3635, Adjusted R-squared: 0.34
## F-statistic: 15.42 on 1 and 27 DF, p-value: 0.0005366Vrboeing$PriceEconomy## [1] 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580
## [15] 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811
## [29] 1356fitted(fit)## 156 157 158 159 160 161 162 163
## 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381
## 164 165 166 167 168 169 170 171
## 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381 1336.381 1336.381
## 172 173 174 175 176 177 178 179
## 1336.381 1336.381 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381
## 180 181 182 183 184
## 1336.381 1336.381 1336.381 1336.381 1336.381cor(Vrboeing$PriceEconomy,Vrboeing$PercentPremiumSeats)## [1] -0.6029359fit<-lm(PricePremium~FlightDuration,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1121.8 -611.7 234.5 620.5 900.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4008.26 1069.08 3.749 0.000856 ***
## FlightDuration -113.00 99.31 -1.138 0.265208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 765.6 on 27 degrees of freedom
## Multiple R-squared: 0.04575, Adjusted R-squared: 0.01041
## F-statistic: 1.295 on 1 and 27 DF, p-value: 0.2652Vrboeing$PricePremium## [1] 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019
## [15] 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531
## [29] 1710fitted(fit)## 156 157 158 159 160 161 162 163
## 2737.034 2737.034 2737.034 2737.034 2643.245 2643.245 2643.245 2643.245
## 164 165 166 167 168 169 170 171
## 2888.451 2888.451 2888.451 2888.451 2784.493 2784.493 2784.493 2784.493
## 172 173 174 175 176 177 178 179
## 2831.952 2586.746 2793.533 2793.533 2793.533 2793.533 2586.746 2727.994
## 180 181 182 183 184
## 3142.697 3142.697 3142.697 3142.697 2586.746cor(Vrboeing$PricePremium,Vrboeing$FlightDuration)## [1] -0.2138983fit<-lm(PriceEconomy~SeatsEconomy,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -762.38 -332.00 19.62 332.00 1032.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 467.621 289.599 1.615 0.117999
## SeatsEconomy 4.388 1.117 3.927 0.000537 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476 on 27 degrees of freedom
## Multiple R-squared: 0.3635, Adjusted R-squared: 0.34
## F-statistic: 15.42 on 1 and 27 DF, p-value: 0.0005366Vrboeing$PricePremium## [1] 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019
## [15] 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531
## [29] 1710fitted(fit)## 156 157 158 159 160 161 162 163
## 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381
## 164 165 166 167 168 169 170 171
## 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381 1336.381 1336.381
## 172 173 174 175 176 177 178 179
## 1336.381 1336.381 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381
## 180 181 182 183 184
## 1336.381 1336.381 1336.381 1336.381 1336.381cor(Vrboeing$PricePremium,Vrboeing$SeatsEconomy)## [1] 0.6519771fit<-lm(PriceEconomy~SeatsPremium,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -762.38 -332.00 19.62 332.00 1032.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 459.553 291.556 1.576 0.126624
## SeatsPremium 25.052 6.379 3.927 0.000537 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476 on 27 degrees of freedom
## Multiple R-squared: 0.3635, Adjusted R-squared: 0.34
## F-statistic: 15.42 on 1 and 27 DF, p-value: 0.0005366Vrboeing$PricePremium## [1] 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019
## [15] 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531
## [29] 1710fitted(fit)## 156 157 158 159 160 161 162 163
## 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381
## 164 165 166 167 168 169 170 171
## 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381 1336.381 1336.381
## 172 173 174 175 176 177 178 179
## 1336.381 1336.381 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381
## 180 181 182 183 184
## 1336.381 1336.381 1336.381 1336.381 1336.381cor(Vrboeing$PricePremium,Vrboeing$SeatsPremium)## [1] 0.6519771fit<-lm(PriceEconomy~PriceRelative,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -945.78 -191.35 -6.34 243.60 532.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2328.6 151.8 15.339 7.48e-15 ***
## PriceRelative -815.7 139.3 -5.858 3.08e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 395.9 on 27 degrees of freedom
## Multiple R-squared: 0.5596, Adjusted R-squared: 0.5433
## F-statistic: 34.31 on 1 and 27 DF, p-value: 3.08e-06Vrboeing$PricePremium## [1] 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019
## [15] 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531
## [29] 1710fitted(fit)## 156 157 158 159 160 161 162
## 844.0523 844.0523 844.0523 844.0523 917.4657 917.4657 917.4657
## 163 164 165 166 167 168 169
## 1202.9620 1537.4007 1537.4007 1537.4007 1537.4007 1586.3429 1586.3429
## 170 171 172 173 174 175 176
## 1586.3429 1586.3429 1643.4422 1871.8393 1912.6245 1912.6245 1912.6245
## 177 178 179 180 181 182 183
## 1912.6245 1920.7815 1928.9386 2002.3519 2002.3519 2002.3519 2002.3519
## 184
## 2116.5505cor(Vrboeing$PricePremium,Vrboeing$PriceRelative)## [1] -0.2812307fit<-lm(PriceEconomy~PercentPremiumSeats,data = Vrboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Vrboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -762.38 -332.00 19.62 332.00 1032.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234633 59353 3.953 0.000501 ***
## PercentPremiumSeats -15532 3955 -3.927 0.000537 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476 on 27 degrees of freedom
## Multiple R-squared: 0.3635, Adjusted R-squared: 0.34
## F-statistic: 15.42 on 1 and 27 DF, p-value: 0.0005366Vrboeing$PricePremium## [1] 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019
## [15] 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531
## [29] 1710fitted(fit)## 156 157 158 159 160 161 162 163
## 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381 1336.381
## 164 165 166 167 168 169 170 171
## 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381 1336.381 1336.381
## 172 173 174 175 176 177 178 179
## 1336.381 1336.381 2113.000 2113.000 2113.000 2113.000 1336.381 1336.381
## 180 181 182 183 184
## 1336.381 1336.381 1336.381 1336.381 1336.381cor(Vrboeing$PricePremium,Vrboeing$PercentPremiumSeats)## [1] -0.6519771Vrairbus <-Virgin[ which(Virgin$Aircraft=='AirBus'),]
View(Vrairbus)
summary(Vrairbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:33 Min. : 6.580 Aug:9
## British : 0 Boeing: 0 1st Qu.: 7.080 Jul:8
## Delta : 0 Median : 7.750 Oct:7
## Jet : 0 Mean : 8.004 Sep:9
## Singapore: 0 3rd Qu.: 8.830
## Virgin :33 Max. :11.330
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :185.0 Min. :38.00 Min. :31
## International:33 1st Qu.:185.0 1st Qu.:38.00 1st Qu.:31
## Median :233.0 Median :38.00 Median :31
## Mean :215.5 Mean :41.64 Mean :31
## 3rd Qu.:233.0 3rd Qu.:48.00 3rd Qu.:31
## Max. :233.0 Max. :48.00 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :18 Min. :21 Min. : 540 Min. : 594
## 1st Qu.:38 1st Qu.:18 1st Qu.:21 1st Qu.:1476 1st Qu.:2499
## Median :38 Median :18 Median :21 Median :1813 Median :2982
## Mean :38 Mean :18 Mean :21 Mean :1650 Mean :2651
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:21 3rd Qu.:1919 3rd Qu.:3128
## Max. :38 Max. :18 Max. :21 Max. :2369 Max. :3540
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.1000 Min. :233.0 Min. :7 Min. :3
## 1st Qu.:0.3900 1st Qu.:233.0 1st Qu.:7 1st Qu.:3
## Median :0.4900 Median :271.0 Median :7 Median :3
## Mean :0.5909 Mean :257.2 Mean :7 Mean :3
## 3rd Qu.:0.8400 3rd Qu.:271.0 3rd Qu.:7 3rd Qu.:3
## Max. :1.0800 Max. :271.0 Max. :7 Max. :3
## PercentPremiumSeats
## Min. :14.02
## 1st Qu.:14.02
## Median :14.02
## Mean :16.41
## 3rd Qu.:20.60
## Max. :20.60mean(Vrairbus$PriceEconomy)## [1] 1650.03mean(Vrairbus$PricePremium)## [1] 2650.515library(plotly)
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Vrairbus$PriceEconomy,Vrairbus$TravelMonth,mean))
y4<-c(by(Vrairbus$PricePremium,Vrairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1069.00 -39.29 163.61 212.93 418.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 356.92 459.77 0.776 0.44345
## FlightDuration 161.56 56.64 2.852 0.00766 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 439.5 on 31 degrees of freedom
## Multiple R-squared: 0.2079, Adjusted R-squared: 0.1823
## F-statistic: 8.136 on 1 and 31 DF, p-value: 0.00766Vrairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 1434 1434
## [15] 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767
## [29] 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1649.394 1649.394 1649.394 1649.394 1783.488 1783.488 1783.488 1783.488
## 70 71 72 73 191 192 193 194
## 1500.759 1500.759 1500.759 1609.004 1473.294 1473.294 1473.294 1473.294
## 195 196 197 198 199 200 201 202
## 1419.979 1419.979 1419.979 1419.979 2038.753 2038.753 2038.753 2187.387
## 203 204 205 206 207 208 209 210
## 2187.387 1554.074 1554.074 1554.074 1554.074 1500.759 1609.004 1609.004
## 211
## 1609.004cor(Vrairbus$PriceEconomy,Vrairbus$FlightDuration)## [1] 0.4559439fit<-lm(PriceEconomy~SeatsEconomy,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1273.08 -80.86 105.92 238.92 812.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2800.622 779.795 3.591 0.00112 **
## SeatsEconomy -5.338 3.597 -1.484 0.14792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 477.1 on 31 degrees of freedom
## Multiple R-squared: 0.06632, Adjusted R-squared: 0.03621
## F-statistic: 2.202 on 1 and 31 DF, p-value: 0.1479Vrairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 1434 1434
## [15] 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767
## [29] 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1813.083 1813.083 1813.083 1813.083 1813.083 1813.083 1813.083 1813.083
## 70 71 72 73 191 192 193 194
## 1813.083 1813.083 1813.083 1813.083 1556.857 1556.857 1556.857 1556.857
## 195 196 197 198 199 200 201 202
## 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857
## 203 204 205 206 207 208 209 210
## 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857
## 211
## 1556.857cor(Vrairbus$PriceEconomy,Vrairbus$SeatsEconomy)## [1] -0.2575346fit<-lm(PriceEconomy~PriceRelative,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1001.1 -271.4 157.1 342.4 741.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1518.9 172.1 8.824 5.81e-10 ***
## PriceRelative 221.9 253.4 0.876 0.388
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 487.8 on 31 degrees of freedom
## Multiple R-squared: 0.02414, Adjusted R-squared: -0.007343
## F-statistic: 0.7667 on 1 and 31 DF, p-value: 0.388Vrairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 1434 1434
## [15] 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767
## [29] 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1680.890 1680.890 1680.890 1680.890 1605.455 1605.455 1605.455 1605.455
## 70 71 72 73 191 192 193 194
## 1576.612 1576.612 1576.612 1541.113 1758.545 1758.545 1758.545 1758.545
## 195 196 197 198 199 200 201 202
## 1747.451 1747.451 1747.451 1747.451 1705.296 1705.296 1705.296 1627.642
## 203 204 205 206 207 208 209 210
## 1627.642 1609.892 1609.892 1609.892 1609.892 1576.612 1541.113 1541.113
## 211
## 1541.113cor(Vrairbus$PriceEconomy,Vrairbus$PriceRelative)## [1] 0.1553585fit<-lm(PriceEconomy~PercentPremiumSeats,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1273.08 -80.86 105.92 238.92 812.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1010.92 438.62 2.305 0.028 *
## PercentPremiumSeats 38.94 26.24 1.484 0.148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 477.1 on 31 degrees of freedom
## Multiple R-squared: 0.06632, Adjusted R-squared: 0.03621
## F-statistic: 2.202 on 1 and 31 DF, p-value: 0.1479Vrairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 1434 1434
## [15] 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767
## [29] 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1813.083 1813.083 1813.083 1813.083 1813.083 1813.083 1813.083 1813.083
## 70 71 72 73 191 192 193 194
## 1813.083 1813.083 1813.083 1813.083 1556.857 1556.857 1556.857 1556.857
## 195 196 197 198 199 200 201 202
## 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857
## 203 204 205 206 207 208 209 210
## 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857 1556.857
## 211
## 1556.857fit<-lm(PricePremium~FlightDuration,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2002.9 -26.2 187.7 478.3 646.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 961.7 847.7 1.134 0.2653
## FlightDuration 211.0 104.4 2.020 0.0521 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 810.3 on 31 degrees of freedom
## Multiple R-squared: 0.1163, Adjusted R-squared: 0.08784
## F-statistic: 4.082 on 1 and 31 DF, p-value: 0.05206Vrairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 2982 2982
## [15] 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499
## [29] 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 2649.684 2649.684 2649.684 2649.684 2824.810 2824.810 2824.810 2824.810
## 70 71 72 73 191 192 193 194
## 2455.568 2455.568 2455.568 2596.935 2419.699 2419.699 2419.699 2419.699
## 195 196 197 198 199 200 201 202
## 2350.071 2350.071 2350.071 2350.071 3158.182 3158.182 3158.182 3352.298
## 203 204 205 206 207 208 209 210
## 3352.298 2525.197 2525.197 2525.197 2525.197 2455.568 2596.935 2596.935
## 211
## 2596.935cor(Vrairbus$PricePremium,Vrairbus$FlightDuration)## [1] 0.3410968fit<-lm(PricePremium~SeatsEconomy,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2058.9 -153.9 329.1 481.6 887.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2621.5940 1408.8114 1.861 0.0723 .
## SeatsEconomy 0.1342 6.4988 0.021 0.9837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 862 on 31 degrees of freedom
## Multiple R-squared: 1.375e-05, Adjusted R-squared: -0.03224
## F-statistic: 0.0004263 on 1 and 31 DF, p-value: 0.9837Vrairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 2982 2982
## [15] 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499
## [29] 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417
## 70 71 72 73 191 192 193 194
## 2646.417 2646.417 2646.417 2646.417 2652.857 2652.857 2652.857 2652.857
## 195 196 197 198 199 200 201 202
## 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857
## 203 204 205 206 207 208 209 210
## 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857
## 211
## 2652.857cor(Vrairbus$PricePremium,Vrairbus$SeatsEconomy)## [1] 0.003708144fit<-lm(PricePremium~SeatsPremium,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2058.9 -153.9 329.1 481.6 887.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2677.331 1307.464 2.048 0.0491 *
## SeatsPremium -0.644 31.195 -0.021 0.9837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 862 on 31 degrees of freedom
## Multiple R-squared: 1.375e-05, Adjusted R-squared: -0.03224
## F-statistic: 0.0004263 on 1 and 31 DF, p-value: 0.9837Vrairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 2982 2982
## [15] 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499
## [29] 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417
## 70 71 72 73 191 192 193 194
## 2646.417 2646.417 2646.417 2646.417 2652.857 2652.857 2652.857 2652.857
## 195 196 197 198 199 200 201 202
## 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857
## 203 204 205 206 207 208 209 210
## 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857
## 211
## 2652.857cor(Vrairbus$PricePremium,Vrairbus$SeatsPremium)## [1] -0.003708144fit<-lm(PricePremium~PriceRelative,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1223.8 -398.4 241.5 435.9 1060.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1648.1 222.9 7.394 2.52e-08 ***
## PriceRelative 1696.4 328.1 5.170 1.32e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 631.7 on 31 degrees of freedom
## Multiple R-squared: 0.463, Adjusted R-squared: 0.4457
## F-statistic: 26.73 on 1 and 31 DF, p-value: 1.32e-05Vrairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 2982 2982
## [15] 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499
## [29] 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 2886.463 2886.463 2886.463 2886.463 2309.702 2309.702 2309.702 2309.702
## 70 71 72 73 191 192 193 194
## 2089.176 2089.176 2089.176 1817.759 3480.187 3480.187 3480.187 3480.187
## 195 196 197 198 199 200 201 202
## 3395.369 3395.369 3395.369 3395.369 3073.062 3073.062 3073.062 2479.338
## 203 204 205 206 207 208 209 210
## 2479.338 2343.629 2343.629 2343.629 2343.629 2089.176 1817.759 1817.759
## 211
## 1817.759cor(Vrairbus$PricePremium,Vrairbus$PriceRelative)## [1] 0.6804258fit<-lm(PricePremium~PercentPremiumSeats,data = Vrairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = Vrairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2058.9 -153.9 329.1 481.6 887.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2666.5799 792.4323 3.365 0.00205 **
## PercentPremiumSeats -0.9788 47.4080 -0.021 0.98366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 862 on 31 degrees of freedom
## Multiple R-squared: 1.375e-05, Adjusted R-squared: -0.03224
## F-statistic: 0.0004263 on 1 and 31 DF, p-value: 0.9837Vrairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 2982 2982
## [15] 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499
## [29] 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417 2646.417
## 70 71 72 73 191 192 193 194
## 2646.417 2646.417 2646.417 2646.417 2652.857 2652.857 2652.857 2652.857
## 195 196 197 198 199 200 201 202
## 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857
## 203 204 205 206 207 208 209 210
## 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857 2652.857
## 211
## 2652.857cor(Vrairbus$PricePremium,Vrairbus$PercentPremiumSeats)## [1] -0.003708144Now We Should Analyse the international aircrafts of Virgin Airlines
Vrint <- Virgin[ which(Virgin$IsInternational=='International'),]
View(Vrint)
summary(Vrint)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:33 Min. : 6.580 Aug:16
## British : 0 Boeing:29 1st Qu.: 7.473 Jul:14
## Delta : 0 Median : 8.830 Oct:16
## Jet : 0 Mean : 9.250 Sep:16
## Singapore: 0 3rd Qu.:10.830
## Virgin :62 Max. :12.580
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :185.0 Min. :35.00 Min. :31
## International:62 1st Qu.:198.0 1st Qu.:35.00 1st Qu.:31
## Median :198.0 Median :38.00 Median :31
## Mean :230.2 Mean :42.53 Mean :31
## 3rd Qu.:233.0 3rd Qu.:48.00 3rd Qu.:31
## Max. :375.0 Max. :66.00 Max. :31
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :18 Min. :21 Min. : 540 Min. : 594
## 1st Qu.:38 1st Qu.:18 1st Qu.:21 1st Qu.:1434 1st Qu.:2499
## Median :38 Median :18 Median :21 Median :1774 Median :2973
## Mean :38 Mean :18 Mean :21 Mean :1604 Mean :2722
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:21 3rd Qu.:1903 3rd Qu.:3128
## Max. :38 Max. :18 Max. :21 Max. :2445 Max. :3694
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.1000 Min. :233.0 Min. :7 Min. :3
## 1st Qu.:0.4000 1st Qu.:233.0 1st Qu.:7 1st Qu.:3
## Median :0.7300 Median :233.0 Median :7 Median :3
## Mean :0.7606 Mean :272.7 Mean :7 Mean :3
## 3rd Qu.:1.0150 3rd Qu.:271.0 3rd Qu.:7 3rd Qu.:3
## Max. :1.8200 Max. :441.0 Max. :7 Max. :3
## PercentPremiumSeats
## Min. :14.02
## 1st Qu.:14.02
## Median :15.02
## Mean :15.75
## 3rd Qu.:15.02
## Max. :20.60mean(Vrint$PriceEconomy)## [1] 1603.532mean(Vrint$PricePremium)## [1] 2721.694library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Vrint$PriceEconomy,Vrint$TravelMonth,mean))
y2<-c(by(Vrint$PricePremium,Vrint$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1092.6 -214.9 152.2 273.4 870.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1782.82 334.46 5.330 1.56e-06 ***
## FlightDuration -19.38 35.40 -0.548 0.586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 536 on 60 degrees of freedom
## Multiple R-squared: 0.004972, Adjusted R-squared: -0.01161
## F-statistic: 0.2998 on 1 and 60 DF, p-value: 0.586Vrint$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1627.769 1627.769 1627.769 1627.769 1611.682 1611.682 1611.682 1611.682
## 70 71 72 73 156 157 158 159
## 1645.600 1645.600 1645.600 1632.614 1564.778 1564.778 1564.778 1564.778
## 160 161 162 163 164 165 166 167
## 1548.691 1548.691 1548.691 1548.691 1590.750 1590.750 1590.750 1590.750
## 168 169 170 171 172 173 174 175
## 1572.918 1572.918 1572.918 1572.918 1581.059 1539.000 1574.469 1574.469
## 176 177 178 179 180 181 182 183
## 1574.469 1574.469 1539.000 1563.227 1634.359 1634.359 1634.359 1634.359
## 184 191 192 193 194 195 196 197
## 1539.000 1648.895 1648.895 1648.895 1648.895 1655.291 1655.291 1655.291
## 198 199 200 201 202 203 204 205
## 1655.291 1581.059 1581.059 1581.059 1563.227 1563.227 1639.204 1639.204
## 206 207 208 209 210 211
## 1639.204 1639.204 1645.600 1632.614 1632.614 1632.614cor(Vrint$PriceEconomy,Vrint$FlightDuration)## [1] -0.0705092fit<-lm(PriceEconomy~SeatsEconomy,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1072.2 -265.0 114.8 347.5 863.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 900.280 259.407 3.471 0.000969 ***
## SeatsEconomy 3.055 1.092 2.798 0.006903 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 505.4 on 60 degrees of freedom
## Multiple R-squared: 0.1154, Adjusted R-squared: 0.1007
## F-statistic: 7.829 on 1 and 60 DF, p-value: 0.006903Vrint$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503
## 70 71 72 73 156 157 158 159
## 1465.503 1465.503 1465.503 1465.503 1505.222 1505.222 1505.222 1505.222
## 160 161 162 163 164 165 166 167
## 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003 2046.003 2046.003
## 168 169 170 171 172 173 174 175
## 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003
## 176 177 178 179 180 181 182 183
## 2046.003 2046.003 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222
## 184 191 192 193 194 195 196 197
## 1505.222 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 198 199 200 201 202 203 204 205
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 206 207 208 209 210 211
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156cor(Vrint$PriceEconomy,Vrint$SeatsEconomy)## [1] 0.3397343fit<-lm(PriceEconomy~PriceRelative,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1366.00 -23.32 43.60 273.32 726.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1951.8 118.1 16.530 < 2e-16 ***
## PriceRelative -457.8 131.9 -3.472 0.000965 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 490.3 on 60 degrees of freedom
## Multiple R-squared: 0.1673, Adjusted R-squared: 0.1534
## F-statistic: 12.05 on 1 and 60 DF, p-value: 0.0009654Vrint$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1617.563 1617.563 1617.563 1617.563 1773.229 1773.229 1773.229 1773.229
## 70 71 72 73 156 157 158 159
## 1832.748 1832.748 1832.748 1906.003 1118.516 1118.516 1118.516 1118.516
## 160 161 162 163 164 165 166 167
## 1159.721 1159.721 1159.721 1319.966 1507.681 1507.681 1507.681 1507.681
## 168 169 170 171 172 173 174 175
## 1535.151 1535.151 1535.151 1535.151 1567.200 1695.396 1718.288 1718.288
## 176 177 178 179 180 181 182 183
## 1718.288 1718.288 1722.866 1727.445 1768.651 1768.651 1768.651 1768.651
## 184 191 192 193 194 195 196 197
## 1832.748 1457.318 1457.318 1457.318 1457.318 1480.210 1480.210 1480.210
## 198 199 200 201 202 203 204 205
## 1480.210 1567.200 1567.200 1567.200 1727.445 1727.445 1764.072 1764.072
## 206 207 208 209 210 211
## 1764.072 1764.072 1832.748 1906.003 1906.003 1906.003cor(Vrint$PriceEconomy,Vrint$PriceRelative)## [1] -0.4089837fit<-lm(PriceEconomy~PercentPremiumSeats,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1262.18 -98.41 63.82 246.76 873.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 957.60 442.67 2.163 0.0345 *
## PercentPremiumSeats 41.00 27.77 1.476 0.1451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 527.8 on 60 degrees of freedom
## Multiple R-squared: 0.03505, Adjusted R-squared: 0.01896
## F-statistic: 2.179 on 1 and 60 DF, p-value: 0.1451Vrint$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179
## 70 71 72 73 156 157 158 159
## 1802.179 1802.179 1802.179 1802.179 1573.405 1573.405 1573.405 1573.405
## 160 161 162 163 164 165 166 167
## 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355 1571.355 1571.355
## 168 169 170 171 172 173 174 175
## 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355
## 176 177 178 179 180 181 182 183
## 1571.355 1571.355 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405
## 184 191 192 193 194 195 196 197
## 1573.405 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 198 199 200 201 202 203 204 205
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 206 207 208 209 210 211
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406cor(Vrint$PriceEconomy,Vrint$PercentPremiumSeats)## [1] 0.1872078fit<-lm(PricePremium~FlightDuration,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2047.5 -124.4 184.8 473.1 892.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2227.50 505.17 4.409 4.37e-05 ***
## FlightDuration 53.42 53.47 0.999 0.322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 809.6 on 60 degrees of freedom
## Multiple R-squared: 0.01637, Adjusted R-squared: -2.707e-05
## F-statistic: 0.9983 on 1 and 60 DF, p-value: 0.3217Vrint$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 2654.889 2654.889 2654.889 2654.889 2699.230 2699.230 2699.230 2699.230
## 70 71 72 73 156 157 158 159
## 2605.739 2605.739 2605.739 2641.533 2828.514 2828.514 2828.514 2828.514
## 160 161 162 163 164 165 166 167
## 2872.856 2872.856 2872.856 2872.856 2756.927 2756.927 2756.927 2756.927
## 168 169 170 171 172 173 174 175
## 2806.076 2806.076 2806.076 2806.076 2783.639 2899.567 2801.803 2801.803
## 176 177 178 179 180 181 182 183
## 2801.803 2801.803 2899.567 2832.788 2636.725 2636.725 2636.725 2636.725
## 184 191 192 193 194 195 196 197
## 2899.567 2596.657 2596.657 2596.657 2596.657 2579.028 2579.028 2579.028
## 198 199 200 201 202 203 204 205
## 2579.028 2783.639 2783.639 2783.639 2832.788 2832.788 2623.369 2623.369
## 206 207 208 209 210 211
## 2623.369 2623.369 2605.739 2641.533 2641.533 2641.533cor(Vrint$PricePremium,Vrint$FlightDuration)## [1] 0.1279329fit<-lm(PriceEconomy~SeatsEconomy,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1072.2 -265.0 114.8 347.5 863.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 900.280 259.407 3.471 0.000969 ***
## SeatsEconomy 3.055 1.092 2.798 0.006903 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 505.4 on 60 degrees of freedom
## Multiple R-squared: 0.1154, Adjusted R-squared: 0.1007
## F-statistic: 7.829 on 1 and 60 DF, p-value: 0.006903Vrint$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503
## 70 71 72 73 156 157 158 159
## 1465.503 1465.503 1465.503 1465.503 1505.222 1505.222 1505.222 1505.222
## 160 161 162 163 164 165 166 167
## 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003 2046.003 2046.003
## 168 169 170 171 172 173 174 175
## 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003
## 176 177 178 179 180 181 182 183
## 2046.003 2046.003 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222
## 184 191 192 193 194 195 196 197
## 1505.222 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 198 199 200 201 202 203 204 205
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 206 207 208 209 210 211
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156cor(Vrint$PricePremium,Vrint$SeatsEconomy)## [1] 0.4124598fit<-lm(PriceEconomy~SeatsPremium,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1197.2 -331.0 118.2 304.7 949.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 564.120 259.600 2.173 0.033737 *
## SeatsPremium 24.438 5.937 4.116 0.000119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 474.5 on 60 degrees of freedom
## Multiple R-squared: 0.2202, Adjusted R-squared: 0.2072
## F-statistic: 16.94 on 1 and 60 DF, p-value: 0.0001194Vrint$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1737.154 1737.154 1737.154 1737.154 1737.154 1737.154 1737.154 1737.154
## 70 71 72 73 156 157 158 159
## 1737.154 1737.154 1737.154 1737.154 1419.457 1419.457 1419.457 1419.457
## 160 161 162 163 164 165 166 167
## 1419.457 1419.457 1419.457 1419.457 2177.042 2177.042 2177.042 2177.042
## 168 169 170 171 172 173 174 175
## 1419.457 1419.457 1419.457 1419.457 1419.457 1419.457 2177.042 2177.042
## 176 177 178 179 180 181 182 183
## 2177.042 2177.042 1419.457 1419.457 1419.457 1419.457 1419.457 1419.457
## 184 191 192 193 194 195 196 197
## 1419.457 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772
## 198 199 200 201 202 203 204 205
## 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772
## 206 207 208 209 210 211
## 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772cor(Vrint$PricePremium,Vrint$SeatsPremium)## [1] 0.3999636fit<-lm(PriceEconomy~PriceRelative,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1366.00 -23.32 43.60 273.32 726.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1951.8 118.1 16.530 < 2e-16 ***
## PriceRelative -457.8 131.9 -3.472 0.000965 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 490.3 on 60 degrees of freedom
## Multiple R-squared: 0.1673, Adjusted R-squared: 0.1534
## F-statistic: 12.05 on 1 and 60 DF, p-value: 0.0009654Vrint$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1617.563 1617.563 1617.563 1617.563 1773.229 1773.229 1773.229 1773.229
## 70 71 72 73 156 157 158 159
## 1832.748 1832.748 1832.748 1906.003 1118.516 1118.516 1118.516 1118.516
## 160 161 162 163 164 165 166 167
## 1159.721 1159.721 1159.721 1319.966 1507.681 1507.681 1507.681 1507.681
## 168 169 170 171 172 173 174 175
## 1535.151 1535.151 1535.151 1535.151 1567.200 1695.396 1718.288 1718.288
## 176 177 178 179 180 181 182 183
## 1718.288 1718.288 1722.866 1727.445 1768.651 1768.651 1768.651 1768.651
## 184 191 192 193 194 195 196 197
## 1832.748 1457.318 1457.318 1457.318 1457.318 1480.210 1480.210 1480.210
## 198 199 200 201 202 203 204 205
## 1480.210 1567.200 1567.200 1567.200 1727.445 1727.445 1764.072 1764.072
## 206 207 208 209 210 211
## 1764.072 1764.072 1832.748 1906.003 1906.003 1906.003cor(Vrint$PricePremium,Vrint$PriceRelative)## [1] 0.1651901fit<-lm(PriceEconomy~PercentPremiumSeats,data = Vrint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Vrint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1262.18 -98.41 63.82 246.76 873.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 957.60 442.67 2.163 0.0345 *
## PercentPremiumSeats 41.00 27.77 1.476 0.1451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 527.8 on 60 degrees of freedom
## Multiple R-squared: 0.03505, Adjusted R-squared: 0.01896
## F-statistic: 2.179 on 1 and 60 DF, p-value: 0.1451Vrint$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179
## 70 71 72 73 156 157 158 159
## 1802.179 1802.179 1802.179 1802.179 1573.405 1573.405 1573.405 1573.405
## 160 161 162 163 164 165 166 167
## 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355 1571.355 1571.355
## 168 169 170 171 172 173 174 175
## 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355
## 176 177 178 179 180 181 182 183
## 1571.355 1571.355 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405
## 184 191 192 193 194 195 196 197
## 1573.405 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 198 199 200 201 202 203 204 205
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 206 207 208 209 210 211
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406cor(Vrint$PricePremium,Vrint$PercentPremiumSeats)## [1] -0.03284634Now It’s time for comparison-
par(mfrow=c(1, 2))
main="Boeing vs AirBus"
library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Vrboeing$PriceEconomy,Vrboeing$TravelMonth,mean))
y2<-c(by(Vrboeing$PricePremium,Vrboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Vrairbus$PriceEconomy,Vrairbus$TravelMonth,mean))
y4<-c(by(Vrairbus$PricePremium,Vrairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(main="mean prices of economy & premium tickets in Boeing",data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Boeing)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'plot_ly(main="mean prices of economy & premium tickets in Airbus"
,data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Airbus)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'short Analysis of Virgin Airlines
mean(Virgin$PriceEconomy)## [1] 1603.532mean(Virgin$PricePremium)## [1] 2721.694library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Virgin$PriceEconomy,Virgin$TravelMonth,mean))
y2<-c(by(Virgin$PricePremium,Virgin$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1092.6 -214.9 152.2 273.4 870.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1782.82 334.46 5.330 1.56e-06 ***
## FlightDuration -19.38 35.40 -0.548 0.586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 536 on 60 degrees of freedom
## Multiple R-squared: 0.004972, Adjusted R-squared: -0.01161
## F-statistic: 0.2998 on 1 and 60 DF, p-value: 0.586Virgin$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1627.769 1627.769 1627.769 1627.769 1611.682 1611.682 1611.682 1611.682
## 70 71 72 73 156 157 158 159
## 1645.600 1645.600 1645.600 1632.614 1564.778 1564.778 1564.778 1564.778
## 160 161 162 163 164 165 166 167
## 1548.691 1548.691 1548.691 1548.691 1590.750 1590.750 1590.750 1590.750
## 168 169 170 171 172 173 174 175
## 1572.918 1572.918 1572.918 1572.918 1581.059 1539.000 1574.469 1574.469
## 176 177 178 179 180 181 182 183
## 1574.469 1574.469 1539.000 1563.227 1634.359 1634.359 1634.359 1634.359
## 184 191 192 193 194 195 196 197
## 1539.000 1648.895 1648.895 1648.895 1648.895 1655.291 1655.291 1655.291
## 198 199 200 201 202 203 204 205
## 1655.291 1581.059 1581.059 1581.059 1563.227 1563.227 1639.204 1639.204
## 206 207 208 209 210 211
## 1639.204 1639.204 1645.600 1632.614 1632.614 1632.614cor(Virgin$PriceEconomy,Virgin$FlightDuration)## [1] -0.0705092fit<-lm(PriceEconomy~SeatsEconomy,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1072.2 -265.0 114.8 347.5 863.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 900.280 259.407 3.471 0.000969 ***
## SeatsEconomy 3.055 1.092 2.798 0.006903 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 505.4 on 60 degrees of freedom
## Multiple R-squared: 0.1154, Adjusted R-squared: 0.1007
## F-statistic: 7.829 on 1 and 60 DF, p-value: 0.006903Virgin$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503
## 70 71 72 73 156 157 158 159
## 1465.503 1465.503 1465.503 1465.503 1505.222 1505.222 1505.222 1505.222
## 160 161 162 163 164 165 166 167
## 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003 2046.003 2046.003
## 168 169 170 171 172 173 174 175
## 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003
## 176 177 178 179 180 181 182 183
## 2046.003 2046.003 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222
## 184 191 192 193 194 195 196 197
## 1505.222 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 198 199 200 201 202 203 204 205
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 206 207 208 209 210 211
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156cor(Virgin$PriceEconomy,Virgin$SeatsEconomy)## [1] 0.3397343fit<-lm(PriceEconomy~PriceRelative,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1366.00 -23.32 43.60 273.32 726.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1951.8 118.1 16.530 < 2e-16 ***
## PriceRelative -457.8 131.9 -3.472 0.000965 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 490.3 on 60 degrees of freedom
## Multiple R-squared: 0.1673, Adjusted R-squared: 0.1534
## F-statistic: 12.05 on 1 and 60 DF, p-value: 0.0009654Virgin$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1617.563 1617.563 1617.563 1617.563 1773.229 1773.229 1773.229 1773.229
## 70 71 72 73 156 157 158 159
## 1832.748 1832.748 1832.748 1906.003 1118.516 1118.516 1118.516 1118.516
## 160 161 162 163 164 165 166 167
## 1159.721 1159.721 1159.721 1319.966 1507.681 1507.681 1507.681 1507.681
## 168 169 170 171 172 173 174 175
## 1535.151 1535.151 1535.151 1535.151 1567.200 1695.396 1718.288 1718.288
## 176 177 178 179 180 181 182 183
## 1718.288 1718.288 1722.866 1727.445 1768.651 1768.651 1768.651 1768.651
## 184 191 192 193 194 195 196 197
## 1832.748 1457.318 1457.318 1457.318 1457.318 1480.210 1480.210 1480.210
## 198 199 200 201 202 203 204 205
## 1480.210 1567.200 1567.200 1567.200 1727.445 1727.445 1764.072 1764.072
## 206 207 208 209 210 211
## 1764.072 1764.072 1832.748 1906.003 1906.003 1906.003cor(Virgin$PriceEconomy,Virgin$PriceRelative)## [1] -0.4089837fit<-lm(PriceEconomy~PercentPremiumSeats,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1262.18 -98.41 63.82 246.76 873.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 957.60 442.67 2.163 0.0345 *
## PercentPremiumSeats 41.00 27.77 1.476 0.1451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 527.8 on 60 degrees of freedom
## Multiple R-squared: 0.03505, Adjusted R-squared: 0.01896
## F-statistic: 2.179 on 1 and 60 DF, p-value: 0.1451Virgin$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 574 574
## [15] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [29] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1434
## [43] 1434 1434 1434 1476 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767
## [57] 1767 1767 1919 540 540 540fitted(fit)## 62 63 64 65 66 67 68 69
## 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179
## 70 71 72 73 156 157 158 159
## 1802.179 1802.179 1802.179 1802.179 1573.405 1573.405 1573.405 1573.405
## 160 161 162 163 164 165 166 167
## 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355 1571.355 1571.355
## 168 169 170 171 172 173 174 175
## 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355
## 176 177 178 179 180 181 182 183
## 1571.355 1571.355 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405
## 184 191 192 193 194 195 196 197
## 1573.405 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 198 199 200 201 202 203 204 205
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 206 207 208 209 210 211
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406cor(Virgin$PriceEconomy,Virgin$PercentPremiumSeats)## [1] 0.1872078fit<-lm(PricePremium~FlightDuration,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2047.5 -124.4 184.8 473.1 892.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2227.50 505.17 4.409 4.37e-05 ***
## FlightDuration 53.42 53.47 0.999 0.322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 809.6 on 60 degrees of freedom
## Multiple R-squared: 0.01637, Adjusted R-squared: -2.707e-05
## F-statistic: 0.9983 on 1 and 60 DF, p-value: 0.3217Virgin$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 2654.889 2654.889 2654.889 2654.889 2699.230 2699.230 2699.230 2699.230
## 70 71 72 73 156 157 158 159
## 2605.739 2605.739 2605.739 2641.533 2828.514 2828.514 2828.514 2828.514
## 160 161 162 163 164 165 166 167
## 2872.856 2872.856 2872.856 2872.856 2756.927 2756.927 2756.927 2756.927
## 168 169 170 171 172 173 174 175
## 2806.076 2806.076 2806.076 2806.076 2783.639 2899.567 2801.803 2801.803
## 176 177 178 179 180 181 182 183
## 2801.803 2801.803 2899.567 2832.788 2636.725 2636.725 2636.725 2636.725
## 184 191 192 193 194 195 196 197
## 2899.567 2596.657 2596.657 2596.657 2596.657 2579.028 2579.028 2579.028
## 198 199 200 201 202 203 204 205
## 2579.028 2783.639 2783.639 2783.639 2832.788 2832.788 2623.369 2623.369
## 206 207 208 209 210 211
## 2623.369 2623.369 2605.739 2641.533 2641.533 2641.533cor(Virgin$PricePremium,Virgin$FlightDuration)## [1] 0.1279329fit<-lm(PriceEconomy~SeatsEconomy,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1072.2 -265.0 114.8 347.5 863.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 900.280 259.407 3.471 0.000969 ***
## SeatsEconomy 3.055 1.092 2.798 0.006903 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 505.4 on 60 degrees of freedom
## Multiple R-squared: 0.1154, Adjusted R-squared: 0.1007
## F-statistic: 7.829 on 1 and 60 DF, p-value: 0.006903Virgin$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503 1465.503
## 70 71 72 73 156 157 158 159
## 1465.503 1465.503 1465.503 1465.503 1505.222 1505.222 1505.222 1505.222
## 160 161 162 163 164 165 166 167
## 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003 2046.003 2046.003
## 168 169 170 171 172 173 174 175
## 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222 2046.003 2046.003
## 176 177 178 179 180 181 182 183
## 2046.003 2046.003 1505.222 1505.222 1505.222 1505.222 1505.222 1505.222
## 184 191 192 193 194 195 196 197
## 1505.222 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 198 199 200 201 202 203 204 205
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156
## 206 207 208 209 210 211
## 1612.156 1612.156 1612.156 1612.156 1612.156 1612.156cor(Virgin$PricePremium,Virgin$SeatsEconomy)## [1] 0.4124598fit<-lm(PriceEconomy~SeatsPremium,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1197.2 -331.0 118.2 304.7 949.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 564.120 259.600 2.173 0.033737 *
## SeatsPremium 24.438 5.937 4.116 0.000119 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 474.5 on 60 degrees of freedom
## Multiple R-squared: 0.2202, Adjusted R-squared: 0.2072
## F-statistic: 16.94 on 1 and 60 DF, p-value: 0.0001194Virgin$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1737.154 1737.154 1737.154 1737.154 1737.154 1737.154 1737.154 1737.154
## 70 71 72 73 156 157 158 159
## 1737.154 1737.154 1737.154 1737.154 1419.457 1419.457 1419.457 1419.457
## 160 161 162 163 164 165 166 167
## 1419.457 1419.457 1419.457 1419.457 2177.042 2177.042 2177.042 2177.042
## 168 169 170 171 172 173 174 175
## 1419.457 1419.457 1419.457 1419.457 1419.457 1419.457 2177.042 2177.042
## 176 177 178 179 180 181 182 183
## 2177.042 2177.042 1419.457 1419.457 1419.457 1419.457 1419.457 1419.457
## 184 191 192 193 194 195 196 197
## 1419.457 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772
## 198 199 200 201 202 203 204 205
## 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772
## 206 207 208 209 210 211
## 1492.772 1492.772 1492.772 1492.772 1492.772 1492.772cor(Virgin$PricePremium,Virgin$SeatsPremium)## [1] 0.3999636fit<-lm(PriceEconomy~PriceRelative,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1366.00 -23.32 43.60 273.32 726.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1951.8 118.1 16.530 < 2e-16 ***
## PriceRelative -457.8 131.9 -3.472 0.000965 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 490.3 on 60 degrees of freedom
## Multiple R-squared: 0.1673, Adjusted R-squared: 0.1534
## F-statistic: 12.05 on 1 and 60 DF, p-value: 0.0009654Virgin$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1617.563 1617.563 1617.563 1617.563 1773.229 1773.229 1773.229 1773.229
## 70 71 72 73 156 157 158 159
## 1832.748 1832.748 1832.748 1906.003 1118.516 1118.516 1118.516 1118.516
## 160 161 162 163 164 165 166 167
## 1159.721 1159.721 1159.721 1319.966 1507.681 1507.681 1507.681 1507.681
## 168 169 170 171 172 173 174 175
## 1535.151 1535.151 1535.151 1535.151 1567.200 1695.396 1718.288 1718.288
## 176 177 178 179 180 181 182 183
## 1718.288 1718.288 1722.866 1727.445 1768.651 1768.651 1768.651 1768.651
## 184 191 192 193 194 195 196 197
## 1832.748 1457.318 1457.318 1457.318 1457.318 1480.210 1480.210 1480.210
## 198 199 200 201 202 203 204 205
## 1480.210 1567.200 1567.200 1567.200 1727.445 1727.445 1764.072 1764.072
## 206 207 208 209 210 211
## 1764.072 1764.072 1832.748 1906.003 1906.003 1906.003cor(Virgin$PricePremium,Virgin$PriceRelative)## [1] 0.1651901fit<-lm(PriceEconomy~PercentPremiumSeats,data = Virgin)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Virgin)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1262.18 -98.41 63.82 246.76 873.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 957.60 442.67 2.163 0.0345 *
## PercentPremiumSeats 41.00 27.77 1.476 0.1451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 527.8 on 60 degrees of freedom
## Multiple R-squared: 0.03505, Adjusted R-squared: 0.01896
## F-statistic: 2.179 on 1 and 60 DF, p-value: 0.1451Virgin$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 1619 1619
## [15] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [29] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 2982
## [43] 2982 2982 2982 2997 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499
## [57] 2499 2499 2409 594 594 594fitted(fit)## 62 63 64 65 66 67 68 69
## 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179 1802.179
## 70 71 72 73 156 157 158 159
## 1802.179 1802.179 1802.179 1802.179 1573.405 1573.405 1573.405 1573.405
## 160 161 162 163 164 165 166 167
## 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355 1571.355 1571.355
## 168 169 170 171 172 173 174 175
## 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405 1571.355 1571.355
## 176 177 178 179 180 181 182 183
## 1571.355 1571.355 1573.405 1573.405 1573.405 1573.405 1573.405 1573.405
## 184 191 192 193 194 195 196 197
## 1573.405 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 198 199 200 201 202 203 204 205
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406
## 206 207 208 209 210 211
## 1532.406 1532.406 1532.406 1532.406 1532.406 1532.406cor(Virgin$PricePremium,Virgin$PercentPremiumSeats)## [1] -0.03284634Delta Airlines
Analyse all about Delta Airlines:-
Delta <- airline[ which(airline$Airline=='Delta'),]
View(Delta)
summary(Delta)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:12 Min. :1.570 Aug:12
## British : 0 Boeing:34 1st Qu.:2.270 Jul:10
## Delta :46 Median :4.260 Oct:13
## Jet : 0 Mean :4.029 Sep:11
## Singapore: 0 3rd Qu.:4.645
## Virgin : 0 Max. :9.500
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic :40 Min. : 78.0 Min. :18.00 Min. :31.00
## International: 6 1st Qu.:120.0 1st Qu.:18.00 1st Qu.:31.00
## Median :126.0 Median :20.00 Median :32.00
## Mean :137.2 Mean :22.57 Mean :31.72
## 3rd Qu.:139.0 3rd Qu.:21.00 3rd Qu.:32.00
## Max. :233.0 Max. :38.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 158.0
## 1st Qu.:34.00 1st Qu.:17.00 1st Qu.:17.00 1st Qu.: 293.0
## Median :34.00 Median :17.00 Median :17.00 Median : 363.0
## Mean :34.72 Mean :17.39 Mean :17.78 Mean : 560.9
## 3rd Qu.:35.00 3rd Qu.:18.00 3rd Qu.:18.00 3rd Qu.: 449.2
## Max. :38.00 Max. :18.00 Max. :21.00 Max. :1999.0
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 173.0 Min. :0.0300 Min. : 98.0 Min. :2
## 1st Qu.: 312.8 1st Qu.:0.0700 1st Qu.:138.0 1st Qu.:2
## Median : 406.5 Median :0.0900 Median :144.0 Median :2
## Mean : 684.7 Mean :0.1250 Mean :159.8 Mean :3
## 3rd Qu.: 489.5 3rd Qu.:0.1175 3rd Qu.:160.0 3rd Qu.:3
## Max. :2765.0 Max. :0.4600 Max. :271.0 Max. :7
## WidthDifference PercentPremiumSeats
## Min. :0.0000 Min. :12.50
## 1st Qu.:0.0000 1st Qu.:12.50
## Median :0.0000 Median :13.09
## Mean :0.3913 Mean :14.48
## 3rd Qu.:0.0000 3rd Qu.:14.50
## Max. :3.0000 Max. :20.41Check the all the means now all Delta aircrafts
mean(Delta$PriceEconomy)## [1] 560.9348mean(Delta$PricePremium)## [1] 684.6739mean(Delta$FlightDuration)## [1] 4.028913mean(Delta$PitchEconomy)## [1] 31.71739mean(Delta$PitchPremium)## [1] 34.71739mean(Delta$WidthEconomy)## [1] 17.3913mean(Delta$WidthPremium)## [1] 17.78261mean(Delta$PriceRelative)## [1] 0.125mean(Delta$PitchDifference)## [1] 3mean(Delta$WidthDifference)## [1] 0.3913043Now Analyse separately for Each Aircrafts in Delta Airlines i.e-Boeing and AirBus
Deboeing <- Delta[ which(Delta$Aircraft=='Boeing'),]
View(Deboeing)
summary(Deboeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 0 Min. :1.570 Aug:10
## British : 0 Boeing:34 1st Qu.:2.308 Jul:10
## Delta :34 Median :4.260 Oct: 8
## Jet : 0 Mean :3.519 Sep: 6
## Singapore: 0 3rd Qu.:4.510
## Virgin : 0 Max. :4.700
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic :34 Min. : 78.0 Min. :18.00 Min. :31.00
## International: 0 1st Qu.:126.0 1st Qu.:18.00 1st Qu.:31.00
## Median :126.0 Median :20.00 Median :32.00
## Mean :122.9 Mean :20.59 Mean :31.76
## 3rd Qu.:136.0 3rd Qu.:20.75 3rd Qu.:32.00
## Max. :171.0 Max. :29.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. :158.0
## 1st Qu.:34.00 1st Qu.:17.00 1st Qu.:17.00 1st Qu.:289.2
## Median :34.00 Median :17.00 Median :17.00 Median :358.5
## Mean :34.24 Mean :17.35 Mean :17.35 Mean :368.4
## 3rd Qu.:34.00 3rd Qu.:18.00 3rd Qu.:18.00 3rd Qu.:413.0
## Max. :35.00 Max. :18.00 Max. :18.00 Max. :713.0
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. :173.0 Min. :0.03000 Min. : 98.0 Min. :2.000
## 1st Qu.:327.0 1st Qu.:0.07000 1st Qu.:144.0 1st Qu.:2.000
## Median :392.5 Median :0.09000 Median :144.0 Median :2.000
## Mean :399.8 Mean :0.08824 Mean :143.5 Mean :2.471
## 3rd Qu.:457.0 3rd Qu.:0.11000 3rd Qu.:157.5 3rd Qu.:3.000
## Max. :757.0 Max. :0.14000 Max. :200.0 Max. :3.000
## WidthDifference PercentPremiumSeats
## Min. :0 Min. :12.50
## 1st Qu.:0 1st Qu.:12.50
## Median :0 Median :12.97
## Mean :0 Mean :14.82
## 3rd Qu.:0 3rd Qu.:15.97
## Max. :0 Max. :20.41mean(Deboeing$PriceEconomy)## [1] 368.3824mean(Deboeing$PricePremium)## [1] 399.7647library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Deboeing$PriceEconomy,Deboeing$TravelMonth,mean))
y2<-c(by(Deboeing$PricePremium,Deboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -146.248 -79.818 -7.311 29.296 312.408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 213.68 65.90 3.242 0.00277 **
## FlightDuration 43.96 17.76 2.475 0.01881 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.8 on 32 degrees of freedom
## Multiple R-squared: 0.1607, Adjusted R-squared: 0.1344
## F-statistic: 6.126 on 1 and 32 DF, p-value: 0.01881Deboeing$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 243 626 354 293 636 349fitted(fit)## 74 75 76 77 78 79 80 81
## 304.2478 316.1179 304.2478 302.0497 316.1179 290.6192 314.7990 314.7990
## 98 152 153 154 155 281 282 283
## 400.9668 404.0443 411.9576 404.0443 411.9576 401.8461 400.5272 417.2332
## 284 285 286 287 288 289 290 291
## 418.1125 418.5521 420.3106 420.3106 420.3106 405.3632 400.5272 408.4406
## 292 293 294 295 299 300 301 304
## 420.3106 400.9668 407.1217 297.6533 325.7898 417.2332 339.4184 282.7059
## 305 307
## 323.5916 282.7059cor(Deboeing$PriceEconomy,Deboeing$FlightDuration)## [1] 0.4008461fit<-lm(PriceEconomy~SeatsEconomy,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -227.18 -66.05 -18.27 35.08 297.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 136.1214 92.2911 1.475 0.1500
## SeatsEconomy 1.8901 0.7318 2.583 0.0146 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 120.9 on 32 degrees of freedom
## Multiple R-squared: 0.1725, Adjusted R-squared: 0.1466
## F-statistic: 6.67 on 1 and 32 DF, p-value: 0.01458Deboeing$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 243 626 354 293 636 349fitted(fit)## 74 75 76 77 78 79 80 81
## 283.5498 283.5498 283.5498 283.5498 283.5498 283.5498 283.5498 283.5498
## 98 152 153 154 155 281 282 283
## 385.6157 459.3299 459.3299 459.3299 459.3299 374.2750 374.2750 374.2750
## 284 285 286 287 288 289 290 291
## 374.2750 374.2750 398.8465 398.8465 398.8465 374.2750 374.2750 374.2750
## 292 293 294 295 299 300 301 304
## 398.8465 374.2750 374.2750 393.1761 393.1761 374.2750 393.1761 374.2750
## 305 307
## 393.1761 374.2750cor(Deboeing$PriceEconomy,Deboeing$SeatsEconomy)## [1] 0.4153204fit<-lm(PriceEconomy~PriceRelative,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -208.47 -71.68 -14.52 68.22 314.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 464.05 68.88 6.737 1.31e-07 ***
## PriceRelative -1084.27 739.47 -1.466 0.152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.7 on 32 degrees of freedom
## Multiple R-squared: 0.06296, Adjusted R-squared: 0.03368
## F-statistic: 2.15 on 1 and 32 DF, p-value: 0.1523Deboeing$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 243 626 354 293 636 349fitted(fit)## 74 75 76 77 78 79 80 81
## 366.4689 377.3117 388.1544 388.1544 388.1544 420.6825 420.6825 431.5252
## 98 152 153 154 155 281 282 283
## 366.4689 323.0981 355.6262 366.4689 398.9971 312.2553 312.2553 333.9408
## 284 285 286 287 288 289 290 291
## 333.9408 333.9408 344.7835 344.7835 344.7835 344.7835 344.7835 344.7835
## 292 293 294 295 299 300 301 304
## 355.6262 355.6262 355.6262 366.4689 377.3117 388.1544 388.1544 409.8398
## 305 307
## 420.6825 420.6825cor(Deboeing$PriceEconomy,Deboeing$PriceRelative)## [1] -0.2509141fit<-lm(PriceEconomy~PercentPremiumSeats,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -222.47 -88.91 -28.68 37.37 341.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 516.959 104.322 4.955 2.26e-05 ***
## PercentPremiumSeats -10.022 6.878 -1.457 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.8 on 32 degrees of freedom
## Multiple R-squared: 0.06223, Adjusted R-squared: 0.03293
## F-statistic: 2.124 on 1 and 32 DF, p-value: 0.1548Deboeing$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 243 626 354 293 636 349fitted(fit)## 74 75 76 77 78 79 80 81
## 312.4052 312.4052 312.4052 312.4052 312.4052 312.4052 312.4052 312.4052
## 98 152 153 154 155 281 282 283
## 351.9931 371.6366 371.6366 371.6366 371.6366 391.6811 391.6811 391.6811
## 284 285 286 287 288 289 290 291
## 391.6811 391.6811 385.3671 385.3671 385.3671 391.6811 391.6811 391.6811
## 292 293 294 295 299 300 301 304
## 385.3671 391.6811 391.6811 388.4740 388.4740 391.6811 388.4740 391.6811
## 305 307
## 388.4740 391.6811cor(Deboeing$PriceEconomy,Deboeing$PercentPremiumSeats)## [1] -0.2494613fit<-lm(PricePremium~FlightDuration,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -147.10 -83.73 -7.27 27.42 315.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 207.60 66.49 3.122 0.00379 **
## FlightDuration 54.61 17.92 3.047 0.00460 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 122.9 on 32 degrees of freedom
## Multiple R-squared: 0.2249, Adjusted R-squared: 0.2007
## F-statistic: 9.287 on 1 and 32 DF, p-value: 0.0046Deboeing$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 262 670 378 308 660 364fitted(fit)## 74 75 76 77 78 79 80 81
## 320.0965 334.8415 320.0965 317.3659 334.8415 303.1669 333.2032 333.2032
## 98 152 153 154 155 281 282 283
## 440.2413 444.0641 453.8941 444.0641 453.8941 441.3335 439.6952 460.4475
## 284 285 286 287 288 289 290 291
## 461.5397 462.0858 464.2703 464.2703 464.2703 445.7024 439.6952 449.5252
## 292 293 294 295 299 300 301 304
## 464.2703 440.2413 447.8869 311.9048 346.8560 460.4475 363.7855 293.3369
## 305 307
## 344.1254 293.3369cor(Deboeing$PricePremium,Deboeing$FlightDuration)## [1] 0.474279fit<-lm(PriceEconomy~SeatsEconomy,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -227.18 -66.05 -18.27 35.08 297.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 136.1214 92.2911 1.475 0.1500
## SeatsEconomy 1.8901 0.7318 2.583 0.0146 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 120.9 on 32 degrees of freedom
## Multiple R-squared: 0.1725, Adjusted R-squared: 0.1466
## F-statistic: 6.67 on 1 and 32 DF, p-value: 0.01458Deboeing$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 262 670 378 308 660 364fitted(fit)## 74 75 76 77 78 79 80 81
## 283.5498 283.5498 283.5498 283.5498 283.5498 283.5498 283.5498 283.5498
## 98 152 153 154 155 281 282 283
## 385.6157 459.3299 459.3299 459.3299 459.3299 374.2750 374.2750 374.2750
## 284 285 286 287 288 289 290 291
## 374.2750 374.2750 398.8465 398.8465 398.8465 374.2750 374.2750 374.2750
## 292 293 294 295 299 300 301 304
## 398.8465 374.2750 374.2750 393.1761 393.1761 374.2750 393.1761 374.2750
## 305 307
## 393.1761 374.2750cor(Deboeing$PricePremium,Deboeing$SeatsEconomy)## [1] 0.4615171fit<-lm(PriceEconomy~SeatsPremium,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -203.419 -49.743 6.757 39.743 288.257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 124.658 130.952 0.952 0.3483
## SeatsPremium 11.838 6.273 1.887 0.0682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.1 on 32 degrees of freedom
## Multiple R-squared: 0.1001, Adjusted R-squared: 0.07202
## F-statistic: 3.561 on 1 and 32 DF, p-value: 0.06825Deboeing$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 262 670 378 308 660 364fitted(fit)## 74 75 76 77 78 79 80 81
## 361.4188 361.4188 361.4188 361.4188 361.4188 361.4188 361.4188 361.4188
## 98 152 153 154 155 281 282 283
## 432.4470 467.9611 467.9611 467.9611 467.9611 337.7427 337.7427 337.7427
## 284 285 286 287 288 289 290 291
## 337.7427 337.7427 373.2568 373.2568 373.2568 337.7427 337.7427 337.7427
## 292 293 294 295 299 300 301 304
## 373.2568 337.7427 337.7427 361.4188 361.4188 337.7427 361.4188 337.7427
## 305 307
## 361.4188 337.7427cor(Deboeing$PricePremium,Deboeing$SeatsPremium)## [1] 0.3243034fit<-lm(PriceEconomy~PriceRelative,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -208.47 -71.68 -14.52 68.22 314.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 464.05 68.88 6.737 1.31e-07 ***
## PriceRelative -1084.27 739.47 -1.466 0.152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.7 on 32 degrees of freedom
## Multiple R-squared: 0.06296, Adjusted R-squared: 0.03368
## F-statistic: 2.15 on 1 and 32 DF, p-value: 0.1523Deboeing$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 262 670 378 308 660 364fitted(fit)## 74 75 76 77 78 79 80 81
## 366.4689 377.3117 388.1544 388.1544 388.1544 420.6825 420.6825 431.5252
## 98 152 153 154 155 281 282 283
## 366.4689 323.0981 355.6262 366.4689 398.9971 312.2553 312.2553 333.9408
## 284 285 286 287 288 289 290 291
## 333.9408 333.9408 344.7835 344.7835 344.7835 344.7835 344.7835 344.7835
## 292 293 294 295 299 300 301 304
## 355.6262 355.6262 355.6262 366.4689 377.3117 388.1544 388.1544 409.8398
## 305 307
## 420.6825 420.6825cor(Deboeing$PricePremium,Deboeing$PriceRelative)## [1] -0.1722524fit<-lm(PriceEconomy~PercentPremiumSeats,data = Deboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Deboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -222.47 -88.91 -28.68 37.37 341.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 516.959 104.322 4.955 2.26e-05 ***
## PercentPremiumSeats -10.022 6.878 -1.457 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.8 on 32 degrees of freedom
## Multiple R-squared: 0.06223, Adjusted R-squared: 0.03293
## F-statistic: 2.124 on 1 and 32 DF, p-value: 0.1548Deboeing$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 262 670 378 308 660 364fitted(fit)## 74 75 76 77 78 79 80 81
## 312.4052 312.4052 312.4052 312.4052 312.4052 312.4052 312.4052 312.4052
## 98 152 153 154 155 281 282 283
## 351.9931 371.6366 371.6366 371.6366 371.6366 391.6811 391.6811 391.6811
## 284 285 286 287 288 289 290 291
## 391.6811 391.6811 385.3671 385.3671 385.3671 391.6811 391.6811 391.6811
## 292 293 294 295 299 300 301 304
## 385.3671 391.6811 391.6811 388.4740 388.4740 391.6811 388.4740 391.6811
## 305 307
## 388.4740 391.6811cor(Deboeing$PricePremium,Deboeing$PercentPremiumSeats)## [1] -0.2983363Deairbus <-Delta[ which(Delta$Aircraft=='AirBus'),]
View(Deairbus)
summary(Deairbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:12 Min. :1.800 Aug:2
## British : 0 Boeing: 0 1st Qu.:1.920 Jul:0
## Delta :12 Median :5.440 Oct:5
## Jet : 0 Mean :5.474 Sep:5
## Singapore: 0 3rd Qu.:8.623
## Virgin : 0 Max. :9.500
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic :6 Min. :120.0 Min. :18.00 Min. :31.00
## International:6 1st Qu.:120.0 1st Qu.:18.00 1st Qu.:31.00
## Median :184.5 Median :29.00 Median :31.50
## Mean :177.8 Mean :28.17 Mean :31.58
## 3rd Qu.:233.0 3rd Qu.:38.00 3rd Qu.:32.00
## Max. :233.0 Max. :38.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.0 Min. :17 Min. : 166
## 1st Qu.:34.00 1st Qu.:17.0 1st Qu.:17 1st Qu.: 293
## Median :36.50 Median :17.5 Median :19 Median :1097
## Mean :36.08 Mean :17.5 Mean :19 Mean :1106
## 3rd Qu.:38.00 3rd Qu.:18.0 3rd Qu.:21 3rd Qu.:1988
## Max. :38.00 Max. :18.0 Max. :21 Max. :1999
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 181 Min. :0.0400 Min. :138.0 Min. :2.0
## 1st Qu.: 308 1st Qu.:0.0725 1st Qu.:138.0 1st Qu.:2.0
## Median :1510 Median :0.1950 Median :213.5 Median :4.5
## Mean :1492 Mean :0.2292 Mean :206.0 Mean :4.5
## 3rd Qu.:2632 3rd Qu.:0.3800 3rd Qu.:271.0 3rd Qu.:7.0
## Max. :2765 Max. :0.4600 Max. :271.0 Max. :7.0
## WidthDifference PercentPremiumSeats
## Min. :0.0 Min. :12.82
## 1st Qu.:0.0 1st Qu.:13.04
## Median :1.5 Median :13.53
## Mean :1.5 Mean :13.51
## 3rd Qu.:3.0 3rd Qu.:14.02
## Max. :3.0 Max. :14.02mean(Deairbus$PriceEconomy)## [1] 1106.5mean(Deairbus$PricePremium)## [1] 1491.917library(plotly)
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Deairbus$PriceEconomy,Deairbus$TravelMonth,mean))
y4<-c(by(Deairbus$PricePremium,Deairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')## Warning: Ignoring 1 observations
## Warning: Ignoring 1 observationsfit<-lm(PriceEconomy~FlightDuration,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -177.05 -52.57 -10.94 43.15 208.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -178.571 61.416 -2.908 0.0156 *
## FlightDuration 234.752 9.475 24.776 2.62e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 113.9 on 10 degrees of freedom
## Multiple R-squared: 0.984, Adjusted R-squared: 0.9824
## F-statistic: 613.9 on 1 and 10 DF, p-value: 2.622e-10Deairbus$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985 166 329 243 293 293 416fitted(fit)## 185 186 187 188 189 190 296
## 1776.9123 1776.9123 2051.5720 2051.5720 2051.5720 1776.9123 279.1952
## 297 298 302 303 306
## 351.9683 420.0463 246.3299 251.0249 243.9824cor(Deairbus$PriceEconomy,Deairbus$FlightDuration)## [1] 0.9919529fit<-lm(PriceEconomy~SeatsEconomy,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -248.79 -102.80 52.09 81.87 159.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1506.6145 125.4989 -12.01 2.91e-07 ***
## SeatsEconomy 14.6942 0.6739 21.81 9.20e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 129.2 on 10 degrees of freedom
## Multiple R-squared: 0.9794, Adjusted R-squared: 0.9773
## F-statistic: 475.5 on 1 and 10 DF, p-value: 9.201e-10Deairbus$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985 166 329 243 293 293 416fitted(fit)## 185 186 187 188 189 190 296
## 1917.1288 1917.1288 1917.1288 1917.1288 1917.1288 1917.1288 256.6868
## 297 298 302 303 306
## 256.6868 491.7936 256.6868 256.6868 256.6868cor(Deairbus$PriceEconomy,Deairbus$SeatsEconomy)## [1] 0.989648fit<-lm(PriceEconomy~PriceRelative,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -385.57 -211.11 6.97 201.78 554.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.07 145.04 0.393 0.702
## PriceRelative 4579.35 509.14 8.994 4.16e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 298.5 on 10 degrees of freedom
## Multiple R-squared: 0.89, Adjusted R-squared: 0.879
## F-statistic: 80.9 on 1 and 10 DF, p-value: 4.161e-06Deairbus$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985 166 329 243 293 293 416fitted(fit)## 185 186 187 188 189 190 296
## 2163.5670 2163.5670 1797.2189 1797.2189 1797.2189 1430.8707 469.2069
## 297 298 302 303 306
## 423.4134 423.4134 286.0328 286.0328 240.2393cor(Deairbus$PriceEconomy,Deairbus$PriceRelative)## [1] 0.9433913fit<-lm(PriceEconomy~PercentPremiumSeats,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -191.07 -82.10 15.43 84.81 235.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -20362.15 989.20 -20.58 1.62e-09 ***
## PercentPremiumSeats 1588.90 73.16 21.72 9.57e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 129.7 on 10 degrees of freedom
## Multiple R-squared: 0.9792, Adjusted R-squared: 0.9772
## F-statistic: 471.7 on 1 and 10 DF, p-value: 9.573e-10Deairbus$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985 166 329 243 293 293 416fitted(fit)## 185 186 187 188 189 190
## 1914.189261 1914.189261 1914.189261 1914.189261 1914.189261 1914.189261
## 296 297 298 302 303 306
## 357.070292 357.070292 7.512973 357.070292 357.070292 357.070292fit<-lm(PricePremium~FlightDuration,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -235.136 -96.476 5.661 124.549 189.009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -370.36 73.72 -5.024 0.000519 ***
## FlightDuration 340.19 11.37 29.912 4.08e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 136.8 on 10 degrees of freedom
## Multiple R-squared: 0.9889, Adjusted R-squared: 0.9878
## F-statistic: 894.7 on 1 and 10 DF, p-value: 4.079e-11Deairbus$PricePremium## [1] 2588 2588 2765 2765 2765 2588 181 354 262 308 308 431fitted(fit)## 185 186 187 188 189 190 296
## 2463.4507 2463.4507 2861.4764 2861.4764 2861.4764 2463.4507 293.0204
## 297 298 302 303 306
## 398.4802 497.1361 245.3934 252.1972 241.9914cor(Deairbus$PricePremium,Deairbus$FlightDuration)## [1] 0.9944581fit<-lm(PricePremium~SeatsEconomy,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -337.68 -80.53 49.57 96.47 172.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2300.9765 141.2277 -16.29 1.58e-08 ***
## SeatsEconomy 21.3284 0.7583 28.13 7.50e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 145.3 on 10 degrees of freedom
## Multiple R-squared: 0.9875, Adjusted R-squared: 0.9863
## F-statistic: 791.1 on 1 and 10 DF, p-value: 7.498e-11Deairbus$PricePremium## [1] 2588 2588 2765 2765 2765 2588 181 354 262 308 308 431fitted(fit)## 185 186 187 188 189 190 296
## 2668.5311 2668.5311 2668.5311 2668.5311 2668.5311 2668.5311 258.4266
## 297 298 302 303 306
## 258.4266 599.6803 258.4266 258.4266 258.4266cor(Deairbus$PricePremium,Deairbus$SeatsEconomy)## [1] 0.9937389fit<-lm(PricePremium~SeatsPremium,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -249.56 -84.34 36.52 92.66 159.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1889.30 108.02 -17.49 7.92e-09 ***
## SeatsPremium 120.04 3.62 33.16 1.47e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 123.5 on 10 degrees of freedom
## Multiple R-squared: 0.991, Adjusted R-squared: 0.9901
## F-statistic: 1100 on 1 and 10 DF, p-value: 1.468e-11Deairbus$PricePremium## [1] 2588 2588 2765 2765 2765 2588 181 354 262 308 308 431fitted(fit)## 185 186 187 188 189 190 296
## 2672.3406 2672.3406 2672.3406 2672.3406 2672.3406 2672.3406 271.4784
## 297 298 302 303 306
## 271.4784 511.5646 271.4784 271.4784 271.4784cor(Deairbus$PricePremium,Deairbus$SeatsPremium)## [1] 0.9954839fit<-lm(PricePremium~PriceRelative,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -462.30 -260.00 25.66 254.79 617.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -55.21 172.27 -0.32 0.755
## PriceRelative 6751.11 604.70 11.16 5.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 354.5 on 10 degrees of freedom
## Multiple R-squared: 0.9257, Adjusted R-squared: 0.9183
## F-statistic: 124.6 on 1 and 10 DF, p-value: 5.742e-07Deairbus$PricePremium## [1] 2588 2588 2765 2765 2765 2588 181 354 262 308 308 431fitted(fit)## 185 186 187 188 189 190 296
## 3050.2972 3050.2972 2510.2086 2510.2086 2510.2086 1970.1201 552.3876
## 297 298 302 303 306
## 484.8766 484.8766 282.3434 282.3434 214.8323cor(Deairbus$PricePremium,Deairbus$PriceRelative)## [1] 0.9621487fit<-lm(PricePremium~PercentPremiumSeats,data = Deairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = Deairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -224.39 -80.53 -63.15 102.10 363.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29633.30 1212.96 -24.43 3.01e-10 ***
## PercentPremiumSeats 2303.58 89.71 25.68 1.84e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 159 on 10 degrees of freedom
## Multiple R-squared: 0.9851, Adjusted R-squared: 0.9836
## F-statistic: 659.4 on 1 and 10 DF, p-value: 1.842e-10Deairbus$PricePremium## [1] 2588 2588 2765 2765 2765 2588 181 354 262 308 308 431fitted(fit)## 185 186 187 188 189 190 296
## 2662.9036 2662.9036 2662.9036 2662.9036 2662.9036 2662.9036 405.3943
## 297 298 302 303 306
## 405.3943 -101.3935 405.3943 405.3943 405.3943cor(Deairbus$PricePremium,Deairbus$PercentPremiumSeats)## [1] 0.9925027Now We Should Analyse the international & domestic aircrafts of Delta Airlines
Dedom <- Delta[ which(Delta$IsInternational=='Domestic'),]
View(Dedom)
summary(Dedom)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 6 Min. :1.570 Aug:10
## British : 0 Boeing:34 1st Qu.:2.060 Jul:10
## Delta :40 Median :3.555 Oct:11
## Jet : 0 Mean :3.296 Sep: 9
## Singapore: 0 3rd Qu.:4.450
## Virgin : 0 Max. :4.700
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic :40 Min. : 78.0 Min. :18.00 Min. :31.00
## International: 0 1st Qu.:120.0 1st Qu.:18.00 1st Qu.:31.00
## Median :126.0 Median :20.00 Median :32.00
## Mean :122.8 Mean :20.25 Mean :31.82
## 3rd Qu.:136.0 3rd Qu.:20.00 3rd Qu.:32.00
## Max. :171.0 Max. :29.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.0 Min. :17.0 Min. :158.0
## 1st Qu.:34.00 1st Qu.:17.0 1st Qu.:17.0 1st Qu.:288.0
## Median :34.00 Median :17.0 Median :17.0 Median :349.0
## Mean :34.23 Mean :17.3 Mean :17.3 Mean :356.6
## 3rd Qu.:34.00 3rd Qu.:18.0 3rd Qu.:18.0 3rd Qu.:413.0
## Max. :35.00 Max. :18.0 Max. :18.0 Max. :713.0
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. :173.0 Min. :0.03000 Min. : 98.0 Min. :2.0
## 1st Qu.:308.0 1st Qu.:0.06750 1st Qu.:138.0 1st Qu.:2.0
## Median :371.0 Median :0.09000 Median :144.0 Median :2.0
## Mean :385.9 Mean :0.08475 Mean :143.1 Mean :2.4
## 3rd Qu.:457.0 3rd Qu.:0.11000 3rd Qu.:156.0 3rd Qu.:3.0
## Max. :757.0 Max. :0.14000 Max. :200.0 Max. :3.0
## WidthDifference PercentPremiumSeats
## Min. :0 Min. :12.50
## 1st Qu.:0 1st Qu.:12.50
## Median :0 Median :13.04
## Mean :0 Mean :14.55
## 3rd Qu.:0 3rd Qu.:14.50
## Max. :0 Max. :20.41mean(Dedom$PriceEconomy)## [1] 356.625mean(Dedom$PricePremium)## [1] 385.9library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Dedom$PriceEconomy,Dedom$TravelMonth,mean))
y2<-c(by(Dedom$PricePremium,Dedom$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -143.391 -78.542 -6.366 23.225 314.946
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 209.33 53.32 3.926 0.000352 ***
## FlightDuration 44.69 15.18 2.943 0.005512 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 116.4 on 38 degrees of freedom
## Multiple R-squared: 0.1856, Adjusted R-squared: 0.1642
## F-statistic: 8.662 on 1 and 38 DF, p-value: 0.005512Dedom$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 166 329 243 243 626 354
## [35] 293 293 293 636 416 349fitted(fit)## 74 75 76 77 78 79 80 81
## 301.3910 313.4567 301.3910 299.1566 313.4567 287.5379 312.1161 312.1161
## 98 152 153 154 155 281 282 283
## 399.7039 402.8321 410.8758 402.8321 410.8758 400.5977 399.2570 416.2384
## 284 285 286 287 288 289 290 291
## 417.1321 417.5790 419.3665 419.3665 419.3665 404.1727 399.2570 407.3008
## 292 293 294 295 296 297 298 299
## 419.3665 399.7039 405.9602 294.6879 296.4754 310.3286 323.2880 323.2880
## 300 301 302 303 304 305 306 307
## 416.2384 337.1412 290.2191 291.1129 279.4941 321.0536 289.7722 279.4941cor(Dedom$PriceEconomy,Dedom$FlightDuration)## [1] 0.4308594fit<-lm(PriceEconomy~SeatsEconomy,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -214.97 -61.13 -22.40 40.56 307.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 129.198 90.064 1.435 0.1596
## SeatsEconomy 1.851 0.717 2.582 0.0138 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 119 on 38 degrees of freedom
## Multiple R-squared: 0.1493, Adjusted R-squared: 0.1269
## F-statistic: 6.667 on 1 and 38 DF, p-value: 0.0138Dedom$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 166 329 243 243 626 354
## [35] 293 293 293 636 416 349fitted(fit)## 74 75 76 77 78 79 80 81
## 273.5961 273.5961 273.5961 273.5961 273.5961 273.5961 273.5961 273.5961
## 98 152 153 154 155 281 282 283
## 373.5640 445.7630 445.7630 445.7630 445.7630 362.4565 362.4565 362.4565
## 284 285 286 287 288 289 290 291
## 362.4565 362.4565 386.5228 386.5228 386.5228 362.4565 362.4565 362.4565
## 292 293 294 295 296 297 298 299
## 386.5228 362.4565 362.4565 380.9690 351.3489 351.3489 380.9690 380.9690
## 300 301 302 303 304 305 306 307
## 362.4565 380.9690 351.3489 351.3489 362.4565 380.9690 351.3489 362.4565cor(Dedom$PriceEconomy,Dedom$SeatsEconomy)## [1] 0.3863472fit<-lm(PriceEconomy~PriceRelative,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -194.15 -93.26 -15.41 77.91 335.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 428.91 60.46 7.094 1.84e-08 ***
## PriceRelative -852.87 673.35 -1.267 0.213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.3 on 38 degrees of freedom
## Multiple R-squared: 0.04051, Adjusted R-squared: 0.01526
## F-statistic: 1.604 on 1 and 38 DF, p-value: 0.213Dedom$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 166 329 243 243 626 354
## [35] 293 293 293 636 416 349fitted(fit)## 74 75 76 77 78 79 80 81
## 352.1475 360.6761 369.2048 369.2048 369.2048 394.7908 394.7908 403.3194
## 98 152 153 154 155 281 282 283
## 352.1475 318.0328 343.6188 352.1475 377.7334 309.5042 309.5042 326.5615
## 284 285 286 287 288 289 290 291
## 326.5615 326.5615 335.0901 335.0901 335.0901 335.0901 335.0901 335.0901
## 292 293 294 295 296 297 298 299
## 343.6188 343.6188 343.6188 352.1475 352.1475 360.6761 360.6761 360.6761
## 300 301 302 303 304 305 306 307
## 369.2048 369.2048 386.2621 386.2621 386.2621 394.7908 394.7908 394.7908cor(Dedom$PriceEconomy,Dedom$PriceRelative)## [1] -0.2012646fit<-lm(PriceEconomy~PercentPremiumSeats,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -203.70 -84.11 -19.41 46.22 355.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 466.488 98.332 4.744 2.94e-05 ***
## PercentPremiumSeats -7.550 6.616 -1.141 0.261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.8 on 38 degrees of freedom
## Multiple R-squared: 0.03314, Adjusted R-squared: 0.007695
## F-statistic: 1.302 on 1 and 38 DF, p-value: 0.2609Dedom$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 288 288 363 363
## [18] 363 413 413 413 413 413 340 423 328 328 166 166 329 243 243 626 354
## [35] 293 293 293 636 416 349fitted(fit)## 74 75 76 77 78 79 80 81
## 312.3935 312.3935 312.3935 312.3935 312.3935 312.3935 312.3935 312.3935
## 98 152 153 154 155 281 282 283
## 342.2159 357.0138 357.0138 357.0138 357.0138 372.1138 372.1138 372.1138
## 284 285 286 287 288 289 290 291
## 372.1138 372.1138 367.3573 367.3573 367.3573 372.1138 372.1138 372.1138
## 292 293 294 295 296 297 298 299
## 367.3573 372.1138 372.1138 369.6978 368.0368 368.0368 369.6978 369.6978
## 300 301 302 303 304 305 306 307
## 372.1138 369.6978 368.0368 368.0368 372.1138 369.6978 368.0368 372.1138cor(Dedom$PriceEconomy,Dedom$PercentPremiumSeats)## [1] -0.1820406fit<-lm(PricePremium~FlightDuration,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -144.80 -82.80 -6.25 27.13 317.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 204.30 53.81 3.797 0.000514 ***
## FlightDuration 55.10 15.32 3.596 0.000918 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 117.5 on 38 degrees of freedom
## Multiple R-squared: 0.2538, Adjusted R-squared: 0.2342
## F-statistic: 12.93 on 1 and 38 DF, p-value: 0.0009183Dedom$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 181 354 262 262 670 378
## [35] 308 308 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80 81
## 317.8012 332.6771 317.8012 315.0464 332.6771 300.7214 331.0243 331.0243
## 98 152 153 154 155 281 282 283
## 439.0127 442.8694 452.7867 442.8694 452.7867 440.1146 438.4617 459.3982
## 284 285 286 287 288 289 290 291
## 460.5002 461.0511 463.2550 463.2550 463.2550 444.5223 438.4617 448.3790
## 292 293 294 295 296 297 298 299
## 463.2550 439.0127 446.7261 309.5368 311.7406 328.8204 344.7983 344.7983
## 300 301 302 303 304 305 306 307
## 459.3982 361.8781 304.0271 305.1291 290.8041 342.0435 303.4762 290.8041cor(Dedom$PricePremium,Dedom$FlightDuration)## [1] 0.5038332fit<-lm(PriceEconomy~SeatsEconomy,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -214.97 -61.13 -22.40 40.56 307.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 129.198 90.064 1.435 0.1596
## SeatsEconomy 1.851 0.717 2.582 0.0138 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 119 on 38 degrees of freedom
## Multiple R-squared: 0.1493, Adjusted R-squared: 0.1269
## F-statistic: 6.667 on 1 and 38 DF, p-value: 0.0138Dedom$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 181 354 262 262 670 378
## [35] 308 308 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80 81
## 273.5961 273.5961 273.5961 273.5961 273.5961 273.5961 273.5961 273.5961
## 98 152 153 154 155 281 282 283
## 373.5640 445.7630 445.7630 445.7630 445.7630 362.4565 362.4565 362.4565
## 284 285 286 287 288 289 290 291
## 362.4565 362.4565 386.5228 386.5228 386.5228 362.4565 362.4565 362.4565
## 292 293 294 295 296 297 298 299
## 386.5228 362.4565 362.4565 380.9690 351.3489 351.3489 380.9690 380.9690
## 300 301 302 303 304 305 306 307
## 362.4565 380.9690 351.3489 351.3489 362.4565 380.9690 351.3489 362.4565cor(Dedom$PricePremium,Dedom$SeatsEconomy)## [1] 0.4286017fit<-lm(PriceEconomy~SeatsPremium,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -195.400 -62.484 1.002 46.699 298.403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 95.37 119.61 0.797 0.430
## SeatsPremium 12.90 5.83 2.213 0.033 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.4 on 38 degrees of freedom
## Multiple R-squared: 0.1142, Adjusted R-squared: 0.09085
## F-statistic: 4.897 on 1 and 38 DF, p-value: 0.03298Dedom$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 181 354 262 262 670 378
## [35] 308 308 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80 81
## 353.3997 353.3997 353.3997 353.3997 353.3997 353.3997 353.3997 353.3997
## 98 152 153 154 155 281 282 283
## 430.8080 469.5121 469.5121 469.5121 469.5121 327.5969 327.5969 327.5969
## 284 285 286 287 288 289 290 291
## 327.5969 327.5969 366.3010 366.3010 366.3010 327.5969 327.5969 327.5969
## 292 293 294 295 296 297 298 299
## 366.3010 327.5969 327.5969 353.3997 327.5969 327.5969 353.3997 353.3997
## 300 301 302 303 304 305 306 307
## 327.5969 353.3997 327.5969 327.5969 327.5969 353.3997 327.5969 327.5969cor(Dedom$PricePremium,Dedom$SeatsPremium)## [1] 0.3507328fit<-lm(PriceEconomy~PriceRelative,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -194.15 -93.26 -15.41 77.91 335.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 428.91 60.46 7.094 1.84e-08 ***
## PriceRelative -852.87 673.35 -1.267 0.213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.3 on 38 degrees of freedom
## Multiple R-squared: 0.04051, Adjusted R-squared: 0.01526
## F-statistic: 1.604 on 1 and 38 DF, p-value: 0.213Dedom$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 181 354 262 262 670 378
## [35] 308 308 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80 81
## 352.1475 360.6761 369.2048 369.2048 369.2048 394.7908 394.7908 403.3194
## 98 152 153 154 155 281 282 283
## 352.1475 318.0328 343.6188 352.1475 377.7334 309.5042 309.5042 326.5615
## 284 285 286 287 288 289 290 291
## 326.5615 326.5615 335.0901 335.0901 335.0901 335.0901 335.0901 335.0901
## 292 293 294 295 296 297 298 299
## 343.6188 343.6188 343.6188 352.1475 352.1475 360.6761 360.6761 360.6761
## 300 301 302 303 304 305 306 307
## 369.2048 369.2048 386.2621 386.2621 386.2621 394.7908 394.7908 394.7908cor(Dedom$PricePremium,Dedom$PriceRelative)## [1] -0.1206376fit<-lm(PriceEconomy~PercentPremiumSeats,data = Dedom)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Dedom)
##
## Residuals:
## Min 1Q Median 3Q Max
## -203.70 -84.11 -19.41 46.22 355.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 466.488 98.332 4.744 2.94e-05 ***
## PercentPremiumSeats -7.550 6.616 -1.141 0.261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.8 on 38 degrees of freedom
## Multiple R-squared: 0.03314, Adjusted R-squared: 0.007695
## F-statistic: 1.302 on 1 and 38 DF, p-value: 0.2609Dedom$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 327 327 407 407
## [18] 407 457 457 457 457 457 379 467 362 362 181 181 354 262 262 670 378
## [35] 308 308 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80 81
## 312.3935 312.3935 312.3935 312.3935 312.3935 312.3935 312.3935 312.3935
## 98 152 153 154 155 281 282 283
## 342.2159 357.0138 357.0138 357.0138 357.0138 372.1138 372.1138 372.1138
## 284 285 286 287 288 289 290 291
## 372.1138 372.1138 367.3573 367.3573 367.3573 372.1138 372.1138 372.1138
## 292 293 294 295 296 297 298 299
## 367.3573 372.1138 372.1138 369.6978 368.0368 368.0368 369.6978 369.6978
## 300 301 302 303 304 305 306 307
## 372.1138 369.6978 368.0368 368.0368 372.1138 369.6978 368.0368 372.1138cor(Dedom$PricePremium,Dedom$PercentPremiumSeats)## [1] -0.2204297Deint <- Delta[ which(Delta$IsInternational=='International'),]
View(Deint)
summary(Deint)## Airline Aircraft FlightDuration TravelMonth IsInternational
## AirFrance:0 AirBus:6 Min. :8.330 Aug:2 Domestic :0
## British :0 Boeing:0 1st Qu.:8.330 Jul:0 International:6
## Delta :6 Median :8.915 Oct:2
## Jet :0 Mean :8.915 Sep:2
## Singapore:0 3rd Qu.:9.500
## Virgin :0 Max. :9.500
## SeatsEconomy SeatsPremium PitchEconomy PitchPremium WidthEconomy
## Min. :233 Min. :38 Min. :31 Min. :38 Min. :18
## 1st Qu.:233 1st Qu.:38 1st Qu.:31 1st Qu.:38 1st Qu.:18
## Median :233 Median :38 Median :31 Median :38 Median :18
## Mean :233 Mean :38 Mean :31 Mean :38 Mean :18
## 3rd Qu.:233 3rd Qu.:38 3rd Qu.:31 3rd Qu.:38 3rd Qu.:18
## Max. :233 Max. :38 Max. :31 Max. :38 Max. :18
## WidthPremium PriceEconomy PricePremium PriceRelative
## Min. :21 Min. :1778 Min. :2588 Min. :0.3000
## 1st Qu.:21 1st Qu.:1830 1st Qu.:2588 1st Qu.:0.3800
## Median :21 Median :1992 Median :2676 Median :0.3800
## Mean :21 Mean :1923 Mean :2676 Mean :0.3933
## 3rd Qu.:21 3rd Qu.:1999 3rd Qu.:2765 3rd Qu.:0.4400
## Max. :21 Max. :1999 Max. :2765 Max. :0.4600
## SeatsTotal PitchDifference WidthDifference PercentPremiumSeats
## Min. :271 Min. :7 Min. :3 Min. :14.02
## 1st Qu.:271 1st Qu.:7 1st Qu.:3 1st Qu.:14.02
## Median :271 Median :7 Median :3 Median :14.02
## Mean :271 Mean :7 Mean :3 Mean :14.02
## 3rd Qu.:271 3rd Qu.:7 3rd Qu.:3 3rd Qu.:14.02
## Max. :271 Max. :7 Max. :3 Max. :14.02mean(Deint$PriceEconomy)## [1] 1923mean(Deint$PricePremium)## [1] 2676.5library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Deint$PriceEconomy,Deint$TravelMonth,mean))
y2<-c(by(Deint$PricePremium,Deint$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')## Warning: Ignoring 1 observations
## Warning: Ignoring 1 observationsfit<-lm(PriceEconomy~FlightDuration,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -6.900e+01 -6.900e+01 2.487e-14 2.487e-14 2.487e-14 1.380e+02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 764.81 526.89 1.452 0.2203
## FlightDuration 129.91 58.97 2.203 0.0924 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 84.51 on 4 degrees of freedom
## Multiple R-squared: 0.5482, Adjusted R-squared: 0.4352
## F-statistic: 4.853 on 1 and 4 DF, p-value: 0.09235Deint$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985fitted(fit)## 185 186 187 188 189 190
## 1847 1847 1999 1999 1999 1847cor(Deint$PriceEconomy,Deint$FlightDuration)## [1] 0.7403807fit<-lm(PriceEconomy~SeatsEconomy,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -145 -145 76 76 76 62
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1923.00 45.91 41.89 1.46e-07 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112.4 on 5 degrees of freedomDeint$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985fitted(fit)## 185 186 187 188 189 190
## 1923 1923 1923 1923 1923 1923cor(Deint$PriceEconomy,Deint$SeatsEconomy)## Warning in cor(Deint$PriceEconomy, Deint$SeatsEconomy): the standard
## deviation is zero## [1] NAfit<-lm(PriceEconomy~PriceRelative,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -41.47 -41.47 55.29 55.29 55.29 -82.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2533.8 205.9 12.307 0.00025 ***
## PriceRelative -1552.9 518.4 -2.996 0.04011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 69.81 on 4 degrees of freedom
## Multiple R-squared: 0.6917, Adjusted R-squared: 0.6146
## F-statistic: 8.974 on 1 and 4 DF, p-value: 0.04011Deint$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985fitted(fit)## 185 186 187 188 189 190
## 1819.471 1819.471 1943.706 1943.706 1943.706 2067.941cor(Deint$PriceEconomy,Deint$PriceRelative)## [1] -0.8316866fit<-lm(PriceEconomy~PercentPremiumSeats,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -145 -145 76 76 76 62
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1923.00 45.91 41.89 1.46e-07 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112.4 on 5 degrees of freedomDeint$PriceEconomy## [1] 1778 1778 1999 1999 1999 1985fitted(fit)## 185 186 187 188 189 190
## 1923 1923 1923 1923 1923 1923cor(Deint$PriceEconomy,Deint$PercentPremiumSeats)## Warning in cor(Deint$PriceEconomy, Deint$PercentPremiumSeats): the standard
## deviation is zero## [1] NAfit<-lm(PricePremium~FlightDuration,data = Deint)
summary(fit)## Warning in summary.lm(fit): essentially perfect fit: summary may be
## unreliable##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## 1.392e-12 -6.962e-13 -3.534e-28 -3.534e-28 -3.534e-28 -6.962e-13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.328e+03 5.316e-12 2.498e+14 <2e-16 ***
## FlightDuration 1.513e+02 5.950e-13 2.542e+14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.527e-13 on 4 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 6.464e+28 on 1 and 4 DF, p-value: < 2.2e-16Deint$PricePremium## [1] 2588 2588 2765 2765 2765 2588fitted(fit)## 185 186 187 188 189 190
## 2588 2588 2765 2765 2765 2588cor(Deint$PricePremium,Deint$FlightDuration)## [1] 1fit<-lm(PriceEconomy~SeatsEconomy,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -145 -145 76 76 76 62
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1923.00 45.91 41.89 1.46e-07 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112.4 on 5 degrees of freedomDeint$PricePremium## [1] 2588 2588 2765 2765 2765 2588fitted(fit)## 185 186 187 188 189 190
## 1923 1923 1923 1923 1923 1923cor(Deint$PricePremium,Deint$SeatsEconomy)## Warning in cor(Deint$PricePremium, Deint$SeatsEconomy): the standard
## deviation is zero## [1] NAfit<-lm(PriceEconomy~SeatsPremium,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -145 -145 76 76 76 62
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1923.00 45.91 41.89 1.46e-07 ***
## SeatsPremium NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112.4 on 5 degrees of freedomDeint$PricePremium## [1] 2588 2588 2765 2765 2765 2588fitted(fit)## 185 186 187 188 189 190
## 1923 1923 1923 1923 1923 1923cor(Deint$PricePremium,Deint$SeatsPremium)## Warning in cor(Deint$PricePremium, Deint$SeatsPremium): the standard
## deviation is zero## [1] NAfit<-lm(PriceEconomy~PriceRelative,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -41.47 -41.47 55.29 55.29 55.29 -82.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2533.8 205.9 12.307 0.00025 ***
## PriceRelative -1552.9 518.4 -2.996 0.04011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 69.81 on 4 degrees of freedom
## Multiple R-squared: 0.6917, Adjusted R-squared: 0.6146
## F-statistic: 8.974 on 1 and 4 DF, p-value: 0.04011Deint$PricePremium## [1] 2588 2588 2765 2765 2765 2588fitted(fit)## 185 186 187 188 189 190
## 1819.471 1819.471 1943.706 1943.706 1943.706 2067.941cor(Deint$PricePremium,Deint$PriceRelative)## [1] -0.2425356fit<-lm(PriceEconomy~PercentPremiumSeats,data = Deint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Deint)
##
## Residuals:
## 185 186 187 188 189 190
## -145 -145 76 76 76 62
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1923.00 45.91 41.89 1.46e-07 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112.4 on 5 degrees of freedomDeint$PricePremium## [1] 2588 2588 2765 2765 2765 2588fitted(fit)## 185 186 187 188 189 190
## 1923 1923 1923 1923 1923 1923cor(Deint$PricePremium,Deint$PercentPremiumSeats)## Warning in cor(Deint$PricePremium, Deint$PercentPremiumSeats): the standard
## deviation is zero## [1] NANow It’s time for comparison-
par(mfrow=c(1, 2))
main="Boeing vs AirBus"
library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Deboeing$PriceEconomy,Deboeing$TravelMonth,mean))
y2<-c(by(Deboeing$PricePremium,Deboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Deairbus$PriceEconomy,Deairbus$TravelMonth,mean))
y4<-c(by(Deairbus$PricePremium,Deairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(main="mean prices of economy & premium tickets in Boeing",data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Boeing)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'plot_ly(main="mean prices of economy & premium tickets in Airbus"
,data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Airbus)"),
margin = list(b = 100),
barmode = 'group')## Warning: Ignoring 1 observations## Warning: Ignoring 1 observations## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'short Analysis of Delta Airlines
mean(Delta$PriceEconomy)## [1] 560.9348mean(Delta$PricePremium)## [1] 684.6739library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Delta$PriceEconomy,Delta$TravelMonth,mean))
y2<-c(by(Delta$PricePremium,Delta$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -334.52 -283.12 22.98 243.54 493.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -311.05 79.02 -3.936 0.000291 ***
## FlightDuration 216.43 17.19 12.592 3.47e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 258.1 on 44 degrees of freedom
## Multiple R-squared: 0.7828, Adjusted R-squared: 0.7778
## F-statistic: 158.6 on 1 and 44 DF, p-value: 3.468e-16Delta$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 1778
## [15] 1778 1999 1999 1999 1985 288 288 363 363 363 413 413 413 413
## [29] 413 340 423 328 328 166 166 329 243 243 626 354 293 293
## [43] 293 636 416 349fitted(fit)## 74 75 76 77 78 79
## 134.80176 193.23802 134.80176 123.98023 193.23802 67.70828
## 80 81 98 152 153 154
## 186.74511 186.74511 610.94907 626.09921 665.05672 626.09921
## 155 185 186 187 188 189
## 665.05672 1491.82158 1491.82158 1745.04538 1745.04538 1745.04538
## 190 281 282 283 284 285
## 1491.82158 615.27768 608.78476 691.02839 695.35700 697.52131
## 286 287 288 289 290 291
## 706.17853 706.17853 706.17853 632.59213 608.78476 647.74227
## 292 293 294 295 296 297
## 706.17853 610.94907 641.24935 102.33718 110.99440 178.08788
## 298 299 300 301 302 303
## 240.85276 240.85276 691.02839 307.94624 80.69412 85.02273
## 304 305 306 307
## 28.75077 230.03123 78.52981 28.75077cor(Delta$PriceEconomy,Delta$FlightDuration)## [1] 0.8847487fit<-lm(PriceEconomy~SeatsEconomy,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -616.70 -164.10 -80.47 216.39 640.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -875.9736 135.6704 -6.457 7.19e-08 ***
## SeatsEconomy 10.4718 0.9406 11.133 2.20e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 283.5 on 44 degrees of freedom
## Multiple R-squared: 0.738, Adjusted R-squared: 0.732
## F-statistic: 123.9 on 1 and 44 DF, p-value: 2.204e-14Delta$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 1778
## [15] 1778 1999 1999 1999 1985 288 288 363 363 363 413 413 413 413
## [29] 413 340 423 328 328 166 166 329 243 243 626 354 293 293
## [43] 293 636 416 349fitted(fit)## 74 75 76 77 78 79
## -59.17586 -59.17586 -59.17586 -59.17586 -59.17586 -59.17586
## 80 81 98 152 153 154
## -59.17586 -59.17586 506.29948 914.69834 914.69834 914.69834
## 155 185 186 187 188 189
## 914.69834 1563.94781 1563.94781 1563.94781 1563.94781 1563.94781
## 190 281 282 283 284 285
## 1563.94781 443.46889 443.46889 443.46889 443.46889 443.46889
## 286 287 288 289 290 291
## 579.60184 579.60184 579.60184 443.46889 443.46889 443.46889
## 292 293 294 295 296 297
## 579.60184 443.46889 443.46889 548.18655 380.63830 380.63830
## 298 299 300 301 302 303
## 548.18655 548.18655 443.46889 548.18655 380.63830 380.63830
## 304 305 306 307
## 443.46889 548.18655 380.63830 443.46889cor(Delta$PriceEconomy,Delta$SeatsEconomy)## [1] 0.8590671fit<-lm(PriceEconomy~PriceRelative,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -340.11 -166.25 -80.76 146.24 640.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.159 52.983 0.022 0.983
## PriceRelative 4478.206 319.007 14.038 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 236.6 on 44 degrees of freedom
## Multiple R-squared: 0.8175, Adjusted R-squared: 0.8133
## F-statistic: 197.1 on 1 and 44 DF, p-value: < 2.2e-16Delta$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 1778
## [15] 1778 1999 1999 1999 1985 288 288 363 363 363 413 413 413 413
## [29] 413 340 423 328 328 166 166 329 243 243 626 354 293 293
## [43] 293 636 416 349fitted(fit)## 74 75 76 77 78 79 80
## 404.1976 359.4155 314.6335 314.6335 314.6335 180.2873 180.2873
## 81 98 152 153 154 155 185
## 135.5052 404.1976 583.3258 448.9796 404.1976 269.8514 2061.1338
## 186 187 188 189 190 281 282
## 2061.1338 1702.8773 1702.8773 1702.8773 1344.6208 628.1079 628.1079
## 283 284 285 286 287 288 289
## 538.5438 538.5438 538.5438 493.7617 493.7617 493.7617 493.7617
## 290 291 292 293 294 295 296
## 493.7617 493.7617 448.9796 448.9796 448.9796 404.1976 404.1976
## 297 298 299 300 301 302 303
## 359.4155 359.4155 359.4155 314.6335 314.6335 225.0693 225.0693
## 304 305 306 307
## 225.0693 180.2873 180.2873 180.2873cor(Delta$PriceEconomy,Delta$PriceRelative)## [1] 0.9041439fit<-lm(PriceEconomy~PercentPremiumSeats,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -427.00 -269.03 -221.56 -67.98 1429.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 840.35 423.33 1.985 0.0534 .
## PercentPremiumSeats -19.29 28.69 -0.673 0.5048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 551 on 44 degrees of freedom
## Multiple R-squared: 0.01018, Adjusted R-squared: -0.01232
## F-statistic: 0.4523 on 1 and 44 DF, p-value: 0.5048Delta$PriceEconomy## [1] 158 189 228 222 216 391 349 581 458 298 423 483 713 1778
## [15] 1778 1999 1999 1999 1985 288 288 363 363 363 413 413 413 413
## [29] 413 340 423 328 328 166 166 329 243 243 626 354 293 293
## [43] 293 636 416 349fitted(fit)## 74 75 76 77 78 79 80 81
## 446.5635 446.5635 446.5635 446.5635 446.5635 446.5635 446.5635 446.5635
## 98 152 153 154 155 185 186 187
## 522.7747 560.5908 560.5908 560.5908 560.5908 569.8520 569.8520 569.8520
## 188 189 190 281 282 283 284 285
## 569.8520 569.8520 569.8520 599.1788 599.1788 599.1788 599.1788 599.1788
## 286 287 288 289 290 291 292 293
## 587.0236 587.0236 587.0236 599.1788 599.1788 599.1788 587.0236 599.1788
## 294 295 296 297 298 299 300 301
## 599.1788 593.0047 588.7600 588.7600 593.0047 593.0047 599.1788 593.0047
## 302 303 304 305 306 307
## 588.7600 588.7600 599.1788 593.0047 588.7600 599.1788cor(Delta$PriceEconomy,Delta$PercentPremiumSeats)## [1] -0.1008732fit<-lm(PricePremium~FlightDuration,data = Delta)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -475.50 -419.01 55.82 353.68 555.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -578.29 112.68 -5.132 6.23e-06 ***
## FlightDuration 313.48 24.51 12.790 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 368.1 on 44 degrees of freedom
## Multiple R-squared: 0.788, Adjusted R-squared: 0.7832
## F-statistic: 163.6 on 1 and 44 DF, p-value: < 2.2e-16Delta$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 2588
## [15] 2588 2765 2765 2765 2588 327 327 407 407 407 457 457 457 457
## [29] 457 379 467 362 362 181 181 354 262 262 670 378 308 308
## [43] 308 660 431 364fitted(fit)## 74 75 76 77 78 79
## 67.467881 152.106270 67.467881 51.794105 152.106270 -29.709529
## 80 81 98 152 153 154
## 142.702004 142.702004 757.114016 779.057302 835.482895 779.057302
## 155 185 186 187 188 189
## 835.482895 2032.959366 2032.959366 2399.725720 2399.725720 2399.725720
## 190 281 282 283 284 285
## 2032.959366 763.383526 753.979261 873.099957 879.369467 882.504222
## 286 287 288 289 290 291
## 895.043243 895.043243 895.043243 788.461568 753.979261 810.404854
## 292 293 294 295 296 297
## 895.043243 757.114016 801.000588 20.446553 32.985574 130.162984
## 298 299 300 301 302 303
## 221.070884 221.070884 873.099957 318.248293 -10.900998 -4.631488
## 304 305 306 307
## -86.135122 205.397108 -14.035754 -86.135122cor(Delta$PricePremium,Delta$FlightDuration)## [1] 0.8877077fit<-lm(PriceEconomy~SeatsEconomy,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -616.70 -164.10 -80.47 216.39 640.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -875.9736 135.6704 -6.457 7.19e-08 ***
## SeatsEconomy 10.4718 0.9406 11.133 2.20e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 283.5 on 44 degrees of freedom
## Multiple R-squared: 0.738, Adjusted R-squared: 0.732
## F-statistic: 123.9 on 1 and 44 DF, p-value: 2.204e-14Delta$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 2588
## [15] 2588 2765 2765 2765 2588 327 327 407 407 407 457 457 457 457
## [29] 457 379 467 362 362 181 181 354 262 262 670 378 308 308
## [43] 308 660 431 364fitted(fit)## 74 75 76 77 78 79
## -59.17586 -59.17586 -59.17586 -59.17586 -59.17586 -59.17586
## 80 81 98 152 153 154
## -59.17586 -59.17586 506.29948 914.69834 914.69834 914.69834
## 155 185 186 187 188 189
## 914.69834 1563.94781 1563.94781 1563.94781 1563.94781 1563.94781
## 190 281 282 283 284 285
## 1563.94781 443.46889 443.46889 443.46889 443.46889 443.46889
## 286 287 288 289 290 291
## 579.60184 579.60184 579.60184 443.46889 443.46889 443.46889
## 292 293 294 295 296 297
## 579.60184 443.46889 443.46889 548.18655 380.63830 380.63830
## 298 299 300 301 302 303
## 548.18655 548.18655 443.46889 548.18655 380.63830 380.63830
## 304 305 306 307
## 443.46889 548.18655 380.63830 443.46889cor(Delta$PricePremium,Delta$SeatsEconomy)## [1] 0.8609209fit<-lm(PriceEconomy~SeatsPremium,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -729.61 -131.90 60.65 133.15 396.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1075.566 124.859 -8.614 5.39e-11 ***
## SeatsPremium 72.523 5.303 13.676 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 241.7 on 44 degrees of freedom
## Multiple R-squared: 0.8095, Adjusted R-squared: 0.8052
## F-statistic: 187 on 1 and 44 DF, p-value: < 2.2e-16Delta$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 2588
## [15] 2588 2765 2765 2765 2588 327 327 407 407 407 457 457 457 457
## [29] 457 379 467 362 362 181 181 354 262 262 670 378 308 308
## [43] 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80
## 374.8971 374.8971 374.8971 374.8971 374.8971 374.8971 374.8971
## 81 98 152 153 154 155 185
## 374.8971 810.0361 1027.6056 1027.6056 1027.6056 1027.6056 1680.3141
## 186 187 188 189 190 281 282
## 1680.3141 1680.3141 1680.3141 1680.3141 1680.3141 229.8507 229.8507
## 283 284 285 286 287 288 289
## 229.8507 229.8507 229.8507 447.4203 447.4203 447.4203 229.8507
## 290 291 292 293 294 295 296
## 229.8507 229.8507 447.4203 229.8507 229.8507 374.8971 229.8507
## 297 298 299 300 301 302 303
## 229.8507 374.8971 374.8971 229.8507 374.8971 229.8507 229.8507
## 304 305 306 307
## 229.8507 374.8971 229.8507 229.8507cor(Delta$PricePremium,Delta$SeatsPremium)## [1] 0.9029489fit<-lm(PriceEconomy~PriceRelative,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -340.11 -166.25 -80.76 146.24 640.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.159 52.983 0.022 0.983
## PriceRelative 4478.206 319.007 14.038 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 236.6 on 44 degrees of freedom
## Multiple R-squared: 0.8175, Adjusted R-squared: 0.8133
## F-statistic: 197.1 on 1 and 44 DF, p-value: < 2.2e-16Delta$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 2588
## [15] 2588 2765 2765 2765 2588 327 327 407 407 407 457 457 457 457
## [29] 457 379 467 362 362 181 181 354 262 262 670 378 308 308
## [43] 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80
## 404.1976 359.4155 314.6335 314.6335 314.6335 180.2873 180.2873
## 81 98 152 153 154 155 185
## 135.5052 404.1976 583.3258 448.9796 404.1976 269.8514 2061.1338
## 186 187 188 189 190 281 282
## 2061.1338 1702.8773 1702.8773 1702.8773 1344.6208 628.1079 628.1079
## 283 284 285 286 287 288 289
## 538.5438 538.5438 538.5438 493.7617 493.7617 493.7617 493.7617
## 290 291 292 293 294 295 296
## 493.7617 493.7617 448.9796 448.9796 448.9796 404.1976 404.1976
## 297 298 299 300 301 302 303
## 359.4155 359.4155 359.4155 314.6335 314.6335 225.0693 225.0693
## 304 305 306 307
## 225.0693 180.2873 180.2873 180.2873cor(Delta$PricePremium,Delta$PriceRelative)## [1] 0.9309232fit<-lm(PriceEconomy~PercentPremiumSeats,data = Delta)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Delta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -427.00 -269.03 -221.56 -67.98 1429.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 840.35 423.33 1.985 0.0534 .
## PercentPremiumSeats -19.29 28.69 -0.673 0.5048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 551 on 44 degrees of freedom
## Multiple R-squared: 0.01018, Adjusted R-squared: -0.01232
## F-statistic: 0.4523 on 1 and 44 DF, p-value: 0.5048Delta$PricePremium## [1] 173 204 243 237 231 406 364 596 497 337 467 527 757 2588
## [15] 2588 2765 2765 2765 2588 327 327 407 407 407 457 457 457 457
## [29] 457 379 467 362 362 181 181 354 262 262 670 378 308 308
## [43] 308 660 431 364fitted(fit)## 74 75 76 77 78 79 80 81
## 446.5635 446.5635 446.5635 446.5635 446.5635 446.5635 446.5635 446.5635
## 98 152 153 154 155 185 186 187
## 522.7747 560.5908 560.5908 560.5908 560.5908 569.8520 569.8520 569.8520
## 188 189 190 281 282 283 284 285
## 569.8520 569.8520 569.8520 599.1788 599.1788 599.1788 599.1788 599.1788
## 286 287 288 289 290 291 292 293
## 587.0236 587.0236 587.0236 599.1788 599.1788 599.1788 587.0236 599.1788
## 294 295 296 297 298 299 300 301
## 599.1788 593.0047 588.7600 588.7600 593.0047 593.0047 599.1788 593.0047
## 302 303 304 305 306 307
## 588.7600 588.7600 599.1788 593.0047 588.7600 599.1788cor(Delta$PricePremium,Delta$PercentPremiumSeats)## [1] -0.09712995Jet Airlines
Analyse all about Jet Airlines:-
Jet <- airline[ which(airline$Airline=='Jet'),]
View(Jet)
summary(Jet)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 7 Min. :2.500 Aug:16
## British : 0 Boeing:54 1st Qu.:2.660 Jul:15
## Delta : 0 Median :3.250 Oct:15
## Jet :61 Mean :4.144 Sep:15
## Singapore: 0 3rd Qu.:4.330
## Virgin : 0 Max. :9.500
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :124.0 Min. : 8.00 Min. :30.00
## International:61 1st Qu.:124.0 1st Qu.: 8.00 1st Qu.:30.00
## Median :138.0 Median :16.00 Median :30.00
## Mean :140.3 Mean :15.66 Mean :30.23
## 3rd Qu.:162.0 3rd Qu.:16.00 3rd Qu.:30.00
## Max. :162.0 Max. :28.00 Max. :32.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :38.00 Min. :17.00 Min. :19.00 Min. :108.0
## 1st Qu.:40.00 1st Qu.:17.00 1st Qu.:21.00 1st Qu.:154.0
## Median :40.00 Median :17.00 Median :21.00 Median :201.0
## Mean :39.77 Mean :17.11 Mean :20.77 Mean :276.2
## 3rd Qu.:40.00 3rd Qu.:17.00 3rd Qu.:21.00 3rd Qu.:354.0
## Max. :40.00 Max. :18.00 Max. :21.00 Max. :676.0
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. :228.0 Min. :0.1200 Min. :140 Min. : 6.000
## 1st Qu.:318.0 1st Qu.:0.4800 1st Qu.:140 1st Qu.:10.000
## Median :483.0 Median :0.8200 Median :166 Median :10.000
## Mean :483.4 Mean :0.9397 Mean :156 Mean : 9.541
## 3rd Qu.:569.0 3rd Qu.:1.2900 3rd Qu.:170 3rd Qu.:10.000
## Max. :931.0 Max. :1.8900 Max. :170 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :1.000 Min. : 4.71
## 1st Qu.:4.000 1st Qu.: 4.71
## Median :4.000 Median :11.43
## Mean :3.656 Mean :10.17
## 3rd Qu.:4.000 3rd Qu.:11.43
## Max. :4.000 Max. :16.87Check the all the means now all Jet aircrafts
mean(Jet$PriceEconomy)## [1] 276.1639mean(Jet$PricePremium)## [1] 483.3607mean(Jet$FlightDuration)## [1] 4.143934mean(Jet$PitchEconomy)## [1] 30.22951mean(Jet$PitchPremium)## [1] 39.77049mean(Jet$WidthEconomy)## [1] 17.11475mean(Jet$WidthPremium)## [1] 20.77049mean(Jet$PriceRelative)## [1] 0.9396721mean(Jet$PitchDifference)## [1] 9.540984mean(Jet$WidthDifference)## [1] 3.655738Now Analyse separately for Each Aircrafts in Jet Airlines i.e-Boeing and AirBus
Jeboeing <- Jet[ which(Jet$Aircraft=='Boeing'),]
View(Jeboeing)
summary(Jeboeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 0 Min. :2.500 Aug:14
## British : 0 Boeing:54 1st Qu.:2.660 Jul:14
## Delta : 0 Median :3.160 Oct:13
## Jet :54 Mean :3.482 Sep:13
## Singapore: 0 3rd Qu.:4.160
## Virgin : 0 Max. :5.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :124.0 Min. : 8.00 Min. :30
## International:54 1st Qu.:124.0 1st Qu.: 8.00 1st Qu.:30
## Median :131.0 Median :16.00 Median :30
## Mean :139.4 Mean :14.96 Mean :30
## 3rd Qu.:162.0 3rd Qu.:16.00 3rd Qu.:30
## Max. :162.0 Max. :28.00 Max. :30
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :40 Min. :17 Min. :21 Min. :108.0 Min. :228.0
## 1st Qu.:40 1st Qu.:17 1st Qu.:21 1st Qu.:154.0 1st Qu.:304.0
## Median :40 Median :17 Median :21 Median :187.0 Median :457.5
## Mean :40 Mean :17 Mean :21 Mean :243.9 Mean :435.6
## 3rd Qu.:40 3rd Qu.:17 3rd Qu.:21 3rd Qu.:323.5 3rd Qu.:531.5
## Max. :40 Max. :17 Max. :21 Max. :594.0 Max. :696.0
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.1200 Min. :140.0 Min. :10 Min. :4
## 1st Qu.:0.5200 1st Qu.:140.0 1st Qu.:10 1st Qu.:4
## Median :0.9750 Median :153.0 Median :10 Median :4
## Mean :0.9707 Mean :154.4 Mean :10 Mean :4
## 3rd Qu.:1.2975 3rd Qu.:170.0 3rd Qu.:10 3rd Qu.:4
## Max. :1.8900 Max. :170.0 Max. :10 Max. :4
## PercentPremiumSeats
## Min. : 4.710
## 1st Qu.: 4.710
## Median :11.430
## Mean : 9.871
## 3rd Qu.:11.430
## Max. :16.870mean(Jeboeing$PriceEconomy)## [1] 243.8519mean(Jeboeing$PricePremium)## [1] 435.6481library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Jeboeing$PriceEconomy,Jeboeing$TravelMonth,mean))
y2<-c(by(Jeboeing$PricePremium,Jeboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153.31 -91.01 -17.58 89.20 345.21
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 317.78 64.65 4.915 9.26e-06 ***
## FlightDuration -21.23 17.90 -1.186 0.241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.1 on 52 degrees of freedom
## Multiple R-squared: 0.02634, Adjusted R-squared: 0.007614
## F-statistic: 1.407 on 1 and 52 DF, p-value: 0.241Jeboeing$PriceEconomy## [1] 354 354 354 354 464 464 464 489 167 167 167 139 149 197 211 139 118
## [18] 118 118 108 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [35] 594 201 148 148 187 187 187 187 245 234 172 172 172 293 281 295 380
## [52] 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 252.3948 252.3948 252.3948 252.3948 252.3948 252.3948 252.3948 252.3948
## 379 380 381 382 383 384 385 386
## 248.7858 248.7858 248.7858 248.7858 248.7858 229.4669 229.4669 231.1652
## 387 388 389 390 391 392 393 394
## 264.7079 264.7079 264.7079 261.3112 261.3112 261.3112 229.4669 248.7858
## 395 396 397 398 399 400 401 402
## 231.1652 229.4669 229.4669 264.7079 261.3112 225.8579 225.8579 225.8579
## 403 404 405 440 441 442 443 444
## 225.8579 248.7858 248.7858 197.6226 250.6964 250.6964 197.6226 197.6226
## 445 446 447 448 449 450 451 452
## 197.6226 197.6226 197.6226 250.6964 263.0095 263.0095 263.0095 250.6964
## 453 454 455 456 457 458
## 263.0095 263.0095 263.0095 263.0095 248.7858 263.0095cor(Jeboeing$PriceEconomy,Jeboeing$FlightDuration)## [1] -0.1622921fit<-lm(PriceEconomy~SeatsEconomy,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -135.55 -96.55 -55.17 96.45 377.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.5746 137.1688 -0.011 0.9909
## SeatsEconomy 1.7600 0.9762 1.803 0.0772 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 124 on 52 degrees of freedom
## Multiple R-squared: 0.05883, Adjusted R-squared: 0.04073
## F-statistic: 3.25 on 1 and 52 DF, p-value: 0.0772Jeboeing$PriceEconomy## [1] 354 354 354 354 464 464 464 489 167 167 167 139 149 197 211 139 118
## [18] 118 118 108 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [35] 594 201 148 148 187 187 187 187 245 234 172 172 172 293 281 295 380
## [52] 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 241.3096 241.3096 241.3096 241.3096 241.3096 241.3096 241.3096 241.3096
## 379 380 381 382 383 384 385 386
## 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692
## 387 388 389 390 391 392 393 394
## 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692
## 395 396 397 398 399 400 401 402
## 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692
## 403 404 405 440 441 442 443 444
## 216.6692 216.6692 216.6692 283.5503 283.5503 283.5503 283.5503 283.5503
## 445 446 447 448 449 450 451 452
## 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503
## 453 454 455 456 457 458
## 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503cor(Jeboeing$PriceEconomy,Jeboeing$SeatsEconomy)## [1] 0.2425531fit<-lm(PriceEconomy~PriceRelative,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -150.289 -74.363 7.586 70.187 202.008
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 423.44 26.47 15.996 < 2e-16 ***
## PriceRelative -185.00 24.33 -7.605 5.37e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.91 on 52 degrees of freedom
## Multiple R-squared: 0.5266, Adjusted R-squared: 0.5175
## F-statistic: 57.84 on 1 and 52 DF, p-value: 5.374e-10Jeboeing$PriceEconomy## [1] 354 354 354 354 464 464 464 489 167 167 167 139 149 197 211 139 118
## [18] 118 118 108 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [35] 594 201 148 148 187 187 187 187 245 234 172 172 172 293 281 295 380
## [52] 380 505 510fitted(fit)## 90 91 92 93 94 95 96
## 334.64053 334.64053 334.64053 334.64053 362.39103 362.39103 362.39103
## 97 379 380 381 382 383 384
## 375.34126 73.78582 73.78582 73.78582 77.48589 114.48656 120.03666
## 385 386 387 388 389 390 391
## 140.38702 184.78782 190.33792 190.33792 190.33792 218.08842 218.08842
## 392 393 394 395 396 397 398
## 218.08842 221.78849 227.33859 231.03866 231.03866 255.08909 273.58942
## 399 400 401 402 403 404 405
## 277.28949 286.53966 286.53966 286.53966 286.53966 330.94046 391.99156
## 440 441 442 443 444 445 446
## 107.08642 112.63652 112.63652 182.93779 182.93779 182.93779 182.93779
## 447 448 449 450 451 452 453
## 197.73806 225.48856 280.98956 280.98956 280.98956 303.18996 312.44013
## 454 455 456 457 458
## 316.14019 340.19063 340.19063 353.14086 401.24173cor(Jeboeing$PriceEconomy,Jeboeing$PriceRelative)## [1] -0.7256655fit<-lm(PriceEconomy~PercentPremiumSeats,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -144.97 -98.97 -34.15 69.20 341.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 186.096 43.329 4.295 7.66e-05 ***
## PercentPremiumSeats 5.851 4.035 1.450 0.153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 125.3 on 52 degrees of freedom
## Multiple R-squared: 0.03885, Adjusted R-squared: 0.02037
## F-statistic: 2.102 on 1 and 52 DF, p-value: 0.1531Jeboeing$PriceEconomy## [1] 354 354 354 354 464 464 464 489 167 167 167 139 149 197 211 139 118
## [18] 118 118 108 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [35] 594 201 148 148 187 187 187 187 245 234 172 172 172 293 281 295 380
## [52] 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 284.7986 284.7986 284.7986 284.7986 284.7986 284.7986 284.7986 284.7986
## 379 380 381 382 383 384 385 386
## 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704
## 387 388 389 390 391 392 393 394
## 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704
## 395 396 397 398 399 400 401 402
## 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704
## 403 404 405 440 441 442 443 444
## 252.9704 252.9704 252.9704 213.6532 213.6532 213.6532 213.6532 213.6532
## 445 446 447 448 449 450 451 452
## 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532
## 453 454 455 456 457 458
## 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532cor(Jeboeing$PriceEconomy,Jeboeing$PercentPremiumSeats)## [1] 0.1971151fit<-lm(PricePremium~FlightDuration,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -195.25 -126.66 35.46 94.42 263.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 383.15 69.47 5.515 1.11e-06 ***
## FlightDuration 15.08 19.23 0.784 0.437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 135.5 on 52 degrees of freedom
## Multiple R-squared: 0.01168, Adjusted R-squared: -0.00733
## F-statistic: 0.6143 on 1 and 52 DF, p-value: 0.4367Jeboeing$PricePremium## [1] 524 524 524 524 616 616 616 616 483 483 483 398 398 520 534 318 267
## [18] 267 267 228 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [35] 696 545 397 397 430 430 430 430 545 483 304 304 304 483 451 464 550
## [52] 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 429.5815 429.5815 429.5815 429.5815 429.5815 429.5815 429.5815 429.5815
## 379 380 381 382 383 384 385 386
## 432.1444 432.1444 432.1444 432.1444 432.1444 445.8634 445.8634 444.6573
## 387 388 389 390 391 392 393 394
## 420.8376 420.8376 420.8376 423.2497 423.2497 423.2497 445.8634 432.1444
## 395 396 397 398 399 400 401 402
## 444.6573 445.8634 445.8634 420.8376 423.2497 448.4263 448.4263 448.4263
## 403 404 405 440 441 442 443 444
## 448.4263 432.1444 432.1444 468.4771 430.7876 430.7876 468.4771 468.4771
## 445 446 447 448 449 450 451 452
## 468.4771 468.4771 468.4771 430.7876 422.0436 422.0436 422.0436 430.7876
## 453 454 455 456 457 458
## 422.0436 422.0436 422.0436 422.0436 432.1444 422.0436cor(Jeboeing$PricePremium,Jeboeing$FlightDuration)## [1] 0.108057fit<-lm(PriceEconomy~SeatsEconomy,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -135.55 -96.55 -55.17 96.45 377.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.5746 137.1688 -0.011 0.9909
## SeatsEconomy 1.7600 0.9762 1.803 0.0772 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 124 on 52 degrees of freedom
## Multiple R-squared: 0.05883, Adjusted R-squared: 0.04073
## F-statistic: 3.25 on 1 and 52 DF, p-value: 0.0772Jeboeing$PricePremium## [1] 524 524 524 524 616 616 616 616 483 483 483 398 398 520 534 318 267
## [18] 267 267 228 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [35] 696 545 397 397 430 430 430 430 545 483 304 304 304 483 451 464 550
## [52] 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 241.3096 241.3096 241.3096 241.3096 241.3096 241.3096 241.3096 241.3096
## 379 380 381 382 383 384 385 386
## 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692
## 387 388 389 390 391 392 393 394
## 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692
## 395 396 397 398 399 400 401 402
## 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692 216.6692
## 403 404 405 440 441 442 443 444
## 216.6692 216.6692 216.6692 283.5503 283.5503 283.5503 283.5503 283.5503
## 445 446 447 448 449 450 451 452
## 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503
## 453 454 455 456 457 458
## 283.5503 283.5503 283.5503 283.5503 283.5503 283.5503cor(Jeboeing$PricePremium,Jeboeing$SeatsEconomy)## [1] 0.2681908fit<-lm(PriceEconomy~SeatsPremium,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -142.20 -96.20 -22.70 65.55 343.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 152.208 41.138 3.700 0.000521 ***
## SeatsPremium 6.125 2.519 2.431 0.018530 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.1 on 52 degrees of freedom
## Multiple R-squared: 0.1021, Adjusted R-squared: 0.0848
## F-statistic: 5.911 on 1 and 52 DF, p-value: 0.01853Jeboeing$PricePremium## [1] 524 524 524 524 616 616 616 616 483 483 483 398 398 520 534 318 267
## [18] 267 267 228 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [35] 696 545 397 397 430 430 430 430 545 483 304 304 304 483 451 464 550
## [52] 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 323.7004 323.7004 323.7004 323.7004 323.7004 323.7004 323.7004 323.7004
## 379 380 381 382 383 384 385 386
## 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034
## 387 388 389 390 391 392 393 394
## 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034
## 395 396 397 398 399 400 401 402
## 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034 250.2034
## 403 404 405 440 441 442 443 444
## 250.2034 250.2034 250.2034 201.2055 201.2055 201.2055 201.2055 201.2055
## 445 446 447 448 449 450 451 452
## 201.2055 201.2055 201.2055 201.2055 201.2055 201.2055 201.2055 201.2055
## 453 454 455 456 457 458
## 201.2055 201.2055 201.2055 201.2055 201.2055 201.2055cor(Jeboeing$PricePremium,Jeboeing$SeatsPremium)## [1] 0.1909838fit<-lm(PriceEconomy~PriceRelative,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -150.289 -74.363 7.586 70.187 202.008
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 423.44 26.47 15.996 < 2e-16 ***
## PriceRelative -185.00 24.33 -7.605 5.37e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.91 on 52 degrees of freedom
## Multiple R-squared: 0.5266, Adjusted R-squared: 0.5175
## F-statistic: 57.84 on 1 and 52 DF, p-value: 5.374e-10Jeboeing$PricePremium## [1] 524 524 524 524 616 616 616 616 483 483 483 398 398 520 534 318 267
## [18] 267 267 228 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [35] 696 545 397 397 430 430 430 430 545 483 304 304 304 483 451 464 550
## [52] 550 696 569fitted(fit)## 90 91 92 93 94 95 96
## 334.64053 334.64053 334.64053 334.64053 362.39103 362.39103 362.39103
## 97 379 380 381 382 383 384
## 375.34126 73.78582 73.78582 73.78582 77.48589 114.48656 120.03666
## 385 386 387 388 389 390 391
## 140.38702 184.78782 190.33792 190.33792 190.33792 218.08842 218.08842
## 392 393 394 395 396 397 398
## 218.08842 221.78849 227.33859 231.03866 231.03866 255.08909 273.58942
## 399 400 401 402 403 404 405
## 277.28949 286.53966 286.53966 286.53966 286.53966 330.94046 391.99156
## 440 441 442 443 444 445 446
## 107.08642 112.63652 112.63652 182.93779 182.93779 182.93779 182.93779
## 447 448 449 450 451 452 453
## 197.73806 225.48856 280.98956 280.98956 280.98956 303.18996 312.44013
## 454 455 456 457 458
## 316.14019 340.19063 340.19063 353.14086 401.24173cor(Jeboeing$PricePremium,Jeboeing$PriceRelative)## [1] -0.2920736fit<-lm(PriceEconomy~PercentPremiumSeats,data = Jeboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Jeboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -144.97 -98.97 -34.15 69.20 341.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 186.096 43.329 4.295 7.66e-05 ***
## PercentPremiumSeats 5.851 4.035 1.450 0.153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 125.3 on 52 degrees of freedom
## Multiple R-squared: 0.03885, Adjusted R-squared: 0.02037
## F-statistic: 2.102 on 1 and 52 DF, p-value: 0.1531Jeboeing$PricePremium## [1] 524 524 524 524 616 616 616 616 483 483 483 398 398 520 534 318 267
## [18] 267 267 228 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [35] 696 545 397 397 430 430 430 430 545 483 304 304 304 483 451 464 550
## [52] 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 284.7986 284.7986 284.7986 284.7986 284.7986 284.7986 284.7986 284.7986
## 379 380 381 382 383 384 385 386
## 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704
## 387 388 389 390 391 392 393 394
## 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704
## 395 396 397 398 399 400 401 402
## 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704 252.9704
## 403 404 405 440 441 442 443 444
## 252.9704 252.9704 252.9704 213.6532 213.6532 213.6532 213.6532 213.6532
## 445 446 447 448 449 450 451 452
## 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532
## 453 454 455 456 457 458
## 213.6532 213.6532 213.6532 213.6532 213.6532 213.6532cor(Jeboeing$PricePremium,Jeboeing$PercentPremiumSeats)## [1] 0.08490088Jeairbus <-Jet[ which(Jet$Aircraft=='AirBus'),]
View(Jeairbus)
summary(Jeairbus)## Airline Aircraft FlightDuration TravelMonth IsInternational
## AirFrance:0 AirBus:7 Min. :8.910 Aug:2 Domestic :0
## British :0 Boeing:0 1st Qu.:8.910 Jul:1 International:7
## Delta :0 Median :9.500 Oct:2
## Jet :7 Mean :9.247 Sep:2
## Singapore:0 3rd Qu.:9.500
## Virgin :0 Max. :9.500
## SeatsEconomy SeatsPremium PitchEconomy PitchPremium WidthEconomy
## Min. :147 Min. :21 Min. :32 Min. :38 Min. :18
## 1st Qu.:147 1st Qu.:21 1st Qu.:32 1st Qu.:38 1st Qu.:18
## Median :147 Median :21 Median :32 Median :38 Median :18
## Mean :147 Mean :21 Mean :32 Mean :38 Mean :18
## 3rd Qu.:147 3rd Qu.:21 3rd Qu.:32 3rd Qu.:38 3rd Qu.:18
## Max. :147 Max. :21 Max. :32 Max. :38 Max. :18
## WidthPremium PriceEconomy PricePremium PriceRelative
## Min. :19 Min. :336.0 Min. :789.0 Min. :0.38
## 1st Qu.:19 1st Qu.:445.5 1st Qu.:815.0 1st Qu.:0.41
## Median :19 Median :557.0 Median :841.0 Median :0.42
## Mean :19 Mean :525.4 Mean :851.4 Mean :0.70
## 3rd Qu.:19 3rd Qu.:609.0 3rd Qu.:884.5 3rd Qu.:0.89
## Max. :19 Max. :676.0 Max. :931.0 Max. :1.50
## SeatsTotal PitchDifference WidthDifference PercentPremiumSeats
## Min. :168 Min. :6 Min. :1 Min. :12.5
## 1st Qu.:168 1st Qu.:6 1st Qu.:1 1st Qu.:12.5
## Median :168 Median :6 Median :1 Median :12.5
## Mean :168 Mean :6 Mean :1 Mean :12.5
## 3rd Qu.:168 3rd Qu.:6 3rd Qu.:1 3rd Qu.:12.5
## Max. :168 Max. :6 Max. :1 Max. :12.5mean(Jeairbus$PriceEconomy)## [1] 525.4286mean(Jeairbus$PricePremium)## [1] 851.4286library(plotly)
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Jeairbus$PriceEconomy,Jeairbus$TravelMonth,mean))
y4<-c(by(Jeairbus$PricePremium,Jeairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Jeairbus)
##
## Residuals:
## 308 309 310 311 312 313 314
## -136.00 -43.00 -10.00 -39.67 -39.67 189.00 79.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2479.3 1371.9 1.807 0.131
## FlightDuration -211.3 148.3 -1.425 0.213
##
## Residual standard error: 114.5 on 5 degrees of freedom
## Multiple R-squared: 0.2888, Adjusted R-squared: 0.1466
## F-statistic: 2.031 on 1 and 5 DF, p-value: 0.2135Jeairbus$PriceEconomy## [1] 336 429 462 557 557 661 676fitted(fit)## 308 309 310 311 312 313 314
## 472.0000 472.0000 472.0000 596.6667 596.6667 472.0000 596.6667cor(Jeairbus$PriceEconomy,Jeairbus$FlightDuration)## [1] -0.5374146fit<-lm(PriceEconomy~SeatsEconomy,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Jeairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -189.43 -79.93 31.57 83.57 150.57
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 525.43 46.87 11.21 3.01e-05 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 124 on 6 degrees of freedomJeairbus$PriceEconomy## [1] 336 429 462 557 557 661 676fitted(fit)## 308 309 310 311 312 313 314
## 525.4286 525.4286 525.4286 525.4286 525.4286 525.4286 525.4286cor(Jeairbus$PriceEconomy,Jeairbus$SeatsEconomy)## Warning in cor(Jeairbus$PriceEconomy, Jeairbus$SeatsEconomy): the standard
## deviation is zero## [1] NAfit<-lm(PriceEconomy~PriceRelative,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Jeairbus)
##
## Residuals:
## 308 309 310 311 312 313 314
## 27.72 -25.86 -30.86 -44.43 -44.43 54.14 63.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 715.43 40.00 17.885 1e-05 ***
## PriceRelative -271.43 49.88 -5.441 0.00285 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 51.63 on 5 degrees of freedom
## Multiple R-squared: 0.8555, Adjusted R-squared: 0.8266
## F-statistic: 29.61 on 1 and 5 DF, p-value: 0.002846Jeairbus$PriceEconomy## [1] 336 429 462 557 557 661 676fitted(fit)## 308 309 310 311 312 313 314
## 308.2814 454.8558 492.8565 601.4301 601.4301 606.8587 612.2874cor(Jeairbus$PriceEconomy,Jeairbus$PriceRelative)## [1] -0.924947fit<-lm(PriceEconomy~PercentPremiumSeats,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Jeairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -189.43 -79.93 31.57 83.57 150.57
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 525.43 46.87 11.21 3.01e-05 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 124 on 6 degrees of freedomJeairbus$PriceEconomy## [1] 336 429 462 557 557 661 676fitted(fit)## 308 309 310 311 312 313 314
## 525.4286 525.4286 525.4286 525.4286 525.4286 525.4286 525.4286fit<-lm(PricePremium~FlightDuration,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Jeairbus)
##
## Residuals:
## 308 309 310 311 312 313 314
## -21.75 -21.75 -21.75 -47.33 -47.33 65.25 94.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 437.40 740.60 0.591 0.58
## FlightDuration 44.77 80.05 0.559 0.60
##
## Residual standard error: 61.84 on 5 degrees of freedom
## Multiple R-squared: 0.05889, Adjusted R-squared: -0.1293
## F-statistic: 0.3128 on 1 and 5 DF, p-value: 0.6001Jeairbus$PricePremium## [1] 841 841 841 789 789 928 931fitted(fit)## 308 309 310 311 312 313 314
## 862.7500 862.7500 862.7500 836.3333 836.3333 862.7500 836.3333cor(Jeairbus$PricePremium,Jeairbus$FlightDuration)## [1] 0.242663fit<-lm(PricePremium~SeatsEconomy,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = Jeairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -62.43 -36.43 -10.43 33.07 79.57
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 851.43 21.99 38.71 1.98e-08 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 58.19 on 6 degrees of freedomJeairbus$PricePremium## [1] 841 841 841 789 789 928 931fitted(fit)## 308 309 310 311 312 313 314
## 851.4286 851.4286 851.4286 851.4286 851.4286 851.4286 851.4286cor(Jeairbus$PricePremium,Jeairbus$SeatsEconomy)## Warning in cor(Jeairbus$PricePremium, Jeairbus$SeatsEconomy): the standard
## deviation is zero## [1] NAfit<-lm(PricePremium~SeatsPremium,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = Jeairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -62.43 -36.43 -10.43 33.07 79.57
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 851.43 21.99 38.71 1.98e-08 ***
## SeatsPremium NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 58.19 on 6 degrees of freedomJeairbus$PricePremium## [1] 841 841 841 789 789 928 931fitted(fit)## 308 309 310 311 312 313 314
## 851.4286 851.4286 851.4286 851.4286 851.4286 851.4286 851.4286cor(Jeairbus$PricePremium,Jeairbus$SeatsPremium)## Warning in cor(Jeairbus$PricePremium, Jeairbus$SeatsPremium): the standard
## deviation is zero## [1] NAfit<-lm(PricePremium~PriceRelative,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = Jeairbus)
##
## Residuals:
## 308 309 310 311 312 313 314
## 8.825 -4.171 -7.541 -69.167 -69.167 69.351 71.870
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 868.28 48.63 17.856 1.01e-05 ***
## PriceRelative -24.07 60.64 -0.397 0.708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 62.76 on 5 degrees of freedom
## Multiple R-squared: 0.03054, Adjusted R-squared: -0.1634
## F-statistic: 0.1575 on 1 and 5 DF, p-value: 0.7078Jeairbus$PricePremium## [1] 841 841 841 789 789 928 931fitted(fit)## 308 309 310 311 312 313 314
## 832.1754 845.1713 848.5406 858.1672 858.1672 858.6485 859.1298cor(Jeairbus$PricePremium,Jeairbus$PriceRelative)## [1] -0.1747558fit<-lm(PricePremium~PercentPremiumSeats,data = Jeairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = Jeairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -62.43 -36.43 -10.43 33.07 79.57
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 851.43 21.99 38.71 1.98e-08 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 58.19 on 6 degrees of freedomJeairbus$PricePremium## [1] 841 841 841 789 789 928 931fitted(fit)## 308 309 310 311 312 313 314
## 851.4286 851.4286 851.4286 851.4286 851.4286 851.4286 851.4286cor(Jeairbus$PricePremium,Jeairbus$PercentPremiumSeats)## Warning in cor(Jeairbus$PricePremium, Jeairbus$PercentPremiumSeats): the
## standard deviation is zero## [1] NANow We Should Analyse the international aircrafts of Jet Airlines
Jeint <- Jet[ which(Jet$IsInternational=='International'),]
View(Jeint)
summary(Jeint)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 7 Min. :2.500 Aug:16
## British : 0 Boeing:54 1st Qu.:2.660 Jul:15
## Delta : 0 Median :3.250 Oct:15
## Jet :61 Mean :4.144 Sep:15
## Singapore: 0 3rd Qu.:4.330
## Virgin : 0 Max. :9.500
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :124.0 Min. : 8.00 Min. :30.00
## International:61 1st Qu.:124.0 1st Qu.: 8.00 1st Qu.:30.00
## Median :138.0 Median :16.00 Median :30.00
## Mean :140.3 Mean :15.66 Mean :30.23
## 3rd Qu.:162.0 3rd Qu.:16.00 3rd Qu.:30.00
## Max. :162.0 Max. :28.00 Max. :32.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :38.00 Min. :17.00 Min. :19.00 Min. :108.0
## 1st Qu.:40.00 1st Qu.:17.00 1st Qu.:21.00 1st Qu.:154.0
## Median :40.00 Median :17.00 Median :21.00 Median :201.0
## Mean :39.77 Mean :17.11 Mean :20.77 Mean :276.2
## 3rd Qu.:40.00 3rd Qu.:17.00 3rd Qu.:21.00 3rd Qu.:354.0
## Max. :40.00 Max. :18.00 Max. :21.00 Max. :676.0
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. :228.0 Min. :0.1200 Min. :140 Min. : 6.000
## 1st Qu.:318.0 1st Qu.:0.4800 1st Qu.:140 1st Qu.:10.000
## Median :483.0 Median :0.8200 Median :166 Median :10.000
## Mean :483.4 Mean :0.9397 Mean :156 Mean : 9.541
## 3rd Qu.:569.0 3rd Qu.:1.2900 3rd Qu.:170 3rd Qu.:10.000
## Max. :931.0 Max. :1.8900 Max. :170 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :1.000 Min. : 4.71
## 1st Qu.:4.000 1st Qu.: 4.71
## Median :4.000 Median :11.43
## Mean :3.656 Mean :10.17
## 3rd Qu.:4.000 3rd Qu.:11.43
## Max. :4.000 Max. :16.87mean(Jeint$PriceEconomy)## [1] 276.1639mean(Jeint$PricePremium)## [1] 483.3607library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Jeint$PriceEconomy,Jeint$TravelMonth,mean))
y2<-c(by(Jeint$PricePremium,Jeint$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -141.72 -116.72 -65.72 114.72 348.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 132.512 39.881 3.323 0.001535 **
## FlightDuration 34.666 8.627 4.018 0.000168 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 138.1 on 59 degrees of freedom
## Multiple R-squared: 0.2149, Adjusted R-squared: 0.2016
## F-statistic: 16.15 on 1 and 59 DF, p-value: 0.0001684Jeint$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 239.2820 239.2820 239.2820 239.2820 239.2820 239.2820 239.2820 239.2820
## 308 309 310 311 312 313 314 379
## 461.8353 461.8353 461.8353 441.3826 441.3826 461.8353 441.3826 245.1751
## 380 381 382 383 384 385 386 387
## 245.1751 245.1751 245.1751 245.1751 276.7209 276.7209 273.9476 219.1759
## 388 389 390 391 392 393 394 395
## 219.1759 219.1759 224.7224 224.7224 224.7224 276.7209 245.1751 273.9476
## 396 397 398 399 400 401 402 403
## 276.7209 276.7209 219.1759 224.7224 282.6140 282.6140 282.6140 282.6140
## 404 405 440 441 442 443 444 445
## 245.1751 245.1751 328.7193 242.0552 242.0552 328.7193 328.7193 328.7193
## 446 447 448 449 450 451 452 453
## 328.7193 328.7193 242.0552 221.9492 221.9492 221.9492 242.0552 221.9492
## 454 455 456 457 458
## 221.9492 221.9492 221.9492 245.1751 221.9492cor(Jeint$PriceEconomy,Jeint$FlightDuration)## [1] 0.4635422fit<-lm(PriceEconomy~SeatsEconomy,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182.87 -117.02 -68.02 83.67 382.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -77.750 165.094 -0.471 0.639
## SeatsEconomy 2.522 1.169 2.158 0.035 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150 on 59 degrees of freedom
## Multiple R-squared: 0.07318, Adjusted R-squared: 0.05747
## F-statistic: 4.659 on 1 and 59 DF, p-value: 0.03498Jeint$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336
## 308 309 310 311 312 313 314 379
## 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 235.0208
## 380 381 382 383 384 385 386 387
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 388 389 390 391 392 393 394 395
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 396 397 398 399 400 401 402 403
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 404 405 440 441 442 443 444 445
## 235.0208 235.0208 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 446 447 448 449 450 451 452 453
## 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 454 455 456 457 458
## 330.8699 330.8699 330.8699 330.8699 330.8699cor(Jeint$PriceEconomy,Jeint$SeatsEconomy)## [1] 0.2705179fit<-lm(PriceEconomy~PriceRelative,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -181.79 -88.34 -10.62 64.69 277.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 480.99 31.05 15.491 < 2e-16 ***
## PriceRelative -217.97 29.31 -7.436 4.93e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112 on 59 degrees of freedom
## Multiple R-squared: 0.4838, Adjusted R-squared: 0.4751
## F-statistic: 55.3 on 1 and 59 DF, p-value: 4.929e-10Jeint$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96
## 376.36087 376.36087 376.36087 376.36087 409.05708 409.05708 409.05708
## 97 308 309 310 311 312 313
## 424.31532 154.02659 271.73297 302.24944 389.43935 389.43935 393.79885
## 314 379 380 381 382 383 384
## 398.15835 69.01642 69.01642 69.01642 73.37592 116.97088 123.51012
## 385 386 387 388 389 390 391
## 147.48735 199.80129 206.34054 206.34054 206.34054 239.03675 239.03675
## 392 393 394 395 396 397 398
## 239.03675 243.39625 249.93549 254.29499 254.29499 282.63171 304.42919
## 399 400 401 402 403 404 405
## 308.78868 319.68742 319.68742 319.68742 319.68742 372.00137 443.93305
## 440 441 442 443 444 445 446
## 108.25188 114.79113 114.79113 197.62155 197.62155 197.62155 197.62155
## 447 448 449 450 451 452 453
## 215.05953 247.75575 313.14818 313.14818 313.14818 339.30515 350.20389
## 454 455 456 457 458
## 354.56339 382.90011 382.90011 398.15835 454.83179cor(Jeint$PriceEconomy,Jeint$PriceRelative)## [1] -0.6955696fit<-lm(PriceEconomy~PercentPremiumSeats,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -180.93 -132.93 -33.66 74.34 376.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 172.81 51.82 3.335 0.00148 **
## PercentPremiumSeats 10.16 4.73 2.148 0.03586 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150.1 on 59 degrees of freedom
## Multiple R-squared: 0.07251, Adjusted R-squared: 0.05679
## F-statistic: 4.612 on 1 and 59 DF, p-value: 0.03586Jeint$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980
## 308 309 310 311 312 313 314 379
## 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 288.9327
## 380 381 382 383 384 385 386 387
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 388 389 390 391 392 393 394 395
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 396 397 398 399 400 401 402 403
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 404 405 440 441 442 443 444 445
## 288.9327 288.9327 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 446 447 448 449 450 451 452 453
## 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 454 455 456 457 458
## 220.6638 220.6638 220.6638 220.6638 220.6638cor(Jeint$PriceEconomy,Jeint$PercentPremiumSeats)## [1] 0.2692723fit<-lm(PricePremium~FlightDuration,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -227.71 -116.06 30.86 105.55 267.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 230.535 39.510 5.835 2.43e-07 ***
## FlightDuration 61.011 8.546 7.139 1.58e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 136.8 on 59 degrees of freedom
## Multiple R-squared: 0.4634, Adjusted R-squared: 0.4544
## F-statistic: 50.96 on 1 and 59 DF, p-value: 1.575e-09Jeint$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 418.4490 418.4490 418.4490 418.4490 418.4490 418.4490 418.4490 418.4490
## 308 309 310 311 312 313 314 379
## 810.1396 810.1396 810.1396 774.1431 774.1431 810.1396 774.1431 428.8208
## 380 381 382 383 384 385 386 387
## 428.8208 428.8208 428.8208 428.8208 484.3408 484.3408 479.4600 383.0626
## 388 389 390 391 392 393 394 395
## 383.0626 383.0626 392.8243 392.8243 392.8243 484.3408 428.8208 479.4600
## 396 397 398 399 400 401 402 403
## 484.3408 484.3408 383.0626 392.8243 494.7127 494.7127 494.7127 494.7127
## 404 405 440 441 442 443 444 445
## 428.8208 428.8208 575.8573 423.3298 423.3298 575.8573 575.8573 575.8573
## 446 447 448 449 450 451 452 453
## 575.8573 575.8573 423.3298 387.9435 387.9435 387.9435 423.3298 387.9435
## 454 455 456 457 458
## 387.9435 387.9435 387.9435 428.8208 387.9435cor(Jeint$PricePremium,Jeint$FlightDuration)## [1] 0.6807696fit<-lm(PriceEconomy~SeatsEconomy,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182.87 -117.02 -68.02 83.67 382.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -77.750 165.094 -0.471 0.639
## SeatsEconomy 2.522 1.169 2.158 0.035 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150 on 59 degrees of freedom
## Multiple R-squared: 0.07318, Adjusted R-squared: 0.05747
## F-statistic: 4.659 on 1 and 59 DF, p-value: 0.03498Jeint$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336
## 308 309 310 311 312 313 314 379
## 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 235.0208
## 380 381 382 383 384 385 386 387
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 388 389 390 391 392 393 394 395
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 396 397 398 399 400 401 402 403
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 404 405 440 441 442 443 444 445
## 235.0208 235.0208 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 446 447 448 449 450 451 452 453
## 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 454 455 456 457 458
## 330.8699 330.8699 330.8699 330.8699 330.8699cor(Jeint$PricePremium,Jeint$SeatsEconomy)## [1] 0.2874964fit<-lm(PriceEconomy~SeatsPremium,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -171.51 -123.51 -29.65 67.69 347.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 123.782 47.797 2.590 0.01208 *
## SeatsPremium 9.733 2.823 3.448 0.00105 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 142.2 on 59 degrees of freedom
## Multiple R-squared: 0.1677, Adjusted R-squared: 0.1536
## F-statistic: 11.89 on 1 and 59 DF, p-value: 0.001048Jeint$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 396.3145 396.3145 396.3145 396.3145 396.3145 396.3145 396.3145 396.3145
## 308 309 310 311 312 313 314 379
## 328.1813 328.1813 328.1813 328.1813 328.1813 328.1813 328.1813 279.5147
## 380 381 382 383 384 385 386 387
## 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147
## 388 389 390 391 392 393 394 395
## 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147
## 396 397 398 399 400 401 402 403
## 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147
## 404 405 440 441 442 443 444 445
## 279.5147 279.5147 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483
## 446 447 448 449 450 451 452 453
## 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483
## 454 455 456 457 458
## 201.6483 201.6483 201.6483 201.6483 201.6483cor(Jeint$PricePremium,Jeint$SeatsPremium)## [1] 0.340239fit<-lm(PriceEconomy~PriceRelative,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -181.79 -88.34 -10.62 64.69 277.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 480.99 31.05 15.491 < 2e-16 ***
## PriceRelative -217.97 29.31 -7.436 4.93e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112 on 59 degrees of freedom
## Multiple R-squared: 0.4838, Adjusted R-squared: 0.4751
## F-statistic: 55.3 on 1 and 59 DF, p-value: 4.929e-10Jeint$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96
## 376.36087 376.36087 376.36087 376.36087 409.05708 409.05708 409.05708
## 97 308 309 310 311 312 313
## 424.31532 154.02659 271.73297 302.24944 389.43935 389.43935 393.79885
## 314 379 380 381 382 383 384
## 398.15835 69.01642 69.01642 69.01642 73.37592 116.97088 123.51012
## 385 386 387 388 389 390 391
## 147.48735 199.80129 206.34054 206.34054 206.34054 239.03675 239.03675
## 392 393 394 395 396 397 398
## 239.03675 243.39625 249.93549 254.29499 254.29499 282.63171 304.42919
## 399 400 401 402 403 404 405
## 308.78868 319.68742 319.68742 319.68742 319.68742 372.00137 443.93305
## 440 441 442 443 444 445 446
## 108.25188 114.79113 114.79113 197.62155 197.62155 197.62155 197.62155
## 447 448 449 450 451 452 453
## 215.05953 247.75575 313.14818 313.14818 313.14818 339.30515 350.20389
## 454 455 456 457 458
## 354.56339 382.90011 382.90011 398.15835 454.83179cor(Jeint$PricePremium,Jeint$PriceRelative)## [1] -0.3214004fit<-lm(PriceEconomy~PercentPremiumSeats,data = Jeint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Jeint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -180.93 -132.93 -33.66 74.34 376.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 172.81 51.82 3.335 0.00148 **
## PercentPremiumSeats 10.16 4.73 2.148 0.03586 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150.1 on 59 degrees of freedom
## Multiple R-squared: 0.07251, Adjusted R-squared: 0.05679
## F-statistic: 4.612 on 1 and 59 DF, p-value: 0.03586Jeint$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980
## 308 309 310 311 312 313 314 379
## 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 288.9327
## 380 381 382 383 384 385 386 387
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 388 389 390 391 392 393 394 395
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 396 397 398 399 400 401 402 403
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 404 405 440 441 442 443 444 445
## 288.9327 288.9327 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 446 447 448 449 450 451 452 453
## 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 454 455 456 457 458
## 220.6638 220.6638 220.6638 220.6638 220.6638cor(Jeint$PricePremium,Jeint$PercentPremiumSeats)## [1] 0.2057525Now It’s time for comparison-
par(mfrow=c(1, 2))
main="Boeing vs AirBus"
library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Jeboeing$PriceEconomy,Jeboeing$TravelMonth,mean))
y2<-c(by(Jeboeing$PricePremium,Jeboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(Jeairbus$PriceEconomy,Jeairbus$TravelMonth,mean))
y4<-c(by(Jeairbus$PricePremium,Jeairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(main="mean prices of economy & premium tickets in Boeing",data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Boeing)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'plot_ly(main="mean prices of economy & premium tickets in Airbus"
,data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Airbus)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'short Analysis of Jet Airlines
mean(Jet$PriceEconomy)## [1] 276.1639mean(Jet$PricePremium)## [1] 483.3607library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Jet$PriceEconomy,Jet$TravelMonth,mean))
y2<-c(by(Jet$PricePremium,Jet$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -141.72 -116.72 -65.72 114.72 348.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 132.512 39.881 3.323 0.001535 **
## FlightDuration 34.666 8.627 4.018 0.000168 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 138.1 on 59 degrees of freedom
## Multiple R-squared: 0.2149, Adjusted R-squared: 0.2016
## F-statistic: 16.15 on 1 and 59 DF, p-value: 0.0001684Jet$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 239.2820 239.2820 239.2820 239.2820 239.2820 239.2820 239.2820 239.2820
## 308 309 310 311 312 313 314 379
## 461.8353 461.8353 461.8353 441.3826 441.3826 461.8353 441.3826 245.1751
## 380 381 382 383 384 385 386 387
## 245.1751 245.1751 245.1751 245.1751 276.7209 276.7209 273.9476 219.1759
## 388 389 390 391 392 393 394 395
## 219.1759 219.1759 224.7224 224.7224 224.7224 276.7209 245.1751 273.9476
## 396 397 398 399 400 401 402 403
## 276.7209 276.7209 219.1759 224.7224 282.6140 282.6140 282.6140 282.6140
## 404 405 440 441 442 443 444 445
## 245.1751 245.1751 328.7193 242.0552 242.0552 328.7193 328.7193 328.7193
## 446 447 448 449 450 451 452 453
## 328.7193 328.7193 242.0552 221.9492 221.9492 221.9492 242.0552 221.9492
## 454 455 456 457 458
## 221.9492 221.9492 221.9492 245.1751 221.9492cor(Jet$PriceEconomy,Jet$FlightDuration)## [1] 0.4635422fit<-lm(PriceEconomy~SeatsEconomy,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182.87 -117.02 -68.02 83.67 382.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -77.750 165.094 -0.471 0.639
## SeatsEconomy 2.522 1.169 2.158 0.035 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150 on 59 degrees of freedom
## Multiple R-squared: 0.07318, Adjusted R-squared: 0.05747
## F-statistic: 4.659 on 1 and 59 DF, p-value: 0.03498Jet$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336
## 308 309 310 311 312 313 314 379
## 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 235.0208
## 380 381 382 383 384 385 386 387
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 388 389 390 391 392 393 394 395
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 396 397 398 399 400 401 402 403
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 404 405 440 441 442 443 444 445
## 235.0208 235.0208 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 446 447 448 449 450 451 452 453
## 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 454 455 456 457 458
## 330.8699 330.8699 330.8699 330.8699 330.8699cor(Jet$PriceEconomy,Jet$SeatsEconomy)## [1] 0.2705179fit<-lm(PriceEconomy~PriceRelative,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -181.79 -88.34 -10.62 64.69 277.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 480.99 31.05 15.491 < 2e-16 ***
## PriceRelative -217.97 29.31 -7.436 4.93e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112 on 59 degrees of freedom
## Multiple R-squared: 0.4838, Adjusted R-squared: 0.4751
## F-statistic: 55.3 on 1 and 59 DF, p-value: 4.929e-10Jet$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96
## 376.36087 376.36087 376.36087 376.36087 409.05708 409.05708 409.05708
## 97 308 309 310 311 312 313
## 424.31532 154.02659 271.73297 302.24944 389.43935 389.43935 393.79885
## 314 379 380 381 382 383 384
## 398.15835 69.01642 69.01642 69.01642 73.37592 116.97088 123.51012
## 385 386 387 388 389 390 391
## 147.48735 199.80129 206.34054 206.34054 206.34054 239.03675 239.03675
## 392 393 394 395 396 397 398
## 239.03675 243.39625 249.93549 254.29499 254.29499 282.63171 304.42919
## 399 400 401 402 403 404 405
## 308.78868 319.68742 319.68742 319.68742 319.68742 372.00137 443.93305
## 440 441 442 443 444 445 446
## 108.25188 114.79113 114.79113 197.62155 197.62155 197.62155 197.62155
## 447 448 449 450 451 452 453
## 215.05953 247.75575 313.14818 313.14818 313.14818 339.30515 350.20389
## 454 455 456 457 458
## 354.56339 382.90011 382.90011 398.15835 454.83179cor(Jet$PriceEconomy,Jet$PriceRelative)## [1] -0.6955696fit<-lm(PriceEconomy~PercentPremiumSeats,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -180.93 -132.93 -33.66 74.34 376.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 172.81 51.82 3.335 0.00148 **
## PercentPremiumSeats 10.16 4.73 2.148 0.03586 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150.1 on 59 degrees of freedom
## Multiple R-squared: 0.07251, Adjusted R-squared: 0.05679
## F-statistic: 4.612 on 1 and 59 DF, p-value: 0.03586Jet$PriceEconomy## [1] 354 354 354 354 464 464 464 489 336 429 462 557 557 661 676 167 167
## [18] 167 139 149 197 211 139 118 118 118 108 108 108 297 234 156 156 324
## [35] 147 127 154 154 154 154 322 594 201 148 148 187 187 187 187 245 234
## [52] 172 172 172 293 281 295 380 380 505 510fitted(fit)## 90 91 92 93 94 95 96 97
## 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980
## 308 309 310 311 312 313 314 379
## 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 288.9327
## 380 381 382 383 384 385 386 387
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 388 389 390 391 392 393 394 395
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 396 397 398 399 400 401 402 403
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 404 405 440 441 442 443 444 445
## 288.9327 288.9327 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 446 447 448 449 450 451 452 453
## 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 454 455 456 457 458
## 220.6638 220.6638 220.6638 220.6638 220.6638cor(Jet$PriceEconomy,Jet$PercentPremiumSeats)## [1] 0.2692723fit<-lm(PricePremium~FlightDuration,data = Jet)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -227.71 -116.06 30.86 105.55 267.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 230.535 39.510 5.835 2.43e-07 ***
## FlightDuration 61.011 8.546 7.139 1.58e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 136.8 on 59 degrees of freedom
## Multiple R-squared: 0.4634, Adjusted R-squared: 0.4544
## F-statistic: 50.96 on 1 and 59 DF, p-value: 1.575e-09Jet$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 418.4490 418.4490 418.4490 418.4490 418.4490 418.4490 418.4490 418.4490
## 308 309 310 311 312 313 314 379
## 810.1396 810.1396 810.1396 774.1431 774.1431 810.1396 774.1431 428.8208
## 380 381 382 383 384 385 386 387
## 428.8208 428.8208 428.8208 428.8208 484.3408 484.3408 479.4600 383.0626
## 388 389 390 391 392 393 394 395
## 383.0626 383.0626 392.8243 392.8243 392.8243 484.3408 428.8208 479.4600
## 396 397 398 399 400 401 402 403
## 484.3408 484.3408 383.0626 392.8243 494.7127 494.7127 494.7127 494.7127
## 404 405 440 441 442 443 444 445
## 428.8208 428.8208 575.8573 423.3298 423.3298 575.8573 575.8573 575.8573
## 446 447 448 449 450 451 452 453
## 575.8573 575.8573 423.3298 387.9435 387.9435 387.9435 423.3298 387.9435
## 454 455 456 457 458
## 387.9435 387.9435 387.9435 428.8208 387.9435cor(Jet$PricePremium,Jet$FlightDuration)## [1] 0.6807696fit<-lm(PriceEconomy~SeatsEconomy,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182.87 -117.02 -68.02 83.67 382.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -77.750 165.094 -0.471 0.639
## SeatsEconomy 2.522 1.169 2.158 0.035 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150 on 59 degrees of freedom
## Multiple R-squared: 0.07318, Adjusted R-squared: 0.05747
## F-statistic: 4.659 on 1 and 59 DF, p-value: 0.03498Jet$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336 270.3336
## 308 309 310 311 312 313 314 379
## 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 293.0347 235.0208
## 380 381 382 383 384 385 386 387
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 388 389 390 391 392 393 394 395
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 396 397 398 399 400 401 402 403
## 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208 235.0208
## 404 405 440 441 442 443 444 445
## 235.0208 235.0208 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 446 447 448 449 450 451 452 453
## 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699 330.8699
## 454 455 456 457 458
## 330.8699 330.8699 330.8699 330.8699 330.8699cor(Jet$PricePremium,Jet$SeatsEconomy)## [1] 0.2874964fit<-lm(PriceEconomy~SeatsPremium,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -171.51 -123.51 -29.65 67.69 347.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 123.782 47.797 2.590 0.01208 *
## SeatsPremium 9.733 2.823 3.448 0.00105 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 142.2 on 59 degrees of freedom
## Multiple R-squared: 0.1677, Adjusted R-squared: 0.1536
## F-statistic: 11.89 on 1 and 59 DF, p-value: 0.001048Jet$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 396.3145 396.3145 396.3145 396.3145 396.3145 396.3145 396.3145 396.3145
## 308 309 310 311 312 313 314 379
## 328.1813 328.1813 328.1813 328.1813 328.1813 328.1813 328.1813 279.5147
## 380 381 382 383 384 385 386 387
## 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147
## 388 389 390 391 392 393 394 395
## 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147
## 396 397 398 399 400 401 402 403
## 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147 279.5147
## 404 405 440 441 442 443 444 445
## 279.5147 279.5147 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483
## 446 447 448 449 450 451 452 453
## 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483 201.6483
## 454 455 456 457 458
## 201.6483 201.6483 201.6483 201.6483 201.6483cor(Jet$PricePremium,Jet$SeatsPremium)## [1] 0.340239fit<-lm(PriceEconomy~PriceRelative,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -181.79 -88.34 -10.62 64.69 277.84
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 480.99 31.05 15.491 < 2e-16 ***
## PriceRelative -217.97 29.31 -7.436 4.93e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112 on 59 degrees of freedom
## Multiple R-squared: 0.4838, Adjusted R-squared: 0.4751
## F-statistic: 55.3 on 1 and 59 DF, p-value: 4.929e-10Jet$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96
## 376.36087 376.36087 376.36087 376.36087 409.05708 409.05708 409.05708
## 97 308 309 310 311 312 313
## 424.31532 154.02659 271.73297 302.24944 389.43935 389.43935 393.79885
## 314 379 380 381 382 383 384
## 398.15835 69.01642 69.01642 69.01642 73.37592 116.97088 123.51012
## 385 386 387 388 389 390 391
## 147.48735 199.80129 206.34054 206.34054 206.34054 239.03675 239.03675
## 392 393 394 395 396 397 398
## 239.03675 243.39625 249.93549 254.29499 254.29499 282.63171 304.42919
## 399 400 401 402 403 404 405
## 308.78868 319.68742 319.68742 319.68742 319.68742 372.00137 443.93305
## 440 441 442 443 444 445 446
## 108.25188 114.79113 114.79113 197.62155 197.62155 197.62155 197.62155
## 447 448 449 450 451 452 453
## 215.05953 247.75575 313.14818 313.14818 313.14818 339.30515 350.20389
## 454 455 456 457 458
## 354.56339 382.90011 382.90011 398.15835 454.83179cor(Jet$PricePremium,Jet$PriceRelative)## [1] -0.3214004fit<-lm(PriceEconomy~PercentPremiumSeats,data = Jet)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Jet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -180.93 -132.93 -33.66 74.34 376.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 172.81 51.82 3.335 0.00148 **
## PercentPremiumSeats 10.16 4.73 2.148 0.03586 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150.1 on 59 degrees of freedom
## Multiple R-squared: 0.07251, Adjusted R-squared: 0.05679
## F-statistic: 4.612 on 1 and 59 DF, p-value: 0.03586Jet$PricePremium## [1] 524 524 524 524 616 616 616 616 841 841 841 789 789 928 931 483 483
## [18] 483 398 398 520 534 318 267 267 267 228 228 228 620 483 318 318 620
## [35] 267 228 267 267 267 267 483 696 545 397 397 430 430 430 430 545 483
## [52] 304 304 304 483 451 464 550 550 696 569fitted(fit)## 90 91 92 93 94 95 96 97
## 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980 344.1980
## 308 309 310 311 312 313 314 379
## 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 299.8029 288.9327
## 380 381 382 383 384 385 386 387
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 388 389 390 391 392 393 394 395
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 396 397 398 399 400 401 402 403
## 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327 288.9327
## 404 405 440 441 442 443 444 445
## 288.9327 288.9327 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 446 447 448 449 450 451 452 453
## 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638 220.6638
## 454 455 456 457 458
## 220.6638 220.6638 220.6638 220.6638 220.6638cor(Jet$PricePremium,Jet$PercentPremiumSeats)## [1] 0.2057525AirFrance Airlines
Analyse all about AF Airlines:-
AF <- airline[ which(airline$Airline=='AirFrance'),]
View(AF)
summary(AF)## Airline Aircraft FlightDuration TravelMonth
## AirFrance:74 AirBus:36 Min. : 6.830 Aug:20
## British : 0 Boeing:38 1st Qu.: 7.770 Jul:12
## Delta : 0 Median : 8.750 Oct:20
## Jet : 0 Mean : 8.989 Sep:22
## Singapore: 0 3rd Qu.: 9.500
## Virgin : 0 Max. :13.000
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :147.0 Min. :21.0 Min. :32
## International:74 1st Qu.:147.0 1st Qu.:21.0 1st Qu.:32
## Median :200.0 Median :24.0 Median :32
## Mean :214.5 Mean :26.7 Mean :32
## 3rd Qu.:200.0 3rd Qu.:28.0 3rd Qu.:32
## Max. :389.0 Max. :38.0 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :17.00 Min. :19 Min. : 630 Min. :1611
## 1st Qu.:38 1st Qu.:17.00 1st Qu.:19 1st Qu.:2659 1st Qu.:2859
## Median :38 Median :18.00 Median :19 Median :2988 Median :3196
## Mean :38 Mean :17.57 Mean :19 Mean :2770 Mean :3065
## 3rd Qu.:38 3rd Qu.:18.00 3rd Qu.:19 3rd Qu.:3165 3rd Qu.:3289
## Max. :38 Max. :18.00 Max. :19 Max. :3593 Max. :3972
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0200 Min. :168.0 Min. :6 Min. :1.000
## 1st Qu.:0.0300 1st Qu.:168.0 1st Qu.:6 1st Qu.:1.000
## Median :0.0700 Median :228.0 Median :6 Median :1.000
## Mean :0.2047 Mean :241.2 Mean :6 Mean :1.432
## 3rd Qu.:0.0800 3rd Qu.:228.0 3rd Qu.:6 3rd Qu.:2.000
## Max. :1.6400 Max. :427.0 Max. :6 Max. :2.000
## PercentPremiumSeats
## Min. : 8.90
## 1st Qu.:12.12
## Median :12.28
## Mean :11.59
## 3rd Qu.:12.50
## Max. :12.50Check the all the means now all AF aircrafts
mean(AF$PriceEconomy)## [1] 2769.784mean(AF$PricePremium)## [1] 3065.216mean(AF$FlightDuration)## [1] 8.988514mean(AF$PitchEconomy)## [1] 32mean(AF$PitchPremium)## [1] 38mean(AF$WidthEconomy)## [1] 17.56757mean(AF$WidthPremium)## [1] 19mean(AF$PriceRelative)## [1] 0.2047297mean(AF$PitchDifference)## [1] 6mean(AF$WidthDifference)## [1] 1.432432Now Analyse separately for Each Aircrafts in AF Airlines i.e-Boeing and AirBus
AFboeing <- AF[ which(AF$Aircraft=='Boeing'),]
View(AFboeing)
summary(AFboeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance:38 AirBus: 0 Min. : 6.830 Aug:11
## British : 0 Boeing:38 1st Qu.: 7.750 Jul: 6
## Delta : 0 Median : 8.750 Oct:11
## Jet : 0 Mean : 9.117 Sep:10
## Singapore: 0 3rd Qu.:10.660
## Virgin : 0 Max. :11.910
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :174.0 Min. :24.00 Min. :32
## International:38 1st Qu.:200.0 1st Qu.:25.00 1st Qu.:32
## Median :200.0 Median :28.00 Median :32
## Mean :227.4 Mean :28.53 Mean :32
## 3rd Qu.:212.0 3rd Qu.:28.00 3rd Qu.:32
## Max. :389.0 Max. :38.00 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :17.00 Min. :19 Min. : 648 Min. :1710
## 1st Qu.:38 1st Qu.:17.00 1st Qu.:19 1st Qu.:2983 1st Qu.:3174
## Median :38 Median :17.00 Median :19 Median :3108 Median :3243
## Mean :38 Mean :17.16 Mean :19 Mean :2933 Mean :3221
## 3rd Qu.:38 3rd Qu.:17.00 3rd Qu.:19 3rd Qu.:3414 3rd Qu.:3573
## Max. :38 Max. :18.00 Max. :19 Max. :3593 Max. :3972
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0300 Min. :198.0 Min. :6 Min. :1.000
## 1st Qu.:0.0300 1st Qu.:228.0 1st Qu.:6 1st Qu.:2.000
## Median :0.0350 Median :228.0 Median :6 Median :2.000
## Mean :0.2016 Mean :255.9 Mean :6 Mean :1.842
## 3rd Qu.:0.0700 3rd Qu.:237.0 3rd Qu.:6 3rd Qu.:2.000
## Max. :1.6400 Max. :427.0 Max. :6 Max. :2.000
## PercentPremiumSeats
## Min. : 8.90
## 1st Qu.:10.53
## Median :12.28
## Mean :11.48
## 3rd Qu.:12.28
## Max. :12.28mean(AFboeing$PriceEconomy)## [1] 2933.289mean(AFboeing$PricePremium)## [1] 3221library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(AFboeing$PriceEconomy,AFboeing$TravelMonth,mean))
y2<-c(by(AFboeing$PricePremium,AFboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1742.3 -316.8 180.8 474.7 686.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 690.33 582.53 1.185 0.243756
## FlightDuration 246.01 62.77 3.919 0.000381 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 670.8 on 36 degrees of freedom
## Multiple R-squared: 0.2991, Adjusted R-squared: 0.2796
## F-statistic: 15.36 on 1 and 36 DF, p-value: 0.0003812AFboeing$PriceEconomy## [1] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [15] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [29] 648 648 700 1094 2996 2996 2996 2979 3593 3593fitted(fit)## 339 340 341 342 343 344 345 346
## 2739.589 2739.589 2739.589 2535.402 2370.575 2842.913 2842.913 2842.913
## 347 348 349 350 351 352 353 354
## 2574.763 2370.575 2370.575 3027.420 3027.420 3027.420 3027.420 2596.904
## 355 356 357 358 359 360 361 362
## 2596.904 2596.904 2616.585 3005.280 3005.280 3005.280 3580.941 3580.941
## 363 364 365 366 406 407 408 409
## 3620.303 3620.303 3620.303 3620.303 2390.256 2390.256 2390.256 2390.256
## 430 431 432 436 437 438
## 3312.791 3312.791 3312.791 2781.411 3519.439 3519.439cor(AFboeing$PriceEconomy,AFboeing$FlightDuration)## [1] 0.5468897fit<-lm(PriceEconomy~SeatsEconomy,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2279.80 -11.71 187.88 493.88 685.37
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2824.0399 436.5193 6.469 1.65e-07 ***
## SeatsEconomy 0.4804 1.8325 0.262 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 800.5 on 36 degrees of freedom
## Multiple R-squared: 0.001905, Adjusted R-squared: -0.02582
## F-statistic: 0.06872 on 1 and 36 DF, p-value: 0.7947AFboeing$PriceEconomy## [1] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [15] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [29] 648 648 700 1094 2996 2996 2996 2979 3593 3593fitted(fit)## 339 340 341 342 343 344 345 346
## 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117
## 347 348 349 350 351 352 353 354
## 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2907.627
## 355 356 357 358 359 360 361 362
## 2907.627 2907.627 2907.627 2920.117 2920.117 2920.117 2907.627 2907.627
## 363 364 365 366 406 407 408 409
## 2920.117 2920.117 2920.117 2920.117 2927.803 2927.803 2927.803 2927.803
## 430 431 432 436 437 438
## 3010.910 3010.910 3010.910 3010.910 3010.910 3010.910cor(AFboeing$PriceEconomy,AFboeing$SeatsEconomy)## [1] 0.04364893fit<-lm(PriceEconomy~PriceRelative,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1222.07 -149.37 -13.76 236.37 364.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3280.42 54.66 60.02 <2e-16 ***
## PriceRelative -1722.06 117.52 -14.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 303.6 on 36 degrees of freedom
## Multiple R-squared: 0.8564, Adjusted R-squared: 0.8524
## F-statistic: 214.7 on 1 and 36 DF, p-value: < 2.2e-16AFboeing$PriceEconomy## [1] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [15] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [29] 648 648 700 1094 2996 2996 2996 2979 3593 3593fitted(fit)## 339 340 341 342 343 344 345
## 2660.4794 2660.4794 2660.4794 3142.6554 3159.8759 3159.8759 3159.8759
## 346 347 348 349 350 351 352
## 3159.8759 3211.5377 3211.5377 3211.5377 3228.7582 3228.7582 3228.7582
## 353 354 355 356 357 358 359
## 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582
## 360 361 362 363 364 365 366
## 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582
## 406 407 408 409 430 431 432
## 456.2462 456.2462 800.6576 2316.0679 3159.8759 3159.8759 3159.8759
## 436 437 438
## 3211.5377 3228.7582 3228.7582cor(AFboeing$PriceEconomy,AFboeing$PriceRelative)## [1] -0.9254236fit<-lm(PriceEconomy~PercentPremiumSeats,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1965.98 -79.54 98.71 374.46 1216.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 458.02 1068.56 0.429 0.6707
## PercentPremiumSeats 215.60 92.47 2.332 0.0254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 746.9 on 36 degrees of freedom
## Multiple R-squared: 0.1312, Adjusted R-squared: 0.1071
## F-statistic: 5.436 on 1 and 36 DF, p-value: 0.02544AFboeing$PriceEconomy## [1] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [15] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [29] 648 648 700 1094 2996 2996 2996 2979 3593 3593fitted(fit)## 339 340 341 342 343 344 345 346
## 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539
## 347 348 349 350 351 352 353 354
## 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3071.044
## 355 356 357 358 359 360 361 362
## 3071.044 3071.044 3071.044 3105.539 3105.539 3105.539 3071.044 3071.044
## 363 364 365 366 406 407 408 409
## 3105.539 3105.539 3105.539 3105.539 2613.980 2613.980 2613.980 2613.980
## 430 431 432 436 437 438
## 2376.824 2376.824 2376.824 2376.824 2376.824 2376.824cor(AFboeing$PriceEconomy,AFboeing$PercentPremiumSeats)## [1] 0.3621991fit<-lm(PricePremium~FlightDuration,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1158.8 -271.1 100.9 310.9 876.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1766.45 455.55 3.878 0.00043 ***
## FlightDuration 159.54 49.09 3.250 0.00250 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 524.6 on 36 degrees of freedom
## Multiple R-squared: 0.2269, Adjusted R-squared: 0.2054
## F-statistic: 10.56 on 1 and 36 DF, p-value: 0.002504AFboeing$PricePremium## [1] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [15] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [29] 1710 1710 1710 1710 3196 3196 3196 3088 3702 3702fitted(fit)## 339 340 341 342 343 344 345 346
## 3095.387 3095.387 3095.387 2962.972 2856.083 3162.392 3162.392 3162.392
## 347 348 349 350 351 352 353 354
## 2988.498 2856.083 2856.083 3282.043 3282.043 3282.043 3282.043 3002.856
## 355 356 357 358 359 360 361 362
## 3002.856 3002.856 3015.619 3267.685 3267.685 3267.685 3640.999 3640.999
## 363 364 365 366 406 407 408 409
## 3666.525 3666.525 3666.525 3666.525 2868.846 2868.846 2868.846 2868.846
## 430 431 432 436 437 438
## 3467.105 3467.105 3467.105 3122.508 3601.115 3601.115cor(AFboeing$PricePremium,AFboeing$FlightDuration)## [1] 0.4762987fit<-lm(PriceEconomy~SeatsEconomy,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2279.80 -11.71 187.88 493.88 685.37
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2824.0399 436.5193 6.469 1.65e-07 ***
## SeatsEconomy 0.4804 1.8325 0.262 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 800.5 on 36 degrees of freedom
## Multiple R-squared: 0.001905, Adjusted R-squared: -0.02582
## F-statistic: 0.06872 on 1 and 36 DF, p-value: 0.7947AFboeing$PricePremium## [1] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [15] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [29] 1710 1710 1710 1710 3196 3196 3196 3088 3702 3702fitted(fit)## 339 340 341 342 343 344 345 346
## 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117
## 347 348 349 350 351 352 353 354
## 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2920.117 2907.627
## 355 356 357 358 359 360 361 362
## 2907.627 2907.627 2907.627 2920.117 2920.117 2920.117 2907.627 2907.627
## 363 364 365 366 406 407 408 409
## 2920.117 2920.117 2920.117 2920.117 2927.803 2927.803 2927.803 2927.803
## 430 431 432 436 437 438
## 3010.910 3010.910 3010.910 3010.910 3010.910 3010.910cor(AFboeing$PricePremium,AFboeing$SeatsEconomy)## [1] 0.006377831fit<-lm(PriceEconomy~SeatsPremium,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2041.78 -30.47 152.03 509.03 903.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1398.63 804.38 1.739 0.0906 .
## SeatsPremium 53.80 27.86 1.931 0.0614 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 762.8 on 36 degrees of freedom
## Multiple R-squared: 0.09384, Adjusted R-squared: 0.06867
## F-statistic: 3.728 on 1 and 36 DF, p-value: 0.0614AFboeing$PricePremium## [1] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [15] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [29] 1710 1710 1710 1710 3196 3196 3196 3088 3702 3702fitted(fit)## 339 340 341 342 343 344 345 346
## 2904.975 2904.975 2904.975 2904.975 2904.975 2904.975 2904.975 2904.975
## 347 348 349 350 351 352 353 354
## 2904.975 2904.975 2904.975 2904.975 2904.975 2904.975 2904.975 2689.783
## 355 356 357 358 359 360 361 362
## 2689.783 2689.783 2689.783 2904.975 2904.975 2904.975 2689.783 2689.783
## 363 364 365 366 406 407 408 409
## 2904.975 2904.975 2904.975 2904.975 2689.783 2689.783 2689.783 2689.783
## 430 431 432 436 437 438
## 3442.955 3442.955 3442.955 3442.955 3442.955 3442.955cor(AFboeing$PricePremium,AFboeing$SeatsPremium)## [1] 0.2673746fit<-lm(PriceEconomy~PriceRelative,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1222.07 -149.37 -13.76 236.37 364.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3280.42 54.66 60.02 <2e-16 ***
## PriceRelative -1722.06 117.52 -14.65 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 303.6 on 36 degrees of freedom
## Multiple R-squared: 0.8564, Adjusted R-squared: 0.8524
## F-statistic: 214.7 on 1 and 36 DF, p-value: < 2.2e-16AFboeing$PricePremium## [1] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [15] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [29] 1710 1710 1710 1710 3196 3196 3196 3088 3702 3702fitted(fit)## 339 340 341 342 343 344 345
## 2660.4794 2660.4794 2660.4794 3142.6554 3159.8759 3159.8759 3159.8759
## 346 347 348 349 350 351 352
## 3159.8759 3211.5377 3211.5377 3211.5377 3228.7582 3228.7582 3228.7582
## 353 354 355 356 357 358 359
## 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582
## 360 361 362 363 364 365 366
## 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582 3228.7582
## 406 407 408 409 430 431 432
## 456.2462 456.2462 800.6576 2316.0679 3159.8759 3159.8759 3159.8759
## 436 437 438
## 3211.5377 3228.7582 3228.7582cor(AFboeing$PricePremium,AFboeing$PriceRelative)## [1] -0.7662207fit<-lm(PriceEconomy~PercentPremiumSeats,data = AFboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = AFboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1965.98 -79.54 98.71 374.46 1216.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 458.02 1068.56 0.429 0.6707
## PercentPremiumSeats 215.60 92.47 2.332 0.0254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 746.9 on 36 degrees of freedom
## Multiple R-squared: 0.1312, Adjusted R-squared: 0.1071
## F-statistic: 5.436 on 1 and 36 DF, p-value: 0.02544AFboeing$PricePremium## [1] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [15] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [29] 1710 1710 1710 1710 3196 3196 3196 3088 3702 3702fitted(fit)## 339 340 341 342 343 344 345 346
## 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539
## 347 348 349 350 351 352 353 354
## 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3105.539 3071.044
## 355 356 357 358 359 360 361 362
## 3071.044 3071.044 3071.044 3105.539 3105.539 3105.539 3071.044 3071.044
## 363 364 365 366 406 407 408 409
## 3105.539 3105.539 3105.539 3105.539 2613.980 2613.980 2613.980 2613.980
## 430 431 432 436 437 438
## 2376.824 2376.824 2376.824 2376.824 2376.824 2376.824cor(AFboeing$PricePremium,AFboeing$PercentPremiumSeats)## [1] 0.3808213AFairbus <-AF[ which(AF$Aircraft=='AirBus'),]
View(AFairbus)
summary(AFairbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance:36 AirBus:36 Min. : 6.830 Aug: 9
## British : 0 Boeing: 0 1st Qu.: 8.330 Jul: 6
## Delta : 0 Median : 8.500 Oct: 9
## Jet : 0 Mean : 8.852 Sep:12
## Singapore: 0 3rd Qu.: 9.250
## Virgin : 0 Max. :13.000
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :147.0 Min. :21.00 Min. :32
## International:36 1st Qu.:147.0 1st Qu.:21.00 1st Qu.:32
## Median :147.0 Median :21.00 Median :32
## Mean :200.8 Mean :24.78 Mean :32
## 3rd Qu.:147.0 3rd Qu.:21.00 3rd Qu.:32
## Max. :389.0 Max. :38.00 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :18 Min. :19 Min. : 630 Min. :1611
## 1st Qu.:38 1st Qu.:18 1st Qu.:19 1st Qu.:2607 1st Qu.:2807
## Median :38 Median :18 Median :19 Median :2659 Median :2859
## Mean :38 Mean :18 Mean :19 Mean :2597 Mean :2901
## 3rd Qu.:38 3rd Qu.:18 3rd Qu.:19 3rd Qu.:3026 3rd Qu.:3275
## Max. :38 Max. :18 Max. :19 Max. :3220 Max. :3289
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0200 Min. :168.0 Min. :6 Min. :1
## 1st Qu.:0.0375 1st Qu.:168.0 1st Qu.:6 1st Qu.:1
## Median :0.0750 Median :168.0 Median :6 Median :1
## Mean :0.2081 Mean :225.6 Mean :6 Mean :1
## 3rd Qu.:0.0800 3rd Qu.:168.0 3rd Qu.:6 3rd Qu.:1
## Max. :1.5600 Max. :427.0 Max. :6 Max. :1
## PercentPremiumSeats
## Min. : 8.9
## 1st Qu.:12.5
## Median :12.5
## Mean :11.7
## 3rd Qu.:12.5
## Max. :12.5mean(AFairbus$PriceEconomy)## [1] 2597.194mean(AFairbus$PricePremium)## [1] 2900.778library(plotly)
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(AFairbus$PriceEconomy,AFairbus$TravelMonth,mean))
y4<-c(by(AFairbus$PricePremium,AFairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1880.62 -193.76 -7.83 403.24 1177.35
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3780.82 671.55 5.630 2.61e-06 ***
## FlightDuration -133.71 74.86 -1.786 0.083 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 652.3 on 34 degrees of freedom
## Multiple R-squared: 0.08578, Adjusted R-squared: 0.05889
## F-statistic: 3.19 on 1 and 34 DF, p-value: 0.08301AFairbus$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2510.620 2510.620 2510.620 2667.056 2667.056 2667.056 2667.056 2667.056
## 220 221 222 223 224 225 226 227
## 2667.056 2667.056 2667.056 2800.761 2800.761 2800.761 2867.614 2867.614
## 228 229 230 231 232 233 234 235
## 2589.506 2589.506 2589.506 2700.482 2553.406 2553.406 2544.046 2544.046
## 236 237 238 239 426 427 428 429
## 2556.080 2556.080 2544.046 2544.046 2042.650 2042.650 2778.031 2778.031
## 433 434 435 439
## 2656.359 2644.326 2644.326 2042.650cor(AFairbus$PriceEconomy,AFairbus$FlightDuration)## [1] -0.2928807fit<-lm(PriceEconomy~SeatsEconomy,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1982.0 -3.0 47.0 463.5 674.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2652.4706 253.5784 10.460 3.64e-12 ***
## SeatsEconomy -0.2753 1.1291 -0.244 0.809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 681.6 on 34 degrees of freedom
## Multiple R-squared: 0.001745, Adjusted R-squared: -0.02761
## F-statistic: 0.05945 on 1 and 34 DF, p-value: 0.8088AFairbus$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000
## 220 221 222 223 224 225 226 227
## 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000
## 228 229 230 231 232 233 234 235
## 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000
## 236 237 238 239 426 427 428 429
## 2612.000 2612.000 2612.000 2612.000 2545.375 2545.375 2545.375 2545.375
## 433 434 435 439
## 2545.375 2545.375 2545.375 2545.375cor(AFairbus$PriceEconomy,AFairbus$SeatsEconomy)## [1] -0.04177842fit<-lm(PriceEconomy~PriceRelative,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -952.42 -188.91 -90.95 291.50 402.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2920.05 53.16 54.93 < 2e-16 ***
## PriceRelative -1551.80 120.45 -12.88 1.23e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 281.3 on 34 degrees of freedom
## Multiple R-squared: 0.83, Adjusted R-squared: 0.825
## F-statistic: 166 on 1 and 34 DF, p-value: 1.229e-14AFairbus$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 212 213 214 215 216 217 218
## 499.2507 1104.4517 1942.4222 2795.9107 2795.9107 2795.9107 2795.9107
## 219 220 221 222 223 224 225
## 2795.9107 2795.9107 2795.9107 2795.9107 2795.9107 2795.9107 2795.9107
## 226 227 228 229 230 231 232
## 2811.4287 2811.4287 2811.4287 2811.4287 2811.4287 2857.9826 2873.5006
## 233 234 235 236 237 238 239
## 2873.5006 2873.5006 2873.5006 2873.5006 2873.5006 2873.5006 2873.5006
## 426 427 428 429 433 434 435
## 1119.9696 1119.9696 2795.9107 2795.9107 2857.9826 2857.9826 2857.9826
## 439
## 2889.0185cor(AFairbus$PriceEconomy,AFairbus$PriceRelative)## [1] -0.911031fit<-lm(PriceEconomy~PercentPremiumSeats,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1982.0 -3.0 47.0 463.5 674.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2380.66 895.31 2.659 0.0119 *
## PercentPremiumSeats 18.51 75.90 0.244 0.8088
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 681.6 on 34 degrees of freedom
## Multiple R-squared: 0.001745, Adjusted R-squared: -0.02761
## F-statistic: 0.05945 on 1 and 34 DF, p-value: 0.8088AFairbus$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000
## 220 221 222 223 224 225 226 227
## 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000
## 228 229 230 231 232 233 234 235
## 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000 2612.000
## 236 237 238 239 426 427 428 429
## 2612.000 2612.000 2612.000 2612.000 2545.375 2545.375 2545.375 2545.375
## 433 434 435 439
## 2545.375 2545.375 2545.375 2545.375fit<-lm(PricePremium~FlightDuration,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1318.90 -25.29 -18.28 253.20 360.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2502.59 456.18 5.486 4.01e-06 ***
## FlightDuration 44.98 50.85 0.885 0.383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 443.1 on 34 degrees of freedom
## Multiple R-squared: 0.02249, Adjusted R-squared: -0.006256
## F-statistic: 0.7824 on 1 and 34 DF, p-value: 0.3826AFairbus$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2929.903 2929.903 2929.903 2877.275 2877.275 2877.275 2877.275 2877.275
## 220 221 222 223 224 225 226 227
## 2877.275 2877.275 2877.275 2832.295 2832.295 2832.295 2809.805 2809.805
## 228 229 230 231 232 233 234 235
## 2903.364 2903.364 2903.364 2866.030 2915.509 2915.509 2918.658 2918.658
## 236 237 238 239 426 427 428 429
## 2914.609 2914.609 2918.658 2918.658 3087.335 3087.335 2839.942 2839.942
## 433 434 435 439
## 2880.874 2884.922 2884.922 3087.335cor(AFairbus$PricePremium,AFairbus$FlightDuration)## [1] 0.149982fit<-lm(PricePremium~SeatsEconomy,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1236.68 -40.68 11.32 215.32 427.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2702.5334 162.3369 16.648 <2e-16 ***
## SeatsEconomy 0.9874 0.7229 1.366 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 436.4 on 34 degrees of freedom
## Multiple R-squared: 0.05202, Adjusted R-squared: 0.02414
## F-statistic: 1.866 on 1 and 34 DF, p-value: 0.1809AFairbus$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 220 221 222 223 224 225 226 227
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 228 229 230 231 232 233 234 235
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 236 237 238 239 426 427 428 429
## 2847.679 2847.679 2847.679 2847.679 3086.625 3086.625 3086.625 3086.625
## 433 434 435 439
## 3086.625 3086.625 3086.625 3086.625cor(AFairbus$PricePremium,AFairbus$SeatsEconomy)## [1] 0.2280809fit<-lm(PricePremium~SeatsPremium,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1236.68 -40.68 11.32 215.32 427.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2552.51 265.14 9.627 3.06e-11 ***
## SeatsPremium 14.06 10.29 1.366 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 436.4 on 34 degrees of freedom
## Multiple R-squared: 0.05202, Adjusted R-squared: 0.02414
## F-statistic: 1.866 on 1 and 34 DF, p-value: 0.1809AFairbus$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 220 221 222 223 224 225 226 227
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 228 229 230 231 232 233 234 235
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 236 237 238 239 426 427 428 429
## 2847.679 2847.679 2847.679 2847.679 3086.625 3086.625 3086.625 3086.625
## 433 434 435 439
## 3086.625 3086.625 3086.625 3086.625cor(AFairbus$PricePremium,AFairbus$SeatsPremium)## [1] 0.2280809fit<-lm(PricePremium~PriceRelative,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1028.2 -173.2 -121.2 263.8 978.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3029.78 70.51 42.972 < 2e-16 ***
## PriceRelative -620.04 159.75 -3.881 0.000454 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 373.1 on 34 degrees of freedom
## Multiple R-squared: 0.307, Adjusted R-squared: 0.2867
## F-statistic: 15.06 on 1 and 34 DF, p-value: 0.0004545AFairbus$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2062.518 2304.334 2639.155 2980.177 2980.177 2980.177 2980.177 2980.177
## 220 221 222 223 224 225 226 227
## 2980.177 2980.177 2980.177 2980.177 2980.177 2980.177 2986.378 2986.378
## 228 229 230 231 232 233 234 235
## 2986.378 2986.378 2986.378 3004.979 3011.179 3011.179 3011.179 3011.179
## 236 237 238 239 426 427 428 429
## 3011.179 3011.179 3011.179 3011.179 2310.534 2310.534 2980.177 2980.177
## 433 434 435 439
## 3004.979 3004.979 3004.979 3017.380cor(AFairbus$PricePremium,AFairbus$PriceRelative)## [1] -0.5541042fit<-lm(PricePremium~PercentPremiumSeats,data = AFairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = AFairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1236.68 -40.68 11.32 215.32 427.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3677.35 573.16 6.416 2.5e-07 ***
## PercentPremiumSeats -66.37 48.59 -1.366 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 436.4 on 34 degrees of freedom
## Multiple R-squared: 0.05202, Adjusted R-squared: 0.02414
## F-statistic: 1.866 on 1 and 34 DF, p-value: 0.1809AFairbus$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 220 221 222 223 224 225 226 227
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 228 229 230 231 232 233 234 235
## 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679 2847.679
## 236 237 238 239 426 427 428 429
## 2847.679 2847.679 2847.679 2847.679 3086.625 3086.625 3086.625 3086.625
## 433 434 435 439
## 3086.625 3086.625 3086.625 3086.625cor(AFairbus$PricePremium,AFairbus$PercentPremiumSeats)## [1] -0.2280809Now We Should Analyse the international aircrafts of AirFrance
AFint <- AF[ which(AF$IsInternational=='International'),]
View(AFint)
summary(AFint)## Airline Aircraft FlightDuration TravelMonth
## AirFrance:74 AirBus:36 Min. : 6.830 Aug:20
## British : 0 Boeing:38 1st Qu.: 7.770 Jul:12
## Delta : 0 Median : 8.750 Oct:20
## Jet : 0 Mean : 8.989 Sep:22
## Singapore: 0 3rd Qu.: 9.500
## Virgin : 0 Max. :13.000
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :147.0 Min. :21.0 Min. :32
## International:74 1st Qu.:147.0 1st Qu.:21.0 1st Qu.:32
## Median :200.0 Median :24.0 Median :32
## Mean :214.5 Mean :26.7 Mean :32
## 3rd Qu.:200.0 3rd Qu.:28.0 3rd Qu.:32
## Max. :389.0 Max. :38.0 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :17.00 Min. :19 Min. : 630 Min. :1611
## 1st Qu.:38 1st Qu.:17.00 1st Qu.:19 1st Qu.:2659 1st Qu.:2859
## Median :38 Median :18.00 Median :19 Median :2988 Median :3196
## Mean :38 Mean :17.57 Mean :19 Mean :2770 Mean :3065
## 3rd Qu.:38 3rd Qu.:18.00 3rd Qu.:19 3rd Qu.:3165 3rd Qu.:3289
## Max. :38 Max. :18.00 Max. :19 Max. :3593 Max. :3972
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0200 Min. :168.0 Min. :6 Min. :1.000
## 1st Qu.:0.0300 1st Qu.:168.0 1st Qu.:6 1st Qu.:1.000
## Median :0.0700 Median :228.0 Median :6 Median :1.000
## Mean :0.2047 Mean :241.2 Mean :6 Mean :1.432
## 3rd Qu.:0.0800 3rd Qu.:228.0 3rd Qu.:6 3rd Qu.:2.000
## Max. :1.6400 Max. :427.0 Max. :6 Max. :2.000
## PercentPremiumSeats
## Min. : 8.90
## 1st Qu.:12.12
## Median :12.28
## Mean :11.59
## 3rd Qu.:12.50
## Max. :12.50mean(AFint$PriceEconomy)## [1] 2769.784mean(AFint$PricePremium)## [1] 3065.216library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(AFint$PriceEconomy,AFint$TravelMonth,mean))
y2<-c(by(AFint$PricePremium,AFint$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2192.13 -43.38 215.62 377.66 667.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1849.8 485.8 3.808 0.000292 ***
## FlightDuration 102.3 53.2 1.924 0.058344 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 736.2 on 72 degrees of freedom
## Multiple R-squared: 0.04889, Adjusted R-squared: 0.03568
## F-statistic: 3.701 on 1 and 72 DF, p-value: 0.05834AFint$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2822.135 2822.135 2822.135 2702.384 2702.384 2702.384 2702.384 2702.384
## 220 221 222 223 224 225 226 227
## 2702.384 2702.384 2702.384 2600.034 2600.034 2600.034 2548.858 2548.858
## 228 229 230 231 232 233 234 235
## 2761.748 2761.748 2761.748 2676.797 2789.383 2789.383 2796.547 2796.547
## 236 237 238 239 339 340 341 342
## 2787.336 2787.336 2796.547 2796.547 2702.384 2702.384 2702.384 2617.433
## 343 344 345 346 347 348 349 350
## 2548.858 2745.372 2745.372 2745.372 2633.809 2548.858 2548.858 2822.135
## 351 352 353 354 355 356 357 358
## 2822.135 2822.135 2822.135 2643.021 2643.021 2643.021 2651.209 2812.923
## 359 360 361 362 363 364 365 366
## 2812.923 2812.923 3052.424 3052.424 3068.800 3068.800 3068.800 3068.800
## 406 407 408 409 426 427 428 429
## 2557.046 2557.046 2557.046 2557.046 3180.362 3180.362 2617.433 2617.433
## 430 431 432 433 434 435 436 437
## 2940.862 2940.862 2940.862 2710.573 2719.784 2719.784 2719.784 3026.836
## 438 439
## 3026.836 3180.362cor(AFint$PriceEconomy,AFint$FlightDuration)## [1] 0.2211007fit<-lm(PriceEconomy~SeatsEconomy,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2122.21 -92.16 169.54 413.84 834.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2710.583 231.850 11.691 <2e-16 ***
## SeatsEconomy 0.276 1.001 0.276 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 754.5 on 72 degrees of freedom
## Multiple R-squared: 0.001056, Adjusted R-squared: -0.01282
## F-statistic: 0.07609 on 1 and 72 DF, p-value: 0.7835AFint$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 220 221 222 223 224 225 226 227
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 228 229 230 231 232 233 234 235
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 236 237 238 239 339 340 341 342
## 2751.162 2751.162 2751.162 2751.162 2765.792 2765.792 2765.792 2765.792
## 343 344 345 346 347 348 349 350
## 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792
## 351 352 353 354 355 356 357 358
## 2765.792 2765.792 2765.792 2758.615 2758.615 2758.615 2758.615 2765.792
## 359 360 361 362 363 364 365 366
## 2765.792 2765.792 2758.615 2758.615 2765.792 2765.792 2765.792 2765.792
## 406 407 408 409 426 427 428 429
## 2770.209 2770.209 2770.209 2770.209 2817.965 2817.965 2817.965 2817.965
## 430 431 432 433 434 435 436 437
## 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965
## 438 439
## 2817.965 2817.965cor(AFint$PriceEconomy,AFint$SeatsEconomy)## [1] 0.03249046fit<-lm(PriceEconomy~PriceRelative,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1090.05 -316.43 34.09 157.14 535.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3107.32 43.57 71.32 <2e-16 ***
## PriceRelative -1648.71 96.03 -17.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 334.5 on 72 degrees of freedom
## Multiple R-squared: 0.8037, Adjusted R-squared: 0.801
## F-statistic: 294.8 on 1 and 72 DF, p-value: < 2.2e-16AFint$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218
## 535.3341 1178.3316 2068.6358 2975.4271 2975.4271 2975.4271 2975.4271
## 219 220 221 222 223 224 225
## 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271
## 226 227 228 229 230 231 232
## 2991.9142 2991.9142 2991.9142 2991.9142 2991.9142 3041.3756 3057.8627
## 233 234 235 236 237 238 239
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 339 340 341 342 343 344 345
## 2513.7879 2513.7879 2513.7879 2975.4271 2991.9142 2991.9142 2991.9142
## 346 347 348 349 350 351 352
## 2991.9142 3041.3756 3041.3756 3041.3756 3057.8627 3057.8627 3057.8627
## 353 354 355 356 357 358 359
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 360 361 362 363 364 365 366
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 406 407 408 409 426 427 428
## 403.4371 403.4371 733.1795 2184.0456 1194.8187 1194.8187 2975.4271
## 429 430 431 432 433 434 435
## 2975.4271 2991.9142 2991.9142 2991.9142 3041.3756 3041.3756 3041.3756
## 436 437 438 439
## 3041.3756 3057.8627 3057.8627 3074.3498cor(AFint$PriceEconomy,AFint$PriceRelative)## [1] -0.8964835fit<-lm(PriceEconomy~PercentPremiumSeats,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2228.1 -199.1 220.2 450.3 1083.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1648.88 715.06 2.306 0.024 *
## PercentPremiumSeats 96.73 61.26 1.579 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 742.1 on 72 degrees of freedom
## Multiple R-squared: 0.03347, Adjusted R-squared: 0.02005
## F-statistic: 2.494 on 1 and 72 DF, p-value: 0.1187AFint$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 220 221 222 223 224 225 226 227
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 228 229 230 231 232 233 234 235
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 236 237 238 239 339 340 341 342
## 2858.047 2858.047 2858.047 2858.047 2836.765 2836.765 2836.765 2836.765
## 343 344 345 346 347 348 349 350
## 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765
## 351 352 353 354 355 356 357 358
## 2836.765 2836.765 2836.765 2821.288 2821.288 2821.288 2821.288 2836.765
## 359 360 361 362 363 364 365 366
## 2836.765 2836.765 2821.288 2821.288 2836.765 2836.765 2836.765 2836.765
## 406 407 408 409 426 427 428 429
## 2616.213 2616.213 2616.213 2616.213 2509.805 2509.805 2509.805 2509.805
## 430 431 432 433 434 435 436 437
## 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805
## 438 439
## 2509.805 2509.805cor(AFint$PriceEconomy,AFint$PercentPremiumSeats)## [1] 0.1829589fit<-lm(PricePremium~FlightDuration,data = AFint)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1516.2 -126.4 82.0 302.1 986.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1975.68 336.57 5.870 1.23e-07 ***
## FlightDuration 121.21 36.86 3.289 0.00156 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 510 on 72 degrees of freedom
## Multiple R-squared: 0.1306, Adjusted R-squared: 0.1185
## F-statistic: 10.82 on 1 and 72 DF, p-value: 0.001559AFint$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 3127.216 3127.216 3127.216 2985.395 2985.395 2985.395 2985.395 2985.395
## 220 221 222 223 224 225 226 227
## 2985.395 2985.395 2985.395 2864.181 2864.181 2864.181 2803.573 2803.573
## 228 229 230 231 232 233 234 235
## 3055.699 3055.699 3055.699 2955.091 3088.427 3088.427 3096.912 3096.912
## 236 237 238 239 339 340 341 342
## 3086.003 3086.003 3096.912 3096.912 2985.395 2985.395 2985.395 2884.787
## 343 344 345 346 347 348 349 350
## 2803.573 3036.305 3036.305 3036.305 2904.181 2803.573 2803.573 3127.216
## 351 352 353 354 355 356 357 358
## 3127.216 3127.216 3127.216 2915.091 2915.091 2915.091 2924.788 3116.306
## 359 360 361 362 363 364 365 366
## 3116.306 3116.306 3399.948 3399.948 3419.342 3419.342 3419.342 3419.342
## 406 407 408 409 426 427 428 429
## 2813.271 2813.271 2813.271 2813.271 3551.466 3551.466 2884.787 2884.787
## 430 431 432 433 434 435 436 437
## 3267.824 3267.824 3267.824 2995.092 3006.001 3006.001 3006.001 3369.644
## 438 439
## 3369.644 3551.466cor(AFint$PricePremium,AFint$FlightDuration)## [1] 0.3613764fit<-lm(PriceEconomy~SeatsEconomy,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2122.21 -92.16 169.54 413.84 834.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2710.583 231.850 11.691 <2e-16 ***
## SeatsEconomy 0.276 1.001 0.276 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 754.5 on 72 degrees of freedom
## Multiple R-squared: 0.001056, Adjusted R-squared: -0.01282
## F-statistic: 0.07609 on 1 and 72 DF, p-value: 0.7835AFint$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 220 221 222 223 224 225 226 227
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 228 229 230 231 232 233 234 235
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 236 237 238 239 339 340 341 342
## 2751.162 2751.162 2751.162 2751.162 2765.792 2765.792 2765.792 2765.792
## 343 344 345 346 347 348 349 350
## 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792
## 351 352 353 354 355 356 357 358
## 2765.792 2765.792 2765.792 2758.615 2758.615 2758.615 2758.615 2765.792
## 359 360 361 362 363 364 365 366
## 2765.792 2765.792 2758.615 2758.615 2765.792 2765.792 2765.792 2765.792
## 406 407 408 409 426 427 428 429
## 2770.209 2770.209 2770.209 2770.209 2817.965 2817.965 2817.965 2817.965
## 430 431 432 433 434 435 436 437
## 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965
## 438 439
## 2817.965 2817.965cor(AFint$PricePremium,AFint$SeatsEconomy)## [1] 0.1507589fit<-lm(PriceEconomy~SeatsPremium,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2067.3 -18.4 140.1 507.5 877.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2231.79 384.95 5.798 1.66e-07 ***
## SeatsPremium 20.15 14.05 1.434 0.156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 744.3 on 72 degrees of freedom
## Multiple R-squared: 0.02778, Adjusted R-squared: 0.01427
## F-statistic: 2.057 on 1 and 72 DF, p-value: 0.1558AFint$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889
## 220 221 222 223 224 225 226 227
## 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889
## 228 229 230 231 232 233 234 235
## 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889
## 236 237 238 239 339 340 341 342
## 2654.889 2654.889 2654.889 2654.889 2795.921 2795.921 2795.921 2795.921
## 343 344 345 346 347 348 349 350
## 2795.921 2795.921 2795.921 2795.921 2795.921 2795.921 2795.921 2795.921
## 351 352 353 354 355 356 357 358
## 2795.921 2795.921 2795.921 2715.331 2715.331 2715.331 2715.331 2795.921
## 359 360 361 362 363 364 365 366
## 2795.921 2795.921 2715.331 2715.331 2795.921 2795.921 2795.921 2795.921
## 406 407 408 409 426 427 428 429
## 2715.331 2715.331 2715.331 2715.331 2997.396 2997.396 2997.396 2997.396
## 430 431 432 433 434 435 436 437
## 2997.396 2997.396 2997.396 2997.396 2997.396 2997.396 2997.396 2997.396
## 438 439
## 2997.396 2997.396cor(AFint$PricePremium,AFint$SeatsPremium)## [1] 0.2995749fit<-lm(PriceEconomy~PriceRelative,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1090.05 -316.43 34.09 157.14 535.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3107.32 43.57 71.32 <2e-16 ***
## PriceRelative -1648.71 96.03 -17.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 334.5 on 72 degrees of freedom
## Multiple R-squared: 0.8037, Adjusted R-squared: 0.801
## F-statistic: 294.8 on 1 and 72 DF, p-value: < 2.2e-16AFint$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218
## 535.3341 1178.3316 2068.6358 2975.4271 2975.4271 2975.4271 2975.4271
## 219 220 221 222 223 224 225
## 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271
## 226 227 228 229 230 231 232
## 2991.9142 2991.9142 2991.9142 2991.9142 2991.9142 3041.3756 3057.8627
## 233 234 235 236 237 238 239
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 339 340 341 342 343 344 345
## 2513.7879 2513.7879 2513.7879 2975.4271 2991.9142 2991.9142 2991.9142
## 346 347 348 349 350 351 352
## 2991.9142 3041.3756 3041.3756 3041.3756 3057.8627 3057.8627 3057.8627
## 353 354 355 356 357 358 359
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 360 361 362 363 364 365 366
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 406 407 408 409 426 427 428
## 403.4371 403.4371 733.1795 2184.0456 1194.8187 1194.8187 2975.4271
## 429 430 431 432 433 434 435
## 2975.4271 2991.9142 2991.9142 2991.9142 3041.3756 3041.3756 3041.3756
## 436 437 438 439
## 3041.3756 3057.8627 3057.8627 3074.3498cor(AFint$PricePremium,AFint$PriceRelative)## [1] -0.6500079fit<-lm(PriceEconomy~PercentPremiumSeats,data = AFint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = AFint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2228.1 -199.1 220.2 450.3 1083.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1648.88 715.06 2.306 0.024 *
## PercentPremiumSeats 96.73 61.26 1.579 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 742.1 on 72 degrees of freedom
## Multiple R-squared: 0.03347, Adjusted R-squared: 0.02005
## F-statistic: 2.494 on 1 and 72 DF, p-value: 0.1187AFint$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 220 221 222 223 224 225 226 227
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 228 229 230 231 232 233 234 235
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 236 237 238 239 339 340 341 342
## 2858.047 2858.047 2858.047 2858.047 2836.765 2836.765 2836.765 2836.765
## 343 344 345 346 347 348 349 350
## 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765
## 351 352 353 354 355 356 357 358
## 2836.765 2836.765 2836.765 2821.288 2821.288 2821.288 2821.288 2836.765
## 359 360 361 362 363 364 365 366
## 2836.765 2836.765 2821.288 2821.288 2836.765 2836.765 2836.765 2836.765
## 406 407 408 409 426 427 428 429
## 2616.213 2616.213 2616.213 2616.213 2509.805 2509.805 2509.805 2509.805
## 430 431 432 433 434 435 436 437
## 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805
## 438 439
## 2509.805 2509.805cor(AFint$PricePremium,AFint$PercentPremiumSeats)## [1] 0.0775941Now It’s time for comparison-
par(mfrow=c(1, 2))
main="Boeing vs AirBus"
library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(AFboeing$PriceEconomy,AFboeing$TravelMonth,mean))
y2<-c(by(AFboeing$PricePremium,AFboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(AFairbus$PriceEconomy,AFairbus$TravelMonth,mean))
y4<-c(by(AFairbus$PricePremium,AFairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(main="mean prices of economy & premium tickets in Boeing",data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Boeing)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'plot_ly(main="mean prices of economy & premium tickets in Airbus"
,data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Airbus)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'short Analysis of Delta Airlines
mean(AF$PriceEconomy)## [1] 2769.784mean(AF$PricePremium)## [1] 3065.216library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(AF$PriceEconomy,AF$TravelMonth,mean))
y2<-c(by(AF$PricePremium,AF$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2192.13 -43.38 215.62 377.66 667.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1849.8 485.8 3.808 0.000292 ***
## FlightDuration 102.3 53.2 1.924 0.058344 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 736.2 on 72 degrees of freedom
## Multiple R-squared: 0.04889, Adjusted R-squared: 0.03568
## F-statistic: 3.701 on 1 and 72 DF, p-value: 0.05834AF$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2822.135 2822.135 2822.135 2702.384 2702.384 2702.384 2702.384 2702.384
## 220 221 222 223 224 225 226 227
## 2702.384 2702.384 2702.384 2600.034 2600.034 2600.034 2548.858 2548.858
## 228 229 230 231 232 233 234 235
## 2761.748 2761.748 2761.748 2676.797 2789.383 2789.383 2796.547 2796.547
## 236 237 238 239 339 340 341 342
## 2787.336 2787.336 2796.547 2796.547 2702.384 2702.384 2702.384 2617.433
## 343 344 345 346 347 348 349 350
## 2548.858 2745.372 2745.372 2745.372 2633.809 2548.858 2548.858 2822.135
## 351 352 353 354 355 356 357 358
## 2822.135 2822.135 2822.135 2643.021 2643.021 2643.021 2651.209 2812.923
## 359 360 361 362 363 364 365 366
## 2812.923 2812.923 3052.424 3052.424 3068.800 3068.800 3068.800 3068.800
## 406 407 408 409 426 427 428 429
## 2557.046 2557.046 2557.046 2557.046 3180.362 3180.362 2617.433 2617.433
## 430 431 432 433 434 435 436 437
## 2940.862 2940.862 2940.862 2710.573 2719.784 2719.784 2719.784 3026.836
## 438 439
## 3026.836 3180.362cor(AF$PriceEconomy,AF$FlightDuration)## [1] 0.2211007fit<-lm(PriceEconomy~SeatsEconomy,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2122.21 -92.16 169.54 413.84 834.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2710.583 231.850 11.691 <2e-16 ***
## SeatsEconomy 0.276 1.001 0.276 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 754.5 on 72 degrees of freedom
## Multiple R-squared: 0.001056, Adjusted R-squared: -0.01282
## F-statistic: 0.07609 on 1 and 72 DF, p-value: 0.7835AF$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 220 221 222 223 224 225 226 227
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 228 229 230 231 232 233 234 235
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 236 237 238 239 339 340 341 342
## 2751.162 2751.162 2751.162 2751.162 2765.792 2765.792 2765.792 2765.792
## 343 344 345 346 347 348 349 350
## 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792
## 351 352 353 354 355 356 357 358
## 2765.792 2765.792 2765.792 2758.615 2758.615 2758.615 2758.615 2765.792
## 359 360 361 362 363 364 365 366
## 2765.792 2765.792 2758.615 2758.615 2765.792 2765.792 2765.792 2765.792
## 406 407 408 409 426 427 428 429
## 2770.209 2770.209 2770.209 2770.209 2817.965 2817.965 2817.965 2817.965
## 430 431 432 433 434 435 436 437
## 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965
## 438 439
## 2817.965 2817.965cor(AF$PriceEconomy,AF$SeatsEconomy)## [1] 0.03249046fit<-lm(PriceEconomy~PriceRelative,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1090.05 -316.43 34.09 157.14 535.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3107.32 43.57 71.32 <2e-16 ***
## PriceRelative -1648.71 96.03 -17.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 334.5 on 72 degrees of freedom
## Multiple R-squared: 0.8037, Adjusted R-squared: 0.801
## F-statistic: 294.8 on 1 and 72 DF, p-value: < 2.2e-16AF$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218
## 535.3341 1178.3316 2068.6358 2975.4271 2975.4271 2975.4271 2975.4271
## 219 220 221 222 223 224 225
## 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271
## 226 227 228 229 230 231 232
## 2991.9142 2991.9142 2991.9142 2991.9142 2991.9142 3041.3756 3057.8627
## 233 234 235 236 237 238 239
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 339 340 341 342 343 344 345
## 2513.7879 2513.7879 2513.7879 2975.4271 2991.9142 2991.9142 2991.9142
## 346 347 348 349 350 351 352
## 2991.9142 3041.3756 3041.3756 3041.3756 3057.8627 3057.8627 3057.8627
## 353 354 355 356 357 358 359
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 360 361 362 363 364 365 366
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 406 407 408 409 426 427 428
## 403.4371 403.4371 733.1795 2184.0456 1194.8187 1194.8187 2975.4271
## 429 430 431 432 433 434 435
## 2975.4271 2991.9142 2991.9142 2991.9142 3041.3756 3041.3756 3041.3756
## 436 437 438 439
## 3041.3756 3057.8627 3057.8627 3074.3498cor(AF$PriceEconomy,AF$PriceRelative)## [1] -0.8964835fit<-lm(PriceEconomy~PercentPremiumSeats,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2228.1 -199.1 220.2 450.3 1083.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1648.88 715.06 2.306 0.024 *
## PercentPremiumSeats 96.73 61.26 1.579 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 742.1 on 72 degrees of freedom
## Multiple R-squared: 0.03347, Adjusted R-squared: 0.02005
## F-statistic: 2.494 on 1 and 72 DF, p-value: 0.1187AF$PriceEconomy## [1] 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607 2607 2607
## [15] 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165 3165 3165
## [29] 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414 3414
## [43] 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159 3159
## [57] 648 648 700 1094 1522 1522 2581 2581 2996 2996 2996 2979 2979 2979
## [71] 2979 3593 3593 3220fitted(fit)## 212 213 214 215 216 217 218 219
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 220 221 222 223 224 225 226 227
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 228 229 230 231 232 233 234 235
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 236 237 238 239 339 340 341 342
## 2858.047 2858.047 2858.047 2858.047 2836.765 2836.765 2836.765 2836.765
## 343 344 345 346 347 348 349 350
## 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765
## 351 352 353 354 355 356 357 358
## 2836.765 2836.765 2836.765 2821.288 2821.288 2821.288 2821.288 2836.765
## 359 360 361 362 363 364 365 366
## 2836.765 2836.765 2821.288 2821.288 2836.765 2836.765 2836.765 2836.765
## 406 407 408 409 426 427 428 429
## 2616.213 2616.213 2616.213 2616.213 2509.805 2509.805 2509.805 2509.805
## 430 431 432 433 434 435 436 437
## 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805
## 438 439
## 2509.805 2509.805cor(AF$PriceEconomy,AF$PercentPremiumSeats)## [1] 0.1829589fit<-lm(PricePremium~FlightDuration,data = AF)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1516.2 -126.4 82.0 302.1 986.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1975.68 336.57 5.870 1.23e-07 ***
## FlightDuration 121.21 36.86 3.289 0.00156 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 510 on 72 degrees of freedom
## Multiple R-squared: 0.1306, Adjusted R-squared: 0.1185
## F-statistic: 10.82 on 1 and 72 DF, p-value: 0.001559AF$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 3127.216 3127.216 3127.216 2985.395 2985.395 2985.395 2985.395 2985.395
## 220 221 222 223 224 225 226 227
## 2985.395 2985.395 2985.395 2864.181 2864.181 2864.181 2803.573 2803.573
## 228 229 230 231 232 233 234 235
## 3055.699 3055.699 3055.699 2955.091 3088.427 3088.427 3096.912 3096.912
## 236 237 238 239 339 340 341 342
## 3086.003 3086.003 3096.912 3096.912 2985.395 2985.395 2985.395 2884.787
## 343 344 345 346 347 348 349 350
## 2803.573 3036.305 3036.305 3036.305 2904.181 2803.573 2803.573 3127.216
## 351 352 353 354 355 356 357 358
## 3127.216 3127.216 3127.216 2915.091 2915.091 2915.091 2924.788 3116.306
## 359 360 361 362 363 364 365 366
## 3116.306 3116.306 3399.948 3399.948 3419.342 3419.342 3419.342 3419.342
## 406 407 408 409 426 427 428 429
## 2813.271 2813.271 2813.271 2813.271 3551.466 3551.466 2884.787 2884.787
## 430 431 432 433 434 435 436 437
## 3267.824 3267.824 3267.824 2995.092 3006.001 3006.001 3006.001 3369.644
## 438 439
## 3369.644 3551.466cor(AF$PricePremium,AF$FlightDuration)## [1] 0.3613764fit<-lm(PriceEconomy~SeatsEconomy,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2122.21 -92.16 169.54 413.84 834.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2710.583 231.850 11.691 <2e-16 ***
## SeatsEconomy 0.276 1.001 0.276 0.783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 754.5 on 72 degrees of freedom
## Multiple R-squared: 0.001056, Adjusted R-squared: -0.01282
## F-statistic: 0.07609 on 1 and 72 DF, p-value: 0.7835AF$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 220 221 222 223 224 225 226 227
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 228 229 230 231 232 233 234 235
## 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162 2751.162
## 236 237 238 239 339 340 341 342
## 2751.162 2751.162 2751.162 2751.162 2765.792 2765.792 2765.792 2765.792
## 343 344 345 346 347 348 349 350
## 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792 2765.792
## 351 352 353 354 355 356 357 358
## 2765.792 2765.792 2765.792 2758.615 2758.615 2758.615 2758.615 2765.792
## 359 360 361 362 363 364 365 366
## 2765.792 2765.792 2758.615 2758.615 2765.792 2765.792 2765.792 2765.792
## 406 407 408 409 426 427 428 429
## 2770.209 2770.209 2770.209 2770.209 2817.965 2817.965 2817.965 2817.965
## 430 431 432 433 434 435 436 437
## 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965 2817.965
## 438 439
## 2817.965 2817.965cor(AF$PricePremium,AF$SeatsEconomy)## [1] 0.1507589fit<-lm(PriceEconomy~SeatsPremium,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2067.3 -18.4 140.1 507.5 877.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2231.79 384.95 5.798 1.66e-07 ***
## SeatsPremium 20.15 14.05 1.434 0.156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 744.3 on 72 degrees of freedom
## Multiple R-squared: 0.02778, Adjusted R-squared: 0.01427
## F-statistic: 2.057 on 1 and 72 DF, p-value: 0.1558AF$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889
## 220 221 222 223 224 225 226 227
## 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889
## 228 229 230 231 232 233 234 235
## 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889 2654.889
## 236 237 238 239 339 340 341 342
## 2654.889 2654.889 2654.889 2654.889 2795.921 2795.921 2795.921 2795.921
## 343 344 345 346 347 348 349 350
## 2795.921 2795.921 2795.921 2795.921 2795.921 2795.921 2795.921 2795.921
## 351 352 353 354 355 356 357 358
## 2795.921 2795.921 2795.921 2715.331 2715.331 2715.331 2715.331 2795.921
## 359 360 361 362 363 364 365 366
## 2795.921 2795.921 2715.331 2715.331 2795.921 2795.921 2795.921 2795.921
## 406 407 408 409 426 427 428 429
## 2715.331 2715.331 2715.331 2715.331 2997.396 2997.396 2997.396 2997.396
## 430 431 432 433 434 435 436 437
## 2997.396 2997.396 2997.396 2997.396 2997.396 2997.396 2997.396 2997.396
## 438 439
## 2997.396 2997.396cor(AF$PricePremium,AF$SeatsPremium)## [1] 0.2995749fit<-lm(PriceEconomy~PriceRelative,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1090.05 -316.43 34.09 157.14 535.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3107.32 43.57 71.32 <2e-16 ***
## PriceRelative -1648.71 96.03 -17.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 334.5 on 72 degrees of freedom
## Multiple R-squared: 0.8037, Adjusted R-squared: 0.801
## F-statistic: 294.8 on 1 and 72 DF, p-value: < 2.2e-16AF$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218
## 535.3341 1178.3316 2068.6358 2975.4271 2975.4271 2975.4271 2975.4271
## 219 220 221 222 223 224 225
## 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271 2975.4271
## 226 227 228 229 230 231 232
## 2991.9142 2991.9142 2991.9142 2991.9142 2991.9142 3041.3756 3057.8627
## 233 234 235 236 237 238 239
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 339 340 341 342 343 344 345
## 2513.7879 2513.7879 2513.7879 2975.4271 2991.9142 2991.9142 2991.9142
## 346 347 348 349 350 351 352
## 2991.9142 3041.3756 3041.3756 3041.3756 3057.8627 3057.8627 3057.8627
## 353 354 355 356 357 358 359
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 360 361 362 363 364 365 366
## 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627 3057.8627
## 406 407 408 409 426 427 428
## 403.4371 403.4371 733.1795 2184.0456 1194.8187 1194.8187 2975.4271
## 429 430 431 432 433 434 435
## 2975.4271 2991.9142 2991.9142 2991.9142 3041.3756 3041.3756 3041.3756
## 436 437 438 439
## 3041.3756 3057.8627 3057.8627 3074.3498cor(AF$PricePremium,AF$PriceRelative)## [1] -0.6500079fit<-lm(PriceEconomy~PercentPremiumSeats,data = AF)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = AF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2228.1 -199.1 220.2 450.3 1083.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1648.88 715.06 2.306 0.024 *
## PercentPremiumSeats 96.73 61.26 1.579 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 742.1 on 72 degrees of freedom
## Multiple R-squared: 0.03347, Adjusted R-squared: 0.02005
## F-statistic: 2.494 on 1 and 72 DF, p-value: 0.1187AF$PricePremium## [1] 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807 2807 2807
## [15] 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275 3275 3275
## [29] 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524 3524
## [43] 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243 3243
## [57] 1710 1710 1710 1710 3289 3289 2781 2781 3196 3196 3196 3088 3088 3088
## [71] 3088 3702 3702 3289fitted(fit)## 212 213 214 215 216 217 218 219
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 220 221 222 223 224 225 226 227
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 228 229 230 231 232 233 234 235
## 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047 2858.047
## 236 237 238 239 339 340 341 342
## 2858.047 2858.047 2858.047 2858.047 2836.765 2836.765 2836.765 2836.765
## 343 344 345 346 347 348 349 350
## 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765 2836.765
## 351 352 353 354 355 356 357 358
## 2836.765 2836.765 2836.765 2821.288 2821.288 2821.288 2821.288 2836.765
## 359 360 361 362 363 364 365 366
## 2836.765 2836.765 2821.288 2821.288 2836.765 2836.765 2836.765 2836.765
## 406 407 408 409 426 427 428 429
## 2616.213 2616.213 2616.213 2616.213 2509.805 2509.805 2509.805 2509.805
## 430 431 432 433 434 435 436 437
## 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805 2509.805
## 438 439
## 2509.805 2509.805cor(AF$PricePremium,AF$PercentPremiumSeats)## [1] 0.0775941Singapore Airlines
Analyse all about SGP Airlines:-
SGP <- airline[ which(airline$Airline=='Singapore'),]
View(SGP)
summary(SGP)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:16 Min. : 3.83 Aug:11
## British : 0 Boeing:24 1st Qu.: 6.50 Jul: 8
## Delta : 0 Median :12.41 Oct:10
## Jet : 0 Mean :10.48 Sep:11
## Singapore:40 3rd Qu.:13.33
## Virgin : 0 Max. :14.66
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :184.0 Min. :28.0 Min. :32
## International:40 1st Qu.:184.0 1st Qu.:28.0 1st Qu.:32
## Median :184.0 Median :28.0 Median :32
## Mean :243.6 Mean :31.2 Mean :32
## 3rd Qu.:333.0 3rd Qu.:36.0 3rd Qu.:32
## Max. :333.0 Max. :36.0 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :19 Min. :20 Min. : 505.0 Min. : 619
## 1st Qu.:38 1st Qu.:19 1st Qu.:20 1st Qu.: 563.0 1st Qu.:1004
## Median :38 Median :19 Median :20 Median : 690.0 Median :1110
## Mean :38 Mean :19 Mean :20 Mean : 860.2 Mean :1240
## 3rd Qu.:38 3rd Qu.:19 3rd Qu.:20 3rd Qu.:1223.0 3rd Qu.:1564
## Max. :38 Max. :19 Max. :20 Max. :1431.0 Max. :1947
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0900 Min. :212.0 Min. :6 Min. :1
## 1st Qu.:0.1300 1st Qu.:212.0 1st Qu.:6 1st Qu.:1
## Median :0.6050 Median :212.0 Median :6 Median :1
## Mean :0.5298 Mean :274.8 Mean :6 Mean :1
## 3rd Qu.:0.8300 3rd Qu.:369.0 3rd Qu.:6 3rd Qu.:1
## Max. :1.1100 Max. :369.0 Max. :6 Max. :1
## PercentPremiumSeats
## Min. : 9.76
## 1st Qu.: 9.76
## Median :13.21
## Mean :11.83
## 3rd Qu.:13.21
## Max. :13.21Check the all the means now all SGP aircrSGPts
mean(SGP$PriceEconomy)## [1] 860.25mean(SGP$PricePremium)## [1] 1239.925mean(SGP$FlightDuration)## [1] 10.481mean(SGP$PitchEconomy)## [1] 32mean(SGP$PitchPremium)## [1] 38mean(SGP$WidthEconomy)## [1] 19mean(SGP$WidthPremium)## [1] 20mean(SGP$PriceRelative)## [1] 0.52975mean(SGP$PitchDifference)## [1] 6mean(SGP$WidthDifference)## [1] 1Now Analyse separately for Each AircrSGPts in SGP Airlines i.e-Boeing and AirBus
SGPboeing <- SGP[ which(SGP$Aircraft=='Boeing'),]
View(SGPboeing)
summary(SGPboeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus: 0 Min. : 3.83 Aug:7
## British : 0 Boeing:24 1st Qu.: 9.66 Jul:4
## Delta : 0 Median :12.41 Oct:6
## Jet : 0 Mean :11.03 Sep:7
## Singapore:24 3rd Qu.:13.91
## Virgin : 0 Max. :14.66
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :184 Min. :28 Min. :32
## International:24 1st Qu.:184 1st Qu.:28 1st Qu.:32
## Median :184 Median :28 Median :32
## Mean :184 Mean :28 Mean :32
## 3rd Qu.:184 3rd Qu.:28 3rd Qu.:32
## Max. :184 Max. :28 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :19 Min. :20 Min. : 563.0 Min. : 619
## 1st Qu.:38 1st Qu.:19 1st Qu.:20 1st Qu.: 747.8 1st Qu.:1242
## Median :38 Median :19 Median :20 Median :1215.0 Median :1452
## Mean :38 Mean :19 Mean :20 Mean :1035.4 Mean :1362
## 3rd Qu.:38 3rd Qu.:19 3rd Qu.:20 3rd Qu.:1406.0 3rd Qu.:1584
## Max. :38 Max. :19 Max. :20 Max. :1431.0 Max. :1947
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0900 Min. :212 Min. :6 Min. :1
## 1st Qu.:0.1000 1st Qu.:212 1st Qu.:6 1st Qu.:1
## Median :0.1300 Median :212 Median :6 Median :1
## Mean :0.3496 Mean :212 Mean :6 Mean :1
## 3rd Qu.:0.6000 3rd Qu.:212 3rd Qu.:6 3rd Qu.:1
## Max. :1.1100 Max. :212 Max. :6 Max. :1
## PercentPremiumSeats
## Min. :13.21
## 1st Qu.:13.21
## Median :13.21
## Mean :13.21
## 3rd Qu.:13.21
## Max. :13.21mean(SGPboeing$PriceEconomy)## [1] 1035.417mean(SGPboeing$PricePremium)## [1] 1361.875library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(SGPboeing$PriceEconomy,SGPboeing$TravelMonth,mean))
y2<-c(by(SGPboeing$PricePremium,SGPboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -414.97 -213.29 99.06 291.13 295.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 393.59 187.93 2.094 0.04797 *
## FlightDuration 58.21 16.22 3.588 0.00164 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 282.5 on 22 degrees of freedom
## Multiple R-squared: 0.3692, Adjusted R-squared: 0.3405
## F-statistic: 12.88 on 1 and 22 DF, p-value: 0.001637SGPboeing$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431fitted(fit)## 315 316 317 318 319 320 321
## 1203.2475 1203.2475 1203.2475 1203.2475 1115.9366 1115.9366 1115.9366
## 322 323 324 325 326 327 328
## 1023.9692 1023.9692 1023.9692 1246.9029 1246.9029 1246.9029 955.8668
## 329 330 331 332 333 334 335
## 955.8668 955.8668 616.5187 616.5187 616.5187 616.5187 1135.7271
## 336 337 338
## 1135.7271 1135.7271 1135.7271cor(SGPboeing$PriceEconomy,SGPboeing$FlightDuration)## [1] 0.6076207fit<-lm(PriceEconomy~SeatsEconomy,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -287.7 179.6 370.6 395.6
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1035.42 71.02 14.58 4.14e-13 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.9 on 23 degrees of freedomSGPboeing$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417cor(SGPboeing$PriceEconomy,SGPboeing$SeatsEconomy)## Warning in cor(SGPboeing$PriceEconomy, SGPboeing$SeatsEconomy): the
## standard deviation is zero## [1] NAfit<-lm(PriceEconomy~PriceRelative,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -565.0 -158.7 130.2 289.2 299.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1165.0 103.3 11.276 1.3e-10 ***
## PriceRelative -370.8 221.6 -1.674 0.108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 335 on 22 degrees of freedom
## Multiple R-squared: 0.1129, Adjusted R-squared: 0.07261
## F-statistic: 2.801 on 1 and 22 DF, p-value: 0.1084SGPboeing$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431fitted(fit)## 315 316 317 318 319 320 321
## 753.4758 857.2919 857.2919 879.5381 942.5693 942.5693 942.5693
## 322 323 324 325 326 327 328
## 961.1079 987.0619 987.0619 1116.8319 1116.8319 1116.8319 1116.8319
## 329 330 331 332 333 334 335
## 1116.8319 1116.8319 1127.9551 1127.9551 1127.9551 1127.9551 1131.6628
## 336 337 338
## 1131.6628 1131.6628 1131.6628cor(SGPboeing$PriceEconomy,SGPboeing$PriceRelative)## [1] -0.3360486fit<-lm(PriceEconomy~PercentPremiumSeats,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -287.7 179.6 370.6 395.6
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1035.42 71.02 14.58 4.14e-13 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.9 on 23 degrees of freedomSGPboeing$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417cor(SGPboeing$PriceEconomy,SGPboeing$PercentPremiumSeats)## Warning in cor(SGPboeing$PriceEconomy, SGPboeing$PercentPremiumSeats): the
## standard deviation is zero## [1] NAfit<-lm(PricePremium~FlightDuration,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -442.96 -127.23 -18.93 71.45 452.11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 301.63 160.95 1.874 0.0743 .
## FlightDuration 96.15 13.89 6.921 5.98e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 242 on 22 degrees of freedom
## Multiple R-squared: 0.6853, Adjusted R-squared: 0.671
## F-statistic: 47.91 on 1 and 22 DF, p-value: 5.982e-07SGPboeing$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564fitted(fit)## 315 316 317 318 319 320 321
## 1639.1159 1639.1159 1639.1159 1639.1159 1494.8865 1494.8865 1494.8865
## 322 323 324 325 326 327 328
## 1342.9649 1342.9649 1342.9649 1711.2306 1711.2306 1711.2306 1230.4660
## 329 330 331 332 333 334 335
## 1230.4660 1230.4660 669.8945 669.8945 669.8945 669.8945 1527.5785
## 336 337 338
## 1527.5785 1527.5785 1527.5785cor(SGPboeing$PricePremium,SGPboeing$FlightDuration)## [1] 0.8278255fit<-lm(PriceEconomy~SeatsEconomy,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -287.7 179.6 370.6 395.6
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1035.42 71.02 14.58 4.14e-13 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.9 on 23 degrees of freedomSGPboeing$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417cor(SGPboeing$PricePremium,SGPboeing$SeatsEconomy)## Warning in cor(SGPboeing$PricePremium, SGPboeing$SeatsEconomy): the
## standard deviation is zero## [1] NAfit<-lm(PriceEconomy~SeatsPremium,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -287.7 179.6 370.6 395.6
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1035.42 71.02 14.58 4.14e-13 ***
## SeatsPremium NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.9 on 23 degrees of freedomSGPboeing$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417cor(SGPboeing$PricePremium,SGPboeing$SeatsPremium)## Warning in cor(SGPboeing$PricePremium, SGPboeing$SeatsPremium): the
## standard deviation is zero## [1] NAfit<-lm(PriceEconomy~PriceRelative,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -565.0 -158.7 130.2 289.2 299.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1165.0 103.3 11.276 1.3e-10 ***
## PriceRelative -370.8 221.6 -1.674 0.108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 335 on 22 degrees of freedom
## Multiple R-squared: 0.1129, Adjusted R-squared: 0.07261
## F-statistic: 2.801 on 1 and 22 DF, p-value: 0.1084SGPboeing$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564fitted(fit)## 315 316 317 318 319 320 321
## 753.4758 857.2919 857.2919 879.5381 942.5693 942.5693 942.5693
## 322 323 324 325 326 327 328
## 961.1079 987.0619 987.0619 1116.8319 1116.8319 1116.8319 1116.8319
## 329 330 331 332 333 334 335
## 1116.8319 1116.8319 1127.9551 1127.9551 1127.9551 1127.9551 1131.6628
## 336 337 338
## 1131.6628 1131.6628 1131.6628cor(SGPboeing$PricePremium,SGPboeing$PriceRelative)## [1] 0.3316675fit<-lm(PriceEconomy~PercentPremiumSeats,data = SGPboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = SGPboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -287.7 179.6 370.6 395.6
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1035.42 71.02 14.58 4.14e-13 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.9 on 23 degrees of freedomSGPboeing$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417cor(SGPboeing$PricePremium,SGPboeing$PercentPremiumSeats)## Warning in cor(SGPboeing$PricePremium, SGPboeing$PercentPremiumSeats): the
## standard deviation is zero## [1] NASGPairbus <-SGP[ which(SGP$Aircraft=='AirBus'),]
View(SGPairbus)
summary(SGPairbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:16 Min. : 6.160 Aug:4
## British : 0 Boeing: 0 1st Qu.: 6.415 Jul:4
## Delta : 0 Median : 9.580 Oct:4
## Jet : 0 Mean : 9.662 Sep:4
## Singapore:16 3rd Qu.:12.828
## Virgin : 0 Max. :13.330
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :333 Min. :36 Min. :32
## International:16 1st Qu.:333 1st Qu.:36 1st Qu.:32
## Median :333 Median :36 Median :32
## Mean :333 Mean :36 Mean :32
## 3rd Qu.:333 3rd Qu.:36 3rd Qu.:32
## Max. :333 Max. :36 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :19 Min. :20 Min. :505.0 Min. :1004
## 1st Qu.:38 1st Qu.:19 1st Qu.:20 1st Qu.:505.0 1st Qu.:1004
## Median :38 Median :19 Median :20 Median :597.5 Median :1057
## Mean :38 Mean :19 Mean :20 Mean :597.5 Mean :1057
## 3rd Qu.:38 3rd Qu.:19 3rd Qu.:20 3rd Qu.:690.0 3rd Qu.:1110
## Max. :38 Max. :19 Max. :20 Max. :690.0 Max. :1110
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.61 Min. :369 Min. :6 Min. :1
## 1st Qu.:0.61 1st Qu.:369 1st Qu.:6 1st Qu.:1
## Median :0.80 Median :369 Median :6 Median :1
## Mean :0.80 Mean :369 Mean :6 Mean :1
## 3rd Qu.:0.99 3rd Qu.:369 3rd Qu.:6 3rd Qu.:1
## Max. :0.99 Max. :369 Max. :6 Max. :1
## PercentPremiumSeats
## Min. :9.76
## 1st Qu.:9.76
## Median :9.76
## Mean :9.76
## 3rd Qu.:9.76
## Max. :9.76mean(SGPairbus$PriceEconomy)## [1] 597.5mean(SGPairbus$PricePremium)## [1] 1057library(plotly)
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(SGPairbus$PriceEconomy,SGPairbus$TravelMonth,mean))
y4<-c(by(SGPairbus$PricePremium,SGPairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = SGPairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -94.892 -91.220 0.172 91.392 94.547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 604.0977 75.5860 7.992 1.39e-06 ***
## FlightDuration -0.6828 7.3926 -0.092 0.928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 98.86 on 14 degrees of freedom
## Multiple R-squared: 0.000609, Adjusted R-squared: -0.07078
## F-statistic: 0.008531 on 1 and 14 DF, p-value: 0.9277SGPairbus$PriceEconomy## [1] 505 505 505 505 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 410 411 412 413 414 415 416 417
## 594.9958 594.9958 594.9958 594.9958 599.8916 599.8916 599.8916 599.8916
## 418 419 420 421 422 423 424 425
## 595.4533 595.4533 595.4533 595.4533 599.6594 599.6594 599.6594 599.6594cor(SGPairbus$PriceEconomy,SGPairbus$FlightDuration)## [1] -0.02467791fit<-lm(PriceEconomy~SeatsEconomy,data = SGPairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -92.5 -92.5 0.0 92.5 92.5
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 597.50 23.88 25.02 1.21e-13 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 95.53 on 15 degrees of freedomSGPairbus$PriceEconomy## [1] 505 505 505 505 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 410 411 412 413 414 415 416 417 418 419 420 421
## 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5
## 422 423 424 425
## 597.5 597.5 597.5 597.5cor(SGPairbus$PriceEconomy,SGPairbus$SeatsEconomy)## Warning in cor(SGPairbus$PriceEconomy, SGPairbus$SeatsEconomy): the
## standard deviation is zero## [1] NAfit<-lm(PriceEconomy~PriceRelative,data = SGPairbus)
summary(fit)## Warning in summary.lm(fit): essentially perfect fit: summary may be
## unreliable##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.984e-13 0.000e+00 0.000e+00 1.421e-14 2.132e-13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.870e+02 1.065e-13 9.266e+15 <2e-16 ***
## PriceRelative -4.868e+02 1.295e-13 -3.758e+15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.846e-14 on 14 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 1.412e+31 on 1 and 14 DF, p-value: < 2.2e-16SGPairbus$PriceEconomy## [1] 505 505 505 505 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425
## 505 505 505 505 505 505 505 505 690 690 690 690 690 690 690 690cor(SGPairbus$PriceEconomy,SGPairbus$PriceRelative)## [1] -1fit<-lm(PriceEconomy~PercentPremiumSeats,data = SGPairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -92.5 -92.5 0.0 92.5 92.5
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 597.50 23.88 25.02 1.21e-13 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 95.53 on 15 degrees of freedomSGPairbus$PriceEconomy## [1] 505 505 505 505 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 410 411 412 413 414 415 416 417 418 419 420 421
## 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5 597.5
## 422 423 424 425
## 597.5 597.5 597.5 597.5fit<-lm(PricePremium~FlightDuration,data = SGPairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.370 -52.266 0.099 52.365 54.173
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1060.7803 43.3087 24.493 6.79e-13 ***
## FlightDuration -0.3912 4.2358 -0.092 0.928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56.64 on 14 degrees of freedom
## Multiple R-squared: 0.000609, Adjusted R-squared: -0.07078
## F-statistic: 0.008531 on 1 and 14 DF, p-value: 0.9277SGPairbus$PricePremium## [1] 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110
## [15] 1110 1110fitted(fit)## 410 411 412 413 414 415 416 417
## 1055.565 1055.565 1055.565 1055.565 1058.370 1058.370 1058.370 1058.370
## 418 419 420 421 422 423 424 425
## 1055.827 1055.827 1055.827 1055.827 1058.237 1058.237 1058.237 1058.237cor(SGPairbus$PricePremium,SGPairbus$FlightDuration)## [1] -0.02467791fit<-lm(PricePremium~SeatsEconomy,data = SGPairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53 -53 0 53 53
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1057.00 13.68 77.24 <2e-16 ***
## SeatsEconomy NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 54.74 on 15 degrees of freedomSGPairbus$PricePremium## [1] 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110
## [15] 1110 1110fitted(fit)## 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424
## 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057
## 425
## 1057cor(SGPairbus$PricePremium,SGPairbus$SeatsEconomy)## Warning in cor(SGPairbus$PricePremium, SGPairbus$SeatsEconomy): the
## standard deviation is zero## [1] NAfit<-lm(PricePremium~SeatsPremium,data = SGPairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53 -53 0 53 53
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1057.00 13.68 77.24 <2e-16 ***
## SeatsPremium NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 54.74 on 15 degrees of freedomSGPairbus$PricePremium## [1] 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110
## [15] 1110 1110fitted(fit)## 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424
## 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057
## 425
## 1057cor(SGPairbus$PricePremium,SGPairbus$SeatsPremium)## Warning in cor(SGPairbus$PricePremium, SGPairbus$SeatsPremium): the
## standard deviation is zero## [1] NAfit<-lm(PricePremium~PriceRelative,data = SGPairbus)
summary(fit)## Warning in summary.lm(fit): essentially perfect fit: summary may be
## unreliable##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.197e-14 -3.197e-14 0.000e+00 0.000e+00 1.528e-13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.280e+03 5.091e-14 2.515e+16 <2e-16 ***
## PriceRelative -2.789e+02 6.192e-14 -4.505e+15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.706e-14 on 14 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 2.03e+31 on 1 and 14 DF, p-value: < 2.2e-16SGPairbus$PricePremium## [1] 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110
## [15] 1110 1110fitted(fit)## 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424
## 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110
## 425
## 1110cor(SGPairbus$PricePremium,SGPairbus$PriceRelative)## [1] -1fit<-lm(PricePremium~PercentPremiumSeats,data = SGPairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = SGPairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53 -53 0 53 53
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1057.00 13.68 77.24 <2e-16 ***
## PercentPremiumSeats NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 54.74 on 15 degrees of freedomSGPairbus$PricePremium## [1] 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110
## [15] 1110 1110fitted(fit)## 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424
## 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057 1057
## 425
## 1057cor(SGPairbus$PricePremium,SGPairbus$PercentPremiumSeats)## Warning in cor(SGPairbus$PricePremium, SGPairbus$PercentPremiumSeats): the
## standard deviation is zero## [1] NANow We Should Analyse the international aircrafts of Singapore Airlines
SGPint <- SGP[ which(SGP$IsInternational=='International'),]
View(SGPint)
summary(SGPint)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 0 AirBus:16 Min. : 3.83 Aug:11
## British : 0 Boeing:24 1st Qu.: 6.50 Jul: 8
## Delta : 0 Median :12.41 Oct:10
## Jet : 0 Mean :10.48 Sep:11
## Singapore:40 3rd Qu.:13.33
## Virgin : 0 Max. :14.66
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :184.0 Min. :28.0 Min. :32
## International:40 1st Qu.:184.0 1st Qu.:28.0 1st Qu.:32
## Median :184.0 Median :28.0 Median :32
## Mean :243.6 Mean :31.2 Mean :32
## 3rd Qu.:333.0 3rd Qu.:36.0 3rd Qu.:32
## Max. :333.0 Max. :36.0 Max. :32
## PitchPremium WidthEconomy WidthPremium PriceEconomy PricePremium
## Min. :38 Min. :19 Min. :20 Min. : 505.0 Min. : 619
## 1st Qu.:38 1st Qu.:19 1st Qu.:20 1st Qu.: 563.0 1st Qu.:1004
## Median :38 Median :19 Median :20 Median : 690.0 Median :1110
## Mean :38 Mean :19 Mean :20 Mean : 860.2 Mean :1240
## 3rd Qu.:38 3rd Qu.:19 3rd Qu.:20 3rd Qu.:1223.0 3rd Qu.:1564
## Max. :38 Max. :19 Max. :20 Max. :1431.0 Max. :1947
## PriceRelative SeatsTotal PitchDifference WidthDifference
## Min. :0.0900 Min. :212.0 Min. :6 Min. :1
## 1st Qu.:0.1300 1st Qu.:212.0 1st Qu.:6 1st Qu.:1
## Median :0.6050 Median :212.0 Median :6 Median :1
## Mean :0.5298 Mean :274.8 Mean :6 Mean :1
## 3rd Qu.:0.8300 3rd Qu.:369.0 3rd Qu.:6 3rd Qu.:1
## Max. :1.1100 Max. :369.0 Max. :6 Max. :1
## PercentPremiumSeats
## Min. : 9.76
## 1st Qu.: 9.76
## Median :13.21
## Mean :11.83
## 3rd Qu.:13.21
## Max. :13.21mean(SGPint$PriceEconomy)## [1] 860.25mean(SGPint$PricePremium)## [1] 1239.925library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(SGPint$PriceEconomy,SGPint$TravelMonth,mean))
y2<-c(by(SGPint$PricePremium,SGPint$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -487.83 -236.24 12.27 286.55 465.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.49 153.91 2.420 0.02040 *
## FlightDuration 46.54 13.91 3.345 0.00186 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 311.1 on 38 degrees of freedom
## Multiple R-squared: 0.2274, Adjusted R-squared: 0.2071
## F-statistic: 11.19 on 1 and 38 DF, p-value: 0.001863SGPint$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321
## 1019.8265 1019.8265 1019.8265 1019.8265 950.0205 950.0205 950.0205
## 322 323 324 325 326 327 328
## 876.4915 876.4915 876.4915 1054.7295 1054.7295 1054.7295 822.0429
## 329 330 331 332 333 334 335
## 822.0429 822.0429 550.7303 550.7303 550.7303 550.7303 965.8432
## 336 337 338 410 411 412 413
## 965.8432 965.8432 965.8432 992.8348 992.8348 992.8348 992.8348
## 414 415 416 417 418 419 420
## 659.1622 659.1622 659.1622 659.1622 961.6548 961.6548 961.6548
## 421 422 423 424 425
## 961.6548 674.9849 674.9849 674.9849 674.9849cor(SGPint$PriceEconomy,SGPint$FlightDuration)## [1] 0.4768936fit<-lm(PriceEconomy~SeatsEconomy,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1576.1997 152.7167 10.321 1.41e-12 ***
## SeatsEconomy -2.9390 0.6005 -4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGPint$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGPint$PriceEconomy,SGPint$SeatsEconomy)## [1] -0.6217862fit<-lm(PriceEconomy~PriceRelative,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -567.19 -119.84 -66.15 294.53 398.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1193.01 79.23 15.06 < 2e-16 ***
## PriceRelative -628.14 125.13 -5.02 1.25e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 274.5 on 38 degrees of freedom
## Multiple R-squared: 0.3987, Adjusted R-squared: 0.3829
## F-statistic: 25.2 on 1 and 38 DF, p-value: 1.248e-05SGPint$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321
## 495.7722 671.6512 671.6512 709.3395 816.1232 816.1232 816.1232
## 322 323 324 325 326 327 328
## 847.5302 891.4999 891.4999 1111.3487 1111.3487 1111.3487 1111.3487
## 329 330 331 332 333 334 335
## 1111.3487 1111.3487 1130.1929 1130.1929 1130.1929 1130.1929 1136.4743
## 336 337 338 410 411 412 413
## 1136.4743 1136.4743 1136.4743 571.1489 571.1489 571.1489 571.1489
## 414 415 416 417 418 419 420
## 571.1489 571.1489 571.1489 571.1489 809.8418 809.8418 809.8418
## 421 422 423 424 425
## 809.8418 809.8418 809.8418 809.8418 809.8418cor(SGPint$PriceEconomy,SGPint$PriceRelative)## [1] -0.6314474fit<-lm(PriceEconomy~PercentPremiumSeats,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -641.36 309.94 -2.069 0.0454 *
## PercentPremiumSeats 126.93 25.94 4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGPint$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGPint$PriceEconomy,SGPint$PercentPremiumSeats)## [1] 0.6217862fit<-lm(PricePremium~FlightDuration,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -424.47 -204.11 50.03 173.92 579.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 546.31 135.23 4.040 0.000251 ***
## FlightDuration 66.18 12.23 5.413 3.63e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 273.4 on 38 degrees of freedom
## Multiple R-squared: 0.4354, Adjusted R-squared: 0.4205
## F-statistic: 29.3 on 1 and 38 DF, p-value: 3.63e-06SGPint$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321
## 1466.8490 1466.8490 1466.8490 1466.8490 1367.5822 1367.5822 1367.5822
## 322 323 324 325 326 327 328
## 1263.0211 1263.0211 1263.0211 1516.4824 1516.4824 1516.4824 1185.5929
## 329 330 331 332 333 334 335
## 1185.5929 1185.5929 799.7758 799.7758 799.7758 799.7758 1390.0827
## 336 337 338 410 411 412 413
## 1390.0827 1390.0827 1390.0827 1428.4658 1428.4658 1428.4658 1428.4658
## 414 415 416 417 418 419 420
## 953.9703 953.9703 953.9703 953.9703 1384.1266 1384.1266 1384.1266
## 421 422 423 424 425
## 1384.1266 976.4708 976.4708 976.4708 976.4708cor(SGPint$PricePremium,SGPint$FlightDuration)## [1] 0.6598354fit<-lm(PriceEconomy~SeatsEconomy,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1576.1997 152.7167 10.321 1.41e-12 ***
## SeatsEconomy -2.9390 0.6005 -4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGPint$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGPint$PricePremium,SGPint$SeatsEconomy)## [1] -0.4211861fit<-lm(PriceEconomy~SeatsPremium,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2568.13 351.71 7.302 9.64e-09 ***
## SeatsPremium -54.74 11.18 -4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGPint$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGPint$PricePremium,SGPint$SeatsPremium)## [1] -0.4211861fit<-lm(PriceEconomy~PriceRelative,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -567.19 -119.84 -66.15 294.53 398.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1193.01 79.23 15.06 < 2e-16 ***
## PriceRelative -628.14 125.13 -5.02 1.25e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 274.5 on 38 degrees of freedom
## Multiple R-squared: 0.3987, Adjusted R-squared: 0.3829
## F-statistic: 25.2 on 1 and 38 DF, p-value: 1.248e-05SGPint$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321
## 495.7722 671.6512 671.6512 709.3395 816.1232 816.1232 816.1232
## 322 323 324 325 326 327 328
## 847.5302 891.4999 891.4999 1111.3487 1111.3487 1111.3487 1111.3487
## 329 330 331 332 333 334 335
## 1111.3487 1111.3487 1130.1929 1130.1929 1130.1929 1130.1929 1136.4743
## 336 337 338 410 411 412 413
## 1136.4743 1136.4743 1136.4743 571.1489 571.1489 571.1489 571.1489
## 414 415 416 417 418 419 420
## 571.1489 571.1489 571.1489 571.1489 809.8418 809.8418 809.8418
## 421 422 423 424 425
## 809.8418 809.8418 809.8418 809.8418 809.8418cor(SGPint$PricePremium,SGPint$PriceRelative)## [1] -0.09445655fit<-lm(PriceEconomy~PercentPremiumSeats,data = SGPint)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = SGPint)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -641.36 309.94 -2.069 0.0454 *
## PercentPremiumSeats 126.93 25.94 4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGPint$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGPint$PricePremium,SGPint$PercentPremiumSeats)## [1] 0.4211861Now It’s time for comparison-
par(mfrow=c(1, 2))
main="Boeing vs AirBus"
library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(SGPboeing$PriceEconomy,SGPboeing$TravelMonth,mean))
y2<-c(by(SGPboeing$PricePremium,SGPboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(SGPairbus$PriceEconomy,SGPairbus$TravelMonth,mean))
y4<-c(by(SGPairbus$PricePremium,SGPairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(main="mean prices of economy & premium tickets in Boeing",data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Boeing)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'plot_ly(main="mean prices of economy & premium tickets in Airbus"
,data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price(In Airbus)"),
margin = list(b = 100),
barmode = 'group')## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'
## Warning: 'bar' objects don't have these attributes: 'main'
## Valid attributes include:
## 'type', 'visible', 'showlegend', 'legendgroup', 'opacity', 'name', 'uid', 'ids', 'customdata', 'hoverinfo', 'hoverlabel', 'stream', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'textposition', 'textfont', 'insidetextfont', 'outsidetextfont', 'orientation', 'base', 'offset', 'width', 'marker', 'r', 't', 'error_y', 'error_x', '_deprecated', 'xaxis', 'yaxis', 'xcalendar', 'ycalendar', 'idssrc', 'customdatasrc', 'hoverinfosrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'textpositionsrc', 'basesrc', 'offsetsrc', 'widthsrc', 'rsrc', 'tsrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule'short Analysis of Singapore Airlines
mean(SGP$PriceEconomy)## [1] 860.25mean(SGP$PricePremium)## [1] 1239.925library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(SGP$PriceEconomy,SGP$TravelMonth,mean))
y2<-c(by(SGP$PricePremium,SGP$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -487.83 -236.24 12.27 286.55 465.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.49 153.91 2.420 0.02040 *
## FlightDuration 46.54 13.91 3.345 0.00186 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 311.1 on 38 degrees of freedom
## Multiple R-squared: 0.2274, Adjusted R-squared: 0.2071
## F-statistic: 11.19 on 1 and 38 DF, p-value: 0.001863SGP$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321
## 1019.8265 1019.8265 1019.8265 1019.8265 950.0205 950.0205 950.0205
## 322 323 324 325 326 327 328
## 876.4915 876.4915 876.4915 1054.7295 1054.7295 1054.7295 822.0429
## 329 330 331 332 333 334 335
## 822.0429 822.0429 550.7303 550.7303 550.7303 550.7303 965.8432
## 336 337 338 410 411 412 413
## 965.8432 965.8432 965.8432 992.8348 992.8348 992.8348 992.8348
## 414 415 416 417 418 419 420
## 659.1622 659.1622 659.1622 659.1622 961.6548 961.6548 961.6548
## 421 422 423 424 425
## 961.6548 674.9849 674.9849 674.9849 674.9849cor(SGP$PriceEconomy,SGP$FlightDuration)## [1] 0.4768936fit<-lm(PriceEconomy~SeatsEconomy,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1576.1997 152.7167 10.321 1.41e-12 ***
## SeatsEconomy -2.9390 0.6005 -4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGP$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGP$PriceEconomy,SGP$SeatsEconomy)## [1] -0.6217862fit<-lm(PriceEconomy~PriceRelative,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -567.19 -119.84 -66.15 294.53 398.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1193.01 79.23 15.06 < 2e-16 ***
## PriceRelative -628.14 125.13 -5.02 1.25e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 274.5 on 38 degrees of freedom
## Multiple R-squared: 0.3987, Adjusted R-squared: 0.3829
## F-statistic: 25.2 on 1 and 38 DF, p-value: 1.248e-05SGP$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321
## 495.7722 671.6512 671.6512 709.3395 816.1232 816.1232 816.1232
## 322 323 324 325 326 327 328
## 847.5302 891.4999 891.4999 1111.3487 1111.3487 1111.3487 1111.3487
## 329 330 331 332 333 334 335
## 1111.3487 1111.3487 1130.1929 1130.1929 1130.1929 1130.1929 1136.4743
## 336 337 338 410 411 412 413
## 1136.4743 1136.4743 1136.4743 571.1489 571.1489 571.1489 571.1489
## 414 415 416 417 418 419 420
## 571.1489 571.1489 571.1489 571.1489 809.8418 809.8418 809.8418
## 421 422 423 424 425
## 809.8418 809.8418 809.8418 809.8418 809.8418cor(SGP$PriceEconomy,SGP$PriceRelative)## [1] -0.6314474fit<-lm(PriceEconomy~PercentPremiumSeats,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -641.36 309.94 -2.069 0.0454 *
## PercentPremiumSeats 126.93 25.94 4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGP$PriceEconomy## [1] 794 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247
## [15] 1247 1247 563 563 563 563 1431 1431 1431 1431 505 505 505 505
## [29] 505 505 505 505 690 690 690 690 690 690 690 690fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGP$PriceEconomy,SGP$PercentPremiumSeats)## [1] 0.6217862fit<-lm(PricePremium~FlightDuration,data = SGP)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -424.47 -204.11 50.03 173.92 579.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 546.31 135.23 4.040 0.000251 ***
## FlightDuration 66.18 12.23 5.413 3.63e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 273.4 on 38 degrees of freedom
## Multiple R-squared: 0.4354, Adjusted R-squared: 0.4205
## F-statistic: 29.3 on 1 and 38 DF, p-value: 3.63e-06SGP$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321
## 1466.8490 1466.8490 1466.8490 1466.8490 1367.5822 1367.5822 1367.5822
## 322 323 324 325 326 327 328
## 1263.0211 1263.0211 1263.0211 1516.4824 1516.4824 1516.4824 1185.5929
## 329 330 331 332 333 334 335
## 1185.5929 1185.5929 799.7758 799.7758 799.7758 799.7758 1390.0827
## 336 337 338 410 411 412 413
## 1390.0827 1390.0827 1390.0827 1428.4658 1428.4658 1428.4658 1428.4658
## 414 415 416 417 418 419 420
## 953.9703 953.9703 953.9703 953.9703 1384.1266 1384.1266 1384.1266
## 421 422 423 424 425
## 1384.1266 976.4708 976.4708 976.4708 976.4708cor(SGP$PricePremium,SGP$FlightDuration)## [1] 0.6598354fit<-lm(PriceEconomy~SeatsEconomy,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1576.1997 152.7167 10.321 1.41e-12 ***
## SeatsEconomy -2.9390 0.6005 -4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGP$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGP$PricePremium,SGP$SeatsEconomy)## [1] -0.4211861fit<-lm(PriceEconomy~SeatsPremium,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2568.13 351.71 7.302 9.64e-09 ***
## SeatsPremium -54.74 11.18 -4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGP$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGP$PricePremium,SGP$SeatsPremium)## [1] -0.4211861fit<-lm(PriceEconomy~PriceRelative,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -567.19 -119.84 -66.15 294.53 398.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1193.01 79.23 15.06 < 2e-16 ***
## PriceRelative -628.14 125.13 -5.02 1.25e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 274.5 on 38 degrees of freedom
## Multiple R-squared: 0.3987, Adjusted R-squared: 0.3829
## F-statistic: 25.2 on 1 and 38 DF, p-value: 1.248e-05SGP$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321
## 495.7722 671.6512 671.6512 709.3395 816.1232 816.1232 816.1232
## 322 323 324 325 326 327 328
## 847.5302 891.4999 891.4999 1111.3487 1111.3487 1111.3487 1111.3487
## 329 330 331 332 333 334 335
## 1111.3487 1111.3487 1130.1929 1130.1929 1130.1929 1130.1929 1136.4743
## 336 337 338 410 411 412 413
## 1136.4743 1136.4743 1136.4743 571.1489 571.1489 571.1489 571.1489
## 414 415 416 417 418 419 420
## 571.1489 571.1489 571.1489 571.1489 809.8418 809.8418 809.8418
## 421 422 423 424 425
## 809.8418 809.8418 809.8418 809.8418 809.8418cor(SGP$PricePremium,SGP$PriceRelative)## [1] -0.09445655fit<-lm(PriceEconomy~PercentPremiumSeats,data = SGP)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = SGP)
##
## Residuals:
## Min 1Q Median 3Q Max
## -472.4 -179.9 92.5 187.6 395.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -641.36 309.94 -2.069 0.0454 *
## PercentPremiumSeats 126.93 25.94 4.894 1.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.2 on 38 degrees of freedom
## Multiple R-squared: 0.3866, Adjusted R-squared: 0.3705
## F-statistic: 23.95 on 1 and 38 DF, p-value: 1.848e-05SGP$PricePremium## [1] 1671 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407
## [15] 1407 1407 619 619 619 619 1564 1564 1564 1564 1004 1004 1004 1004
## [29] 1004 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110fitted(fit)## 315 316 317 318 319 320 321 322
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 323 324 325 326 327 328 329 330
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 331 332 333 334 335 336 337 338
## 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417 1035.417
## 410 411 412 413 414 415 416 417
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500
## 418 419 420 421 422 423 424 425
## 597.500 597.500 597.500 597.500 597.500 597.500 597.500 597.500cor(SGP$PricePremium,SGP$PercentPremiumSeats)## [1] 0.4211861Now we should analysis all international aircrafts because Only Delta Airlines has domestic aircrafts.If we want to analyse domestic aircrafts,then we can analyse Delta-Domestic Aircrafts only.
International Aircrafts
Analyse all about International aircrafts of all airlines:-
INTL <- airline[ which(airline$IsInternational=='International'),]
View(INTL)
summary(INTL)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 74 AirBus:145 Min. : 1.250 Aug:117
## British :175 Boeing:273 1st Qu.: 5.473 Jul: 65
## Delta : 6 Median : 8.330 Oct:116
## Jet : 61 Mean : 7.988 Sep:120
## Singapore: 40 3rd Qu.:10.830
## Virgin : 62 Max. :14.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :122.0 Min. : 8.00 Min. :30.00
## International:418 1st Qu.:147.0 1st Qu.:28.00 1st Qu.:31.00
## Median :198.0 Median :36.00 Median :31.00
## Mean :209.9 Mean :34.93 Mean :31.16
## 3rd Qu.:243.0 3rd Qu.:40.00 3rd Qu.:32.00
## Max. :389.0 Max. :66.00 Max. :32.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :38.00 Min. :17.00 Min. :19.00 Min. : 65
## 1st Qu.:38.00 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 509
## Median :38.00 Median :18.00 Median :19.00 Median :1434
## Mean :38.26 Mean :17.89 Mean :19.68 Mean :1420
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:2052
## Max. :40.00 Max. :19.00 Max. :21.00 Max. :3593
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86 Min. :0.0200 Min. :140.0 Min. : 6.000
## 1st Qu.: 789 1st Qu.:0.1400 1st Qu.:168.0 1st Qu.: 6.000
## Median :2084 Median :0.4000 Median :228.0 Median : 7.000
## Mean :1985 Mean :0.5257 Mean :244.8 Mean : 7.098
## 3rd Qu.:2999 3rd Qu.:0.7975 3rd Qu.:279.0 3rd Qu.: 7.000
## Max. :7414 Max. :1.8900 Max. :441.0 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :1.000 Min. : 4.71
## 1st Qu.:1.000 1st Qu.:12.28
## Median :1.000 Median :13.21
## Mean :1.789 Mean :14.65
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :4.000 Max. :24.69Check the all the means now all INTL aircrafts
mean(INTL$PriceEconomy)## [1] 1419.943mean(INTL$PricePremium)## [1] 1984.909mean(INTL$FlightDuration)## [1] 7.987584mean(INTL$PitchEconomy)## [1] 31.16029mean(INTL$PitchPremium)## [1] 38.25837mean(INTL$WidthEconomy)## [1] 17.88995mean(INTL$WidthPremium)## [1] 19.67943mean(INTL$PriceRelative)## [1] 0.5257177mean(INTL$PitchDifference)## [1] 7.098086mean(INTL$WidthDifference)## [1] 1.789474Now Analyse separately for Each Aircrafts in INTL Airlines i.e-Boeing and AirBus
INTLboeing <- INTL[ which(INTL$Aircraft=='Boeing'),]
View(INTLboeing)
summary(INTLboeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 38 AirBus: 0 Min. : 1.250 Aug:78
## British :128 Boeing:273 1st Qu.: 4.910 Jul:40
## Delta : 0 Median : 8.660 Oct:78
## Jet : 54 Mean : 8.162 Sep:77
## Singapore: 24 3rd Qu.:11.080
## Virgin : 29 Max. :14.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :122.0 Min. : 8.00 Min. :30.00
## International:273 1st Qu.:124.0 1st Qu.:28.00 1st Qu.:31.00
## Median :184.0 Median :35.00 Median :31.00
## Mean :188.3 Mean :32.23 Mean :31.03
## 3rd Qu.:243.0 3rd Qu.:40.00 3rd Qu.:31.00
## Max. :389.0 Max. :66.00 Max. :32.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :38.0 Min. :17.00 Min. :19.0 Min. : 65
## 1st Qu.:38.0 1st Qu.:17.00 1st Qu.:19.0 1st Qu.: 563
## Median :38.0 Median :18.00 Median :19.0 Median :1406
## Mean :38.4 Mean :17.77 Mean :19.7 Mean :1423
## 3rd Qu.:38.0 3rd Qu.:18.00 3rd Qu.:21.0 3rd Qu.:1824
## Max. :40.0 Max. :19.00 Max. :21.0 Max. :3593
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86 Min. :0.030 Min. :140.0 Min. : 6.000
## 1st Qu.: 797 1st Qu.:0.240 1st Qu.:162.0 1st Qu.: 7.000
## Median :1866 Median :0.420 Median :212.0 Median : 7.000
## Mean :2012 Mean :0.577 Mean :220.5 Mean : 7.366
## 3rd Qu.:3019 3rd Qu.:0.830 3rd Qu.:279.0 3rd Qu.: 7.000
## Max. :7414 Max. :1.890 Max. :441.0 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :1.000 Min. : 4.71
## 1st Qu.:1.000 1st Qu.:12.12
## Median :1.000 Median :12.90
## Mean :1.923 Mean :15.07
## 3rd Qu.:3.000 3rd Qu.:18.73
## Max. :4.000 Max. :24.69mean(INTLboeing$PriceEconomy)## [1] 1422.758mean(INTLboeing$PricePremium)## [1] 2011.872library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(INTLboeing$PriceEconomy,INTLboeing$TravelMonth,mean))
y2<-c(by(INTLboeing$PricePremium,INTLboeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1581.6 -558.8 -113.8 449.8 1875.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234.79 126.41 1.857 0.0644 .
## FlightDuration 145.55 14.22 10.238 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 828.9 on 271 degrees of freedom
## Multiple R-squared: 0.2789, Adjusted R-squared: 0.2762
## F-statistic: 104.8 on 1 and 271 DF, p-value: < 2.2e-16INTLboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 354
## [71] 354 354 354 464 464 464 489 137 109 77 77 69 65 574
## [85] 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580
## [99] 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356
## [113] 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281
## [127] 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140
## [141] 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 794
## [155] 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247 1247
## [169] 1247 563 563 563 563 1431 1431 1431 1431 2918 2918 2918 2581 2860
## [183] 3026 3026 3026 3057 3057 3057 3414 3414 3414 3414 3215 3215 3215 3215
## [197] 3480 3480 3480 3593 3593 3159 3159 3159 3159 3102 3102 3102 2166 2166
## [211] 2166 649 575 575 797 524 582 167 167 167 139 149 197 211
## [225] 139 118 118 118 108 108 108 297 234 156 156 324 147 127
## [239] 154 154 154 154 322 594 648 648 700 1094 2996 2996 2996 2979
## [253] 3593 3593 201 148 148 187 187 187 187 245 234 172 172 172
## [267] 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 2017.8081 2017.8081 2017.8081 2017.8081 1422.4970 1422.4970 1422.4970
## 8 9 10 11 12 13 14
## 1180.8793 1180.8793 1908.6435 1908.6435 1908.6435 1908.6435 1920.2877
## 15 16 17 18 19 20 21
## 1920.2877 1920.2877 1568.0498 1568.0498 1568.0498 1217.2675 1217.2675
## 22 23 24 25 26 27 28
## 1217.2675 1204.1677 1204.1677 1204.1677 1508.3732 1508.3732 1508.3732
## 29 30 31 32 33 34 35
## 949.4503 949.4503 949.4503 792.2532 792.2532 792.2532 792.2532
## 36 37 38 39 40 41 42
## 2199.7491 2199.7491 2199.7491 792.2532 792.2532 792.2532 792.2532
## 43 44 45 46 47 48 49
## 1022.2267 1022.2267 1022.2267 1435.5967 1435.5967 1435.5967 2090.5845
## 50 51 52 53 54 55 56
## 2090.5845 2090.5845 1180.8793 1847.5113 1847.5113 1847.5113 1119.7471
## 57 58 59 60 61 82 83
## 1119.7471 1119.7471 2054.1963 1988.6975 2054.1963 1228.9117 1228.9117
## 84 85 86 87 88 89 90
## 1228.9117 1228.9117 1338.0763 1338.0763 1338.0763 1338.0763 683.0886
## 91 92 93 94 95 96 97
## 683.0886 683.0886 683.0886 683.0886 683.0886 683.0886 683.0886
## 138 144 147 148 149 151 156
## 416.7269 416.7269 428.3711 428.3711 416.7269 428.3711 1872.2553
## 157 158 159 160 161 162 163
## 1872.2553 1872.2553 1872.2553 1993.0641 1993.0641 1993.0641 1993.0641
## 164 165 166 167 168 169 170
## 1677.2145 1677.2145 1677.2145 1677.2145 1811.1231 1811.1231 1811.1231
## 171 172 173 174 175 176 177
## 1811.1231 1749.9909 2065.8405 1799.4788 1799.4788 1799.4788 1799.4788
## 178 179 180 181 182 183 184
## 2065.8405 1883.8995 1349.7206 1349.7206 1349.7206 1349.7206 2065.8405
## 240 241 242 243 244 245 246
## 1749.9909 1749.9909 1749.9909 1835.8670 1835.8670 1835.8670 1677.2145
## 247 248 249 250 251 252 253
## 1677.2145 1677.2145 1677.2145 1483.6292 1483.6292 1483.6292 1895.5437
## 254 255 256 257 258 259 260
## 1895.5437 1895.5437 1895.5437 1592.7938 1592.7938 1592.7938 1531.6616
## 261 262 263 264 265 266 267
## 1495.2734 1495.2734 1495.2734 1290.0439 1290.0439 1290.0439 1531.6616
## 268 269 270 271 272 273 274
## 1265.2999 1265.2999 1265.2999 1895.5437 1895.5437 1895.5437 1895.5437
## 275 276 277 278 279 280 315
## 1531.6616 1847.5113 1847.5113 1847.5113 1265.2999 1847.5113 2259.4258
## 316 317 318 319 320 321 322
## 2259.4258 2259.4258 2259.4258 2041.0965 2041.0965 2041.0965 1811.1231
## 323 324 325 326 327 328 329
## 1811.1231 1811.1231 2368.5904 2368.5904 2368.5904 1640.8262 1640.8262
## 330 331 332 333 334 335 336
## 1640.8262 792.2532 792.2532 792.2532 792.2532 2090.5845 2090.5845
## 337 338 339 340 341 342 343
## 2090.5845 2090.5845 1447.2410 1447.2410 1447.2410 1326.4321 1228.9117
## 344 345 346 347 348 349 350
## 1508.3732 1508.3732 1508.3732 1349.7206 1228.9117 1228.9117 1617.5378
## 351 352 353 354 355 356 357
## 1617.5378 1617.5378 1617.5378 1362.8203 1362.8203 1362.8203 1374.4646
## 358 359 360 361 362 363 364
## 1604.4380 1604.4380 1604.4380 1945.0317 1945.0317 1968.3201 1968.3201
## 365 366 367 368 369 370 371
## 1968.3201 1968.3201 2247.7816 2247.7816 2247.7816 2175.0052 2175.0052
## 372 373 374 375 376 377 378
## 2175.0052 1531.6616 1531.6616 1531.6616 1629.1820 1629.1820 1629.1820
## 379 380 381 382 383 384 385
## 707.8326 707.8326 707.8326 707.8326 707.8326 840.2856 840.2856
## 386 387 388 389 390 391 392
## 828.6414 598.6679 598.6679 598.6679 621.9564 621.9564 621.9564
## 393 394 395 396 397 398 399
## 840.2856 707.8326 828.6414 840.2856 840.2856 598.6679 621.9564
## 400 401 402 403 404 405 406
## 865.0296 865.0296 865.0296 865.0296 707.8326 707.8326 1240.5559
## 407 408 409 430 431 432 436
## 1240.5559 1240.5559 1240.5559 1786.3791 1786.3791 1786.3791 1471.9850
## 437 438 440 441 442 443 444
## 1908.6435 1908.6435 1058.6149 694.7328 694.7328 1058.6149 1058.6149
## 445 446 447 448 449 450 451
## 1058.6149 1058.6149 1058.6149 694.7328 610.3122 610.3122 610.3122
## 452 453 454 455 456 457 458
## 694.7328 610.3122 610.3122 610.3122 610.3122 707.8326 610.3122cor(INTLboeing$PriceEconomy,INTLboeing$FlightDuration)## [1] 0.5281179fit<-lm(PriceEconomy~SeatsEconomy,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1939.70 -813.74 -49.35 564.42 2242.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 467.7409 169.4286 2.761 0.00616 **
## SeatsEconomy 5.0725 0.8502 5.966 7.56e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 917.7 on 271 degrees of freedom
## Multiple R-squared: 0.1161, Adjusted R-squared: 0.1128
## F-statistic: 35.6 on 1 and 271 DF, p-value: 7.559e-09INTLboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 354
## [71] 354 354 354 464 464 464 489 137 109 77 77 69 65 574
## [85] 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580
## [99] 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356
## [113] 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281
## [127] 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140
## [141] 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 794
## [155] 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247 1247
## [169] 1247 563 563 563 563 1431 1431 1431 1431 2918 2918 2918 2581 2860
## [183] 3026 3026 3026 3057 3057 3057 3414 3414 3414 3414 3215 3215 3215 3215
## [197] 3480 3480 3480 3593 3593 3159 3159 3159 3159 3102 3102 3102 2166 2166
## [211] 2166 649 575 575 797 524 582 167 167 167 139 149 197 211
## [225] 139 118 118 118 108 108 108 297 234 156 156 324 147 127
## [239] 154 154 154 154 322 594 648 648 700 1094 2996 2996 2996 2979
## [253] 3593 3593 201 148 148 187 187 187 187 245 234 172 172 172
## [267] 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 9 10 11 12 13 14 15 16
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 17 18 19 20 21 22 23 24
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 25 26 27 28 29 30 31 32
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 33 34 35 36 37 38 39 40
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 41 42 43 44 45 46 47 48
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 49 50 51 52 53 54 55 56
## 1086.582 1086.582 1086.582 1111.944 1111.944 1111.944 1111.944 1111.944
## 57 58 59 60 61 82 83 84
## 1111.944 1111.944 1111.944 1111.944 1111.944 1700.350 1700.350 1700.350
## 85 86 87 88 89 90 91 92
## 1700.350 1700.350 1700.350 1700.350 1700.350 1167.741 1167.741 1167.741
## 93 94 95 96 97 138 144 147
## 1167.741 1167.741 1167.741 1167.741 1167.741 2004.698 2004.698 2004.698
## 148 149 151 156 157 158 159 160
## 2004.698 2004.698 2004.698 1472.089 1472.089 1472.089 1472.089 1472.089
## 161 162 163 164 165 166 167 168
## 1472.089 1472.089 1472.089 2369.916 2369.916 2369.916 2369.916 1472.089
## 169 170 171 172 173 174 175 176
## 1472.089 1472.089 1472.089 1472.089 1472.089 2369.916 2369.916 2369.916
## 177 178 179 180 181 182 183 184
## 2369.916 1472.089 1472.089 1472.089 1472.089 1472.089 1472.089 1472.089
## 240 241 242 243 244 245 246 247
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 248 249 250 251 252 253 254 255
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 256 257 258 259 260 261 262 263
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 264 265 266 267 268 269 270 271
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 272 273 274 275 276 277 278 279
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 280 315 316 317 318 319 320 321
## 1700.350 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075
## 322 323 324 325 326 327 328 329
## 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075
## 330 331 332 333 334 335 336 337
## 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075
## 338 339 340 341 342 343 344 345
## 1401.075 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234
## 346 347 348 349 350 351 352 353
## 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234
## 354 355 356 357 358 359 360 361
## 1350.350 1350.350 1350.350 1350.350 1482.234 1482.234 1482.234 1350.350
## 362 363 364 365 366 367 368 369
## 1350.350 1482.234 1482.234 1482.234 1482.234 1497.452 1497.452 1497.452
## 370 371 372 373 374 375 376 377
## 1497.452 1497.452 1497.452 1497.452 1497.452 1497.452 1497.452 1497.452
## 378 379 380 381 382 383 384 385
## 1497.452 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727
## 386 387 388 389 390 391 392 393
## 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727
## 394 395 396 397 398 399 400 401
## 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727
## 402 403 404 405 406 407 408 409
## 1096.727 1096.727 1096.727 1096.727 1563.394 1563.394 1563.394 1563.394
## 430 431 432 436 437 438 440 441
## 2440.931 2440.931 2440.931 2440.931 2440.931 2440.931 1289.481 1289.481
## 442 443 444 445 446 447 448 449
## 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481
## 450 451 452 453 454 455 456 457
## 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481
## 458
## 1289.481cor(INTLboeing$PriceEconomy,INTLboeing$SeatsEconomy)## [1] 0.3407435fit<-lm(PriceEconomy~PriceRelative,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1635.86 -627.61 -28.31 608.58 2501.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2006.04 80.31 24.979 <2e-16 ***
## PriceRelative -1010.95 107.16 -9.434 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 846.9 on 271 degrees of freedom
## Multiple R-squared: 0.2472, Adjusted R-squared: 0.2445
## F-statistic: 89.01 on 1 and 271 DF, p-value: < 2.2e-16INTLboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 354
## [71] 354 354 354 464 464 464 489 137 109 77 77 69 65 574
## [85] 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580
## [99] 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356
## [113] 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281
## [127] 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140
## [141] 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 794
## [155] 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247 1247
## [169] 1247 563 563 563 563 1431 1431 1431 1431 2918 2918 2918 2581 2860
## [183] 3026 3026 3026 3057 3057 3057 3414 3414 3414 3414 3215 3215 3215 3215
## [197] 3480 3480 3480 3593 3593 3159 3159 3159 3159 3102 3102 3102 2166 2166
## [211] 2166 649 575 575 797 524 582 167 167 167 139 149 197 211
## [225] 139 118 118 118 108 108 108 297 234 156 156 324 147 127
## [239] 154 154 154 154 322 594 648 648 700 1094 2996 2996 2996 2979
## [253] 3593 3593 201 148 148 187 187 187 187 245 234 172 172 172
## [267] 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6
## 1621.87481 1621.87481 1621.87481 1621.87481 1328.69909 1328.69909
## 7 8 9 10 11 12
## 1328.69909 964.75682 964.75682 1247.82303 1247.82303 1439.90367
## 13 14 15 16 17 18
## 1743.18890 1480.34170 1480.34170 1480.34170 1621.87481 1621.87481
## 19 20 21 22 23 24
## 1621.87481 1662.31284 1662.31284 1662.31284 1672.42234 1672.42234
## 25 26 27 28 29 30
## 1672.42234 1652.20333 1672.42234 1672.42234 1662.31284 1662.31284
## 31 32 33 34 35 36
## 1662.31284 1581.43678 1581.43678 1581.43678 1581.43678 1348.91810
## 37 38 39 40 41 42
## 1348.91810 1348.91810 1763.40791 1763.40791 1763.40791 1763.40791
## 43 44 45 46 47 48
## 1834.17446 1834.17446 1834.17446 1925.16003 1925.16003 1925.16003
## 49 50 51 52 53 54
## 1480.34170 1480.34170 1480.34170 964.75682 1642.09382 1642.09382
## 55 56 57 58 59 60
## 1642.09382 1662.31284 1662.31284 1662.31284 1793.73643 1793.73643
## 61 82 83 84 85 86
## 1389.35613 924.31879 924.31879 924.31879 924.31879 1601.65579
## 87 88 89 90 91 92
## 1601.65579 1601.65579 1601.65579 1520.77973 1520.77973 1520.77973
## 93 94 95 96 97 138
## 1520.77973 1672.42234 1672.42234 1672.42234 1743.18890 1743.18890
## 144 147 148 149 151 156
## 1702.75087 1712.86037 1712.86037 1601.65579 1672.42234 166.10572
## 157 158 159 160 161 162
## 166.10572 166.10572 166.10572 257.09129 257.09129 257.09129
## 163 164 165 166 167 168
## 610.92405 1025.41386 1025.41386 1025.41386 1025.41386 1086.07091
## 169 170 171 172 173 174
## 1086.07091 1086.07091 1086.07091 1156.83746 1439.90367 1490.45121
## 175 176 177 178 179 180
## 1490.45121 1490.45121 1490.45121 1500.56072 1510.67022 1601.65579
## 181 182 183 184 240 241
## 1601.65579 1601.65579 1601.65579 1743.18890 863.66174 863.66174
## 242 243 244 245 246 247
## 1743.18890 1551.10825 1551.10825 1551.10825 1642.09382 1642.09382
## 248 249 250 251 252 253
## 1642.09382 1642.09382 1015.30435 1015.30435 1015.30435 1672.42234
## 254 255 256 257 258 259
## 1672.42234 1672.42234 1672.42234 1642.09382 1642.09382 1642.09382
## 260 261 262 263 264 265
## 863.66174 1581.43678 1581.43678 1581.43678 1601.65579 1601.65579
## 266 267 268 269 270 271
## 1601.65579 1197.27549 1935.26954 1935.26954 1935.26954 883.88076
## 272 273 274 275 276 277
## 883.88076 1086.07091 1803.84594 1197.27549 1834.17446 1834.17446
## 278 279 280 315 316 317
## 1834.17446 1793.73643 1429.79416 883.88076 1166.94697 1166.94697
## 318 319 320 321 322 323
## 1227.60401 1399.46564 1399.46564 1399.46564 1450.01318 1520.77973
## 324 325 326 327 328 329
## 1520.77973 1874.61249 1874.61249 1874.61249 1874.61249 1874.61249
## 330 331 332 333 334 335
## 1874.61249 1904.94102 1904.94102 1904.94102 1904.94102 1915.05052
## 336 337 338 339 340 341
## 1915.05052 1915.05052 1915.05052 1642.09382 1642.09382 1642.09382
## 342 343 344 345 346 347
## 1925.16003 1935.26954 1935.26954 1935.26954 1935.26954 1965.59806
## 348 349 350 351 352 353
## 1965.59806 1965.59806 1975.70757 1975.70757 1975.70757 1975.70757
## 354 355 356 357 358 359
## 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757
## 360 361 362 363 364 365
## 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757
## 366 367 368 369 370 371
## 1975.70757 600.81455 600.81455 600.81455 1864.50299 1864.50299
## 372 373 374 375 376 377
## 1864.50299 1227.60401 1520.77973 1520.77973 1965.59806 1480.34170
## 378 379 380 381 382 383
## 1631.98431 95.33917 95.33917 95.33917 115.55818 317.74834
## 384 385 386 387 388 389
## 348.07686 459.28144 701.90962 732.23814 732.23814 732.23814
## 390 391 392 393 394 395
## 883.88076 883.88076 883.88076 904.09977 934.42829 954.64731
## 396 397 398 399 400 401
## 954.64731 1086.07091 1187.16598 1207.38500 1257.93253 1257.93253
## 402 403 404 405 406 407
## 1257.93253 1257.93253 1500.56072 1834.17446 348.07686 348.07686
## 408 409 430 431 432 436
## 550.26701 1439.90367 1935.26954 1935.26954 1935.26954 1965.59806
## 437 438 440 441 442 443
## 1975.70757 1975.70757 277.31031 307.63883 307.63883 691.80011
## 444 445 446 447 448 449
## 691.80011 691.80011 691.80011 772.67617 924.31879 1227.60401
## 450 451 452 453 454 455
## 1227.60401 1227.60401 1348.91810 1399.46564 1419.68466 1551.10825
## 456 457 458
## 1551.10825 1621.87481 1884.72200cor(INTLboeing$PriceEconomy,INTLboeing$PriceRelative)## [1] -0.497232fit<-lm(PriceEconomy~PercentPremiumSeats,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1363.8 -821.4 -79.5 435.0 2297.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1111.56 164.40 6.761 8.37e-11 ***
## PercentPremiumSeats 20.65 10.19 2.026 0.0437 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 968.8 on 271 degrees of freedom
## Multiple R-squared: 0.01492, Adjusted R-squared: 0.01129
## F-statistic: 4.105 on 1 and 271 DF, p-value: 0.04372INTLboeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1444 1444 1444 1444 1824 1824 1824 1823 354
## [71] 354 354 354 464 464 464 489 137 109 77 77 69 65 574
## [85] 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580
## [99] 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356
## [113] 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281
## [127] 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140
## [141] 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 794
## [155] 794 794 794 1215 1215 1215 876 609 609 1406 1406 1406 1247 1247
## [169] 1247 563 563 563 563 1431 1431 1431 1431 2918 2918 2918 2581 2860
## [183] 3026 3026 3026 3057 3057 3057 3414 3414 3414 3414 3215 3215 3215 3215
## [197] 3480 3480 3480 3593 3593 3159 3159 3159 3159 3102 3102 3102 2166 2166
## [211] 2166 649 575 575 797 524 582 167 167 167 139 149 197 211
## [225] 139 118 118 118 108 108 108 297 234 156 156 324 147 127
## [239] 154 154 154 154 322 594 648 648 700 1094 2996 2996 2996 2979
## [253] 3593 3593 201 148 148 187 187 187 187 245 234 172 172 172
## [267] 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 9 10 11 12 13 14 15 16
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 17 18 19 20 21 22 23 24
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 25 26 27 28 29 30 31 32
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 33 34 35 36 37 38 39 40
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 41 42 43 44 45 46 47 48
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 49 50 51 52 53 54 55 56
## 1621.501 1621.501 1621.501 1596.717 1596.717 1596.717 1596.717 1596.717
## 57 58 59 60 61 82 83 84
## 1596.717 1596.717 1596.717 1596.717 1596.717 1498.405 1498.405 1498.405
## 85 86 87 88 89 90 91 92
## 1498.405 1498.405 1498.405 1498.405 1498.405 1459.989 1459.989 1459.989
## 93 94 95 96 97 138 144 147
## 1459.989 1459.989 1459.989 1459.989 1459.989 1428.802 1428.802 1428.802
## 148 149 151 156 157 158 159 160
## 1428.802 1428.802 1428.802 1421.780 1421.780 1421.780 1421.780 1421.780
## 161 162 163 164 165 166 167 168
## 1421.780 1421.780 1421.780 1420.747 1420.747 1420.747 1420.747 1421.780
## 169 170 171 172 173 174 175 176
## 1421.780 1421.780 1421.780 1421.780 1421.780 1420.747 1420.747 1420.747
## 177 178 179 180 181 182 183 184
## 1420.747 1421.780 1421.780 1421.780 1421.780 1421.780 1421.780 1421.780
## 240 241 242 243 244 245 246 247
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 248 249 250 251 252 253 254 255
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 256 257 258 259 260 261 262 263
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 264 265 266 267 268 269 270 271
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 272 273 274 275 276 277 278 279
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 280 315 316 317 318 319 320 321
## 1377.994 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397
## 322 323 324 325 326 327 328 329
## 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397
## 330 331 332 333 334 335 336 337
## 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397
## 338 339 340 341 342 343 344 345
## 1384.397 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189
## 346 347 348 349 350 351 352 353
## 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189
## 354 355 356 357 358 359 360 361
## 1361.884 1361.884 1361.884 1361.884 1365.189 1365.189 1365.189 1361.884
## 362 363 364 365 366 367 368 369
## 1361.884 1365.189 1365.189 1365.189 1365.189 1329.871 1329.871 1329.871
## 370 371 372 373 374 375 376 377
## 1329.871 1329.871 1329.871 1329.871 1329.871 1329.871 1329.871 1329.871
## 378 379 380 381 382 383 384 385
## 1329.871 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633
## 386 387 388 389 390 391 392 393
## 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633
## 394 395 396 397 398 399 400 401
## 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633
## 402 403 404 405 406 407 408 409
## 1347.633 1347.633 1347.633 1347.633 1318.099 1318.099 1318.099 1318.099
## 430 431 432 436 437 438 440 441
## 1295.380 1295.380 1295.380 1295.380 1295.380 1295.380 1208.841 1208.841
## 442 443 444 445 446 447 448 449
## 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841
## 450 451 452 453 454 455 456 457
## 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841
## 458
## 1208.841cor(INTLboeing$PriceEconomy,INTLboeing$PercentPremiumSeats)## [1] 0.1221607fit<-lm(PricePremium~FlightDuration,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2221.0 -648.1 85.7 661.7 4188.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 264.84 157.27 1.684 0.0933 .
## FlightDuration 214.05 17.69 12.102 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1031 on 271 degrees of freedom
## Multiple R-squared: 0.3508, Adjusted R-squared: 0.3484
## F-statistic: 146.5 on 1 and 271 DF, p-value: < 2.2e-16INTLboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 524
## [71] 524 524 524 616 616 616 616 172 141 99 99 97 86 1619
## [85] 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019
## [99] 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710
## [113] 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025
## [127] 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049
## [141] 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 1671
## [155] 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407 1407
## [169] 1407 619 619 619 619 1564 1564 1564 1564 3972 3972 3972 2781 3063
## [183] 3226 3226 3226 3167 3167 3167 3524 3524 3524 3524 3325 3325 3325 3325
## [197] 3589 3589 3589 3702 3702 3243 3243 3243 3243 7414 7414 7414 2470 2470
## [211] 2470 1152 853 853 826 797 797 483 483 483 398 398 520 534
## [225] 318 267 267 267 228 228 228 620 483 318 318 620 267 228
## [239] 267 267 267 267 483 696 1710 1710 1710 1710 3196 3196 3196 3088
## [253] 3702 3702 545 397 397 430 430 430 430 545 483 304 304 304
## [267] 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 2886.9523 2886.9523 2886.9523 2886.9523 2011.4876 2011.4876 2011.4876
## 8 9 10 11 12 13 14
## 1656.1645 1656.1645 2726.4148 2726.4148 2726.4148 2726.4148 2743.5388
## 15 16 17 18 19 20 21
## 2743.5388 2743.5388 2225.5377 2225.5377 2225.5377 1709.6770 1709.6770
## 22 23 24 25 26 27 28
## 1709.6770 1690.4125 1690.4125 1690.4125 2137.7771 2137.7771 2137.7771
## 29 30 31 32 33 34 35
## 1315.8249 1315.8249 1315.8249 1084.6508 1084.6508 1084.6508 1084.6508
## 36 37 38 39 40 41 42
## 3154.5149 3154.5149 3154.5149 1084.6508 1084.6508 1084.6508 1084.6508
## 43 44 45 46 47 48 49
## 1422.8499 1422.8499 1422.8499 2030.7521 2030.7521 2030.7521 2993.9774
## 50 51 52 53 54 55 56
## 2993.9774 2993.9774 1656.1645 2636.5138 2636.5138 2636.5138 1566.2635
## 57 58 59 60 61 82 83
## 1566.2635 1566.2635 2940.4649 2844.1423 2940.4649 1726.8010 1726.8010
## 84 85 86 87 88 89 90
## 1726.8010 1726.8010 1887.3386 1887.3386 1887.3386 1887.3386 924.1133
## 91 92 93 94 95 96 97
## 924.1133 924.1133 924.1133 924.1133 924.1133 924.1133 924.1133
## 138 144 147 148 149 151 156
## 532.4017 532.4017 549.5257 549.5257 532.4017 549.5257 2672.9023
## 157 158 159 160 161 162 163
## 2672.9023 2672.9023 2672.9023 2850.5638 2850.5638 2850.5638 2850.5638
## 164 165 166 167 168 169 170
## 2386.0752 2386.0752 2386.0752 2386.0752 2583.0013 2583.0013 2583.0013
## 171 172 173 174 175 176 177
## 2583.0013 2493.1002 2957.5889 2565.8773 2565.8773 2565.8773 2565.8773
## 178 179 180 181 182 183 184
## 2957.5889 2690.0263 1904.4626 1904.4626 1904.4626 1904.4626 2957.5889
## 240 241 242 243 244 245 246
## 2493.1002 2493.1002 2493.1002 2619.3898 2619.3898 2619.3898 2386.0752
## 247 248 249 250 251 252 253
## 2386.0752 2386.0752 2386.0752 2101.3886 2101.3886 2101.3886 2707.1503
## 254 255 256 257 258 259 260
## 2707.1503 2707.1503 2707.1503 2261.9262 2261.9262 2261.9262 2172.0251
## 261 262 263 264 265 266 267
## 2118.5126 2118.5126 2118.5126 1816.7020 1816.7020 1816.7020 2172.0251
## 268 269 270 271 272 273 274
## 1780.3135 1780.3135 1780.3135 2707.1503 2707.1503 2707.1503 2707.1503
## 275 276 277 278 279 280 315
## 2172.0251 2636.5138 2636.5138 2636.5138 1780.3135 2636.5138 3242.2754
## 316 317 318 319 320 321 322
## 3242.2754 3242.2754 3242.2754 2921.2004 2921.2004 2921.2004 2583.0013
## 323 324 325 326 327 328 329
## 2583.0013 2583.0013 3402.8130 3402.8130 3402.8130 2332.5627 2332.5627
## 330 331 332 333 334 335 336
## 2332.5627 1084.6508 1084.6508 1084.6508 1084.6508 2993.9774 2993.9774
## 337 338 339 340 341 342 343
## 2993.9774 2993.9774 2047.8761 2047.8761 2047.8761 1870.2146 1726.8010
## 344 345 346 347 348 349 350
## 2137.7771 2137.7771 2137.7771 1904.4626 1726.8010 1726.8010 2298.3147
## 351 352 353 354 355 356 357
## 2298.3147 2298.3147 2298.3147 1923.7271 1923.7271 1923.7271 1940.8511
## 358 359 360 361 362 363 364
## 2279.0502 2279.0502 2279.0502 2779.9273 2779.9273 2814.1753 2814.1753
## 365 366 367 368 369 370 371
## 2814.1753 2814.1753 3225.1514 3225.1514 3225.1514 3118.1264 3118.1264
## 372 373 374 375 376 377 378
## 3118.1264 2172.0251 2172.0251 2172.0251 2315.4387 2315.4387 2315.4387
## 379 380 381 382 383 384 385
## 960.5018 960.5018 960.5018 960.5018 960.5018 1155.2874 1155.2874
## 386 387 388 389 390 391 392
## 1138.1634 799.9643 799.9643 799.9643 834.2123 834.2123 834.2123
## 393 394 395 396 397 398 399
## 1155.2874 960.5018 1138.1634 1155.2874 1155.2874 799.9643 834.2123
## 400 401 402 403 404 405 406
## 1191.6759 1191.6759 1191.6759 1191.6759 960.5018 960.5018 1743.9250
## 407 408 409 430 431 432 436
## 1743.9250 1743.9250 1743.9250 2546.6127 2546.6127 2546.6127 2084.2646
## 437 438 440 441 442 443 444
## 2726.4148 2726.4148 1476.3625 941.2373 941.2373 1476.3625 1476.3625
## 445 446 447 448 449 450 451
## 1476.3625 1476.3625 1476.3625 941.2373 817.0883 817.0883 817.0883
## 452 453 454 455 456 457 458
## 941.2373 817.0883 817.0883 817.0883 817.0883 960.5018 817.0883cor(INTLboeing$PricePremium,INTLboeing$FlightDuration)## [1] 0.5923196fit<-lm(PriceEconomy~SeatsEconomy,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1939.70 -813.74 -49.35 564.42 2242.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 467.7409 169.4286 2.761 0.00616 **
## SeatsEconomy 5.0725 0.8502 5.966 7.56e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 917.7 on 271 degrees of freedom
## Multiple R-squared: 0.1161, Adjusted R-squared: 0.1128
## F-statistic: 35.6 on 1 and 271 DF, p-value: 7.559e-09INTLboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 524
## [71] 524 524 524 616 616 616 616 172 141 99 99 97 86 1619
## [85] 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019
## [99] 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710
## [113] 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025
## [127] 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049
## [141] 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 1671
## [155] 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407 1407
## [169] 1407 619 619 619 619 1564 1564 1564 1564 3972 3972 3972 2781 3063
## [183] 3226 3226 3226 3167 3167 3167 3524 3524 3524 3524 3325 3325 3325 3325
## [197] 3589 3589 3589 3702 3702 3243 3243 3243 3243 7414 7414 7414 2470 2470
## [211] 2470 1152 853 853 826 797 797 483 483 483 398 398 520 534
## [225] 318 267 267 267 228 228 228 620 483 318 318 620 267 228
## [239] 267 267 267 267 483 696 1710 1710 1710 1710 3196 3196 3196 3088
## [253] 3702 3702 545 397 397 430 430 430 430 545 483 304 304 304
## [267] 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 9 10 11 12 13 14 15 16
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 17 18 19 20 21 22 23 24
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 25 26 27 28 29 30 31 32
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 33 34 35 36 37 38 39 40
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 41 42 43 44 45 46 47 48
## 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582 1086.582
## 49 50 51 52 53 54 55 56
## 1086.582 1086.582 1086.582 1111.944 1111.944 1111.944 1111.944 1111.944
## 57 58 59 60 61 82 83 84
## 1111.944 1111.944 1111.944 1111.944 1111.944 1700.350 1700.350 1700.350
## 85 86 87 88 89 90 91 92
## 1700.350 1700.350 1700.350 1700.350 1700.350 1167.741 1167.741 1167.741
## 93 94 95 96 97 138 144 147
## 1167.741 1167.741 1167.741 1167.741 1167.741 2004.698 2004.698 2004.698
## 148 149 151 156 157 158 159 160
## 2004.698 2004.698 2004.698 1472.089 1472.089 1472.089 1472.089 1472.089
## 161 162 163 164 165 166 167 168
## 1472.089 1472.089 1472.089 2369.916 2369.916 2369.916 2369.916 1472.089
## 169 170 171 172 173 174 175 176
## 1472.089 1472.089 1472.089 1472.089 1472.089 2369.916 2369.916 2369.916
## 177 178 179 180 181 182 183 184
## 2369.916 1472.089 1472.089 1472.089 1472.089 1472.089 1472.089 1472.089
## 240 241 242 243 244 245 246 247
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 248 249 250 251 252 253 254 255
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 256 257 258 259 260 261 262 263
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 264 265 266 267 268 269 270 271
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 272 273 274 275 276 277 278 279
## 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350 1700.350
## 280 315 316 317 318 319 320 321
## 1700.350 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075
## 322 323 324 325 326 327 328 329
## 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075
## 330 331 332 333 334 335 336 337
## 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075 1401.075
## 338 339 340 341 342 343 344 345
## 1401.075 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234
## 346 347 348 349 350 351 352 353
## 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234 1482.234
## 354 355 356 357 358 359 360 361
## 1350.350 1350.350 1350.350 1350.350 1482.234 1482.234 1482.234 1350.350
## 362 363 364 365 366 367 368 369
## 1350.350 1482.234 1482.234 1482.234 1482.234 1497.452 1497.452 1497.452
## 370 371 372 373 374 375 376 377
## 1497.452 1497.452 1497.452 1497.452 1497.452 1497.452 1497.452 1497.452
## 378 379 380 381 382 383 384 385
## 1497.452 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727
## 386 387 388 389 390 391 392 393
## 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727
## 394 395 396 397 398 399 400 401
## 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727 1096.727
## 402 403 404 405 406 407 408 409
## 1096.727 1096.727 1096.727 1096.727 1563.394 1563.394 1563.394 1563.394
## 430 431 432 436 437 438 440 441
## 2440.931 2440.931 2440.931 2440.931 2440.931 2440.931 1289.481 1289.481
## 442 443 444 445 446 447 448 449
## 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481
## 450 451 452 453 454 455 456 457
## 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481 1289.481
## 458
## 1289.481cor(INTLboeing$PricePremium,INTLboeing$SeatsEconomy)## [1] 0.3563493fit<-lm(PriceEconomy~SeatsPremium,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1944.3 -626.6 -211.1 293.1 2382.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 592.471 155.435 3.812 0.000171 ***
## SeatsPremium 25.761 4.501 5.723 2.77e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 922 on 271 degrees of freedom
## Multiple R-squared: 0.1078, Adjusted R-squared: 0.1045
## F-statistic: 32.76 on 1 and 271 DF, p-value: 2.765e-08INTLboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 524
## [71] 524 524 524 616 616 616 616 172 141 99 99 97 86 1619
## [85] 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019
## [99] 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710
## [113] 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025
## [127] 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049
## [141] 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 1671
## [155] 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407 1407
## [169] 1407 619 619 619 619 1564 1564 1564 1564 3972 3972 3972 2781 3063
## [183] 3226 3226 3226 3167 3167 3167 3524 3524 3524 3524 3325 3325 3325 3325
## [197] 3589 3589 3589 3702 3702 3243 3243 3243 3243 7414 7414 7414 2470 2470
## [211] 2470 1152 853 853 826 797 797 483 483 483 398 398 520 534
## [225] 318 267 267 267 228 228 228 620 483 318 318 620 267 228
## [239] 267 267 267 267 483 696 1710 1710 1710 1710 3196 3196 3196 3088
## [253] 3702 3702 545 397 397 430 430 430 430 545 483 304 304 304
## [267] 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990
## 8 9 10 11 12 13 14
## 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990
## 15 16 17 18 19 20 21
## 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990
## 22 23 24 25 26 27 28
## 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990
## 29 30 31 32 33 34 35
## 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990
## 36 37 38 39 40 41 42
## 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990
## 43 44 45 46 47 48 49
## 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990 1622.8990
## 50 51 52 53 54 55 56
## 1622.8990 1622.8990 1597.1383 1597.1383 1597.1383 1597.1383 1597.1383
## 57 58 59 60 61 82 83
## 1597.1383 1597.1383 1597.1383 1597.1383 1597.1383 2035.0702 2035.0702
## 84 85 86 87 88 89 90
## 2035.0702 2035.0702 2035.0702 2035.0702 2035.0702 2035.0702 1313.7707
## 91 92 93 94 95 96 97
## 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707
## 138 144 147 148 149 151 156
## 2009.3095 2009.3095 2009.3095 2009.3095 2009.3095 2009.3095 1494.0956
## 157 158 159 160 161 162 163
## 1494.0956 1494.0956 1494.0956 1494.0956 1494.0956 1494.0956 1494.0956
## 164 165 166 167 168 169 170
## 2292.6772 2292.6772 2292.6772 2292.6772 1494.0956 1494.0956 1494.0956
## 171 172 173 174 175 176 177
## 1494.0956 1494.0956 1494.0956 2292.6772 2292.6772 2292.6772 2292.6772
## 178 179 180 181 182 183 184
## 1494.0956 1494.0956 1494.0956 1494.0956 1494.0956 1494.0956 1494.0956
## 240 241 242 243 244 245 246
## 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563
## 247 248 249 250 251 252 253
## 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563
## 254 255 256 257 258 259 260
## 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563
## 261 262 263 264 265 266 267
## 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563
## 268 269 270 271 272 273 274
## 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563
## 275 276 277 278 279 280 315
## 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1519.8563 1313.7707
## 316 317 318 319 320 321 322
## 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707
## 323 324 325 326 327 328 329
## 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707
## 330 331 332 333 334 335 336
## 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707
## 337 338 339 340 341 342 343
## 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707
## 344 345 346 347 348 349 350
## 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707 1313.7707
## 351 352 353 354 355 356 357
## 1313.7707 1313.7707 1313.7707 1210.7279 1210.7279 1210.7279 1210.7279
## 358 359 360 361 362 363 364
## 1313.7707 1313.7707 1313.7707 1210.7279 1210.7279 1313.7707 1313.7707
## 365 366 367 368 369 370 371
## 1313.7707 1313.7707 1210.7279 1210.7279 1210.7279 1210.7279 1210.7279
## 372 373 374 375 376 377 378
## 1210.7279 1210.7279 1210.7279 1210.7279 1210.7279 1210.7279 1210.7279
## 379 380 381 382 383 384 385
## 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423
## 386 387 388 389 390 391 392
## 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423
## 393 394 395 396 397 398 399
## 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423
## 400 401 402 403 404 405 406
## 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1004.6423 1210.7279
## 407 408 409 430 431 432 436
## 1210.7279 1210.7279 1210.7279 1571.3776 1571.3776 1571.3776 1571.3776
## 437 438 440 441 442 443 444
## 1571.3776 1571.3776 798.5567 798.5567 798.5567 798.5567 798.5567
## 445 446 447 448 449 450 451
## 798.5567 798.5567 798.5567 798.5567 798.5567 798.5567 798.5567
## 452 453 454 455 456 457 458
## 798.5567 798.5567 798.5567 798.5567 798.5567 798.5567 798.5567cor(INTLboeing$PricePremium,INTLboeing$SeatsPremium)## [1] 0.4141117fit<-lm(PriceEconomy~PriceRelative,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1635.86 -627.61 -28.31 608.58 2501.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2006.04 80.31 24.979 <2e-16 ***
## PriceRelative -1010.95 107.16 -9.434 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 846.9 on 271 degrees of freedom
## Multiple R-squared: 0.2472, Adjusted R-squared: 0.2445
## F-statistic: 89.01 on 1 and 271 DF, p-value: < 2.2e-16INTLboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 524
## [71] 524 524 524 616 616 616 616 172 141 99 99 97 86 1619
## [85] 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019
## [99] 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710
## [113] 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025
## [127] 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049
## [141] 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 1671
## [155] 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407 1407
## [169] 1407 619 619 619 619 1564 1564 1564 1564 3972 3972 3972 2781 3063
## [183] 3226 3226 3226 3167 3167 3167 3524 3524 3524 3524 3325 3325 3325 3325
## [197] 3589 3589 3589 3702 3702 3243 3243 3243 3243 7414 7414 7414 2470 2470
## [211] 2470 1152 853 853 826 797 797 483 483 483 398 398 520 534
## [225] 318 267 267 267 228 228 228 620 483 318 318 620 267 228
## [239] 267 267 267 267 483 696 1710 1710 1710 1710 3196 3196 3196 3088
## [253] 3702 3702 545 397 397 430 430 430 430 545 483 304 304 304
## [267] 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6
## 1621.87481 1621.87481 1621.87481 1621.87481 1328.69909 1328.69909
## 7 8 9 10 11 12
## 1328.69909 964.75682 964.75682 1247.82303 1247.82303 1439.90367
## 13 14 15 16 17 18
## 1743.18890 1480.34170 1480.34170 1480.34170 1621.87481 1621.87481
## 19 20 21 22 23 24
## 1621.87481 1662.31284 1662.31284 1662.31284 1672.42234 1672.42234
## 25 26 27 28 29 30
## 1672.42234 1652.20333 1672.42234 1672.42234 1662.31284 1662.31284
## 31 32 33 34 35 36
## 1662.31284 1581.43678 1581.43678 1581.43678 1581.43678 1348.91810
## 37 38 39 40 41 42
## 1348.91810 1348.91810 1763.40791 1763.40791 1763.40791 1763.40791
## 43 44 45 46 47 48
## 1834.17446 1834.17446 1834.17446 1925.16003 1925.16003 1925.16003
## 49 50 51 52 53 54
## 1480.34170 1480.34170 1480.34170 964.75682 1642.09382 1642.09382
## 55 56 57 58 59 60
## 1642.09382 1662.31284 1662.31284 1662.31284 1793.73643 1793.73643
## 61 82 83 84 85 86
## 1389.35613 924.31879 924.31879 924.31879 924.31879 1601.65579
## 87 88 89 90 91 92
## 1601.65579 1601.65579 1601.65579 1520.77973 1520.77973 1520.77973
## 93 94 95 96 97 138
## 1520.77973 1672.42234 1672.42234 1672.42234 1743.18890 1743.18890
## 144 147 148 149 151 156
## 1702.75087 1712.86037 1712.86037 1601.65579 1672.42234 166.10572
## 157 158 159 160 161 162
## 166.10572 166.10572 166.10572 257.09129 257.09129 257.09129
## 163 164 165 166 167 168
## 610.92405 1025.41386 1025.41386 1025.41386 1025.41386 1086.07091
## 169 170 171 172 173 174
## 1086.07091 1086.07091 1086.07091 1156.83746 1439.90367 1490.45121
## 175 176 177 178 179 180
## 1490.45121 1490.45121 1490.45121 1500.56072 1510.67022 1601.65579
## 181 182 183 184 240 241
## 1601.65579 1601.65579 1601.65579 1743.18890 863.66174 863.66174
## 242 243 244 245 246 247
## 1743.18890 1551.10825 1551.10825 1551.10825 1642.09382 1642.09382
## 248 249 250 251 252 253
## 1642.09382 1642.09382 1015.30435 1015.30435 1015.30435 1672.42234
## 254 255 256 257 258 259
## 1672.42234 1672.42234 1672.42234 1642.09382 1642.09382 1642.09382
## 260 261 262 263 264 265
## 863.66174 1581.43678 1581.43678 1581.43678 1601.65579 1601.65579
## 266 267 268 269 270 271
## 1601.65579 1197.27549 1935.26954 1935.26954 1935.26954 883.88076
## 272 273 274 275 276 277
## 883.88076 1086.07091 1803.84594 1197.27549 1834.17446 1834.17446
## 278 279 280 315 316 317
## 1834.17446 1793.73643 1429.79416 883.88076 1166.94697 1166.94697
## 318 319 320 321 322 323
## 1227.60401 1399.46564 1399.46564 1399.46564 1450.01318 1520.77973
## 324 325 326 327 328 329
## 1520.77973 1874.61249 1874.61249 1874.61249 1874.61249 1874.61249
## 330 331 332 333 334 335
## 1874.61249 1904.94102 1904.94102 1904.94102 1904.94102 1915.05052
## 336 337 338 339 340 341
## 1915.05052 1915.05052 1915.05052 1642.09382 1642.09382 1642.09382
## 342 343 344 345 346 347
## 1925.16003 1935.26954 1935.26954 1935.26954 1935.26954 1965.59806
## 348 349 350 351 352 353
## 1965.59806 1965.59806 1975.70757 1975.70757 1975.70757 1975.70757
## 354 355 356 357 358 359
## 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757
## 360 361 362 363 364 365
## 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757 1975.70757
## 366 367 368 369 370 371
## 1975.70757 600.81455 600.81455 600.81455 1864.50299 1864.50299
## 372 373 374 375 376 377
## 1864.50299 1227.60401 1520.77973 1520.77973 1965.59806 1480.34170
## 378 379 380 381 382 383
## 1631.98431 95.33917 95.33917 95.33917 115.55818 317.74834
## 384 385 386 387 388 389
## 348.07686 459.28144 701.90962 732.23814 732.23814 732.23814
## 390 391 392 393 394 395
## 883.88076 883.88076 883.88076 904.09977 934.42829 954.64731
## 396 397 398 399 400 401
## 954.64731 1086.07091 1187.16598 1207.38500 1257.93253 1257.93253
## 402 403 404 405 406 407
## 1257.93253 1257.93253 1500.56072 1834.17446 348.07686 348.07686
## 408 409 430 431 432 436
## 550.26701 1439.90367 1935.26954 1935.26954 1935.26954 1965.59806
## 437 438 440 441 442 443
## 1975.70757 1975.70757 277.31031 307.63883 307.63883 691.80011
## 444 445 446 447 448 449
## 691.80011 691.80011 691.80011 772.67617 924.31879 1227.60401
## 450 451 452 453 454 455
## 1227.60401 1227.60401 1348.91810 1399.46564 1419.68466 1551.10825
## 456 457 458
## 1551.10825 1621.87481 1884.72200cor(INTLboeing$PricePremium,INTLboeing$PriceRelative)## [1] -0.1652608fit<-lm(PriceEconomy~PercentPremiumSeats,data = INTLboeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = INTLboeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1363.8 -821.4 -79.5 435.0 2297.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1111.56 164.40 6.761 8.37e-11 ***
## PercentPremiumSeats 20.65 10.19 2.026 0.0437 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 968.8 on 271 degrees of freedom
## Multiple R-squared: 0.01492, Adjusted R-squared: 0.01129
## F-statistic: 4.105 on 1 and 271 DF, p-value: 0.04372INTLboeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 2982 2982 2982 2982 2549 2549 2549 2548 524
## [71] 524 524 524 616 616 616 616 172 141 99 99 97 86 1619
## [85] 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019
## [99] 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710
## [113] 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025
## [127] 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049
## [141] 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 1671
## [155] 1452 1452 1408 1947 1947 1947 1356 900 900 1584 1584 1584 1407 1407
## [169] 1407 619 619 619 619 1564 1564 1564 1564 3972 3972 3972 2781 3063
## [183] 3226 3226 3226 3167 3167 3167 3524 3524 3524 3524 3325 3325 3325 3325
## [197] 3589 3589 3589 3702 3702 3243 3243 3243 3243 7414 7414 7414 2470 2470
## [211] 2470 1152 853 853 826 797 797 483 483 483 398 398 520 534
## [225] 318 267 267 267 228 228 228 620 483 318 318 620 267 228
## [239] 267 267 267 267 483 696 1710 1710 1710 1710 3196 3196 3196 3088
## [253] 3702 3702 545 397 397 430 430 430 430 545 483 304 304 304
## [267] 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 9 10 11 12 13 14 15 16
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 17 18 19 20 21 22 23 24
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 25 26 27 28 29 30 31 32
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 33 34 35 36 37 38 39 40
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 41 42 43 44 45 46 47 48
## 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501 1621.501
## 49 50 51 52 53 54 55 56
## 1621.501 1621.501 1621.501 1596.717 1596.717 1596.717 1596.717 1596.717
## 57 58 59 60 61 82 83 84
## 1596.717 1596.717 1596.717 1596.717 1596.717 1498.405 1498.405 1498.405
## 85 86 87 88 89 90 91 92
## 1498.405 1498.405 1498.405 1498.405 1498.405 1459.989 1459.989 1459.989
## 93 94 95 96 97 138 144 147
## 1459.989 1459.989 1459.989 1459.989 1459.989 1428.802 1428.802 1428.802
## 148 149 151 156 157 158 159 160
## 1428.802 1428.802 1428.802 1421.780 1421.780 1421.780 1421.780 1421.780
## 161 162 163 164 165 166 167 168
## 1421.780 1421.780 1421.780 1420.747 1420.747 1420.747 1420.747 1421.780
## 169 170 171 172 173 174 175 176
## 1421.780 1421.780 1421.780 1421.780 1421.780 1420.747 1420.747 1420.747
## 177 178 179 180 181 182 183 184
## 1420.747 1421.780 1421.780 1421.780 1421.780 1421.780 1421.780 1421.780
## 240 241 242 243 244 245 246 247
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 248 249 250 251 252 253 254 255
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 256 257 258 259 260 261 262 263
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 264 265 266 267 268 269 270 271
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 272 273 274 275 276 277 278 279
## 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994 1377.994
## 280 315 316 317 318 319 320 321
## 1377.994 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397
## 322 323 324 325 326 327 328 329
## 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397
## 330 331 332 333 334 335 336 337
## 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397 1384.397
## 338 339 340 341 342 343 344 345
## 1384.397 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189
## 346 347 348 349 350 351 352 353
## 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189 1365.189
## 354 355 356 357 358 359 360 361
## 1361.884 1361.884 1361.884 1361.884 1365.189 1365.189 1365.189 1361.884
## 362 363 364 365 366 367 368 369
## 1361.884 1365.189 1365.189 1365.189 1365.189 1329.871 1329.871 1329.871
## 370 371 372 373 374 375 376 377
## 1329.871 1329.871 1329.871 1329.871 1329.871 1329.871 1329.871 1329.871
## 378 379 380 381 382 383 384 385
## 1329.871 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633
## 386 387 388 389 390 391 392 393
## 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633
## 394 395 396 397 398 399 400 401
## 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633 1347.633
## 402 403 404 405 406 407 408 409
## 1347.633 1347.633 1347.633 1347.633 1318.099 1318.099 1318.099 1318.099
## 430 431 432 436 437 438 440 441
## 1295.380 1295.380 1295.380 1295.380 1295.380 1295.380 1208.841 1208.841
## 442 443 444 445 446 447 448 449
## 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841
## 450 451 452 453 454 455 456 457
## 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841 1208.841
## 458
## 1208.841cor(INTLboeing$PricePremium,INTLboeing$PercentPremiumSeats)## [1] 0.1666565INTLairbus <-INTL[ which(INTL$Aircraft=='AirBus'),]
View(INTLairbus)
summary(INTLairbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance:36 AirBus:145 Min. : 1.25 Aug:39
## British :47 Boeing: 0 1st Qu.: 6.16 Jul:25
## Delta : 6 Median : 8.08 Oct:38
## Jet : 7 Mean : 7.66 Sep:43
## Singapore:16 3rd Qu.: 9.50
## Virgin :33 Max. :13.33
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 0 Min. :147.0 Min. :21.00 Min. :31.00
## International:145 1st Qu.:185.0 1st Qu.:36.00 1st Qu.:31.00
## Median :233.0 Median :38.00 Median :31.00
## Mean :250.7 Mean :40.01 Mean :31.41
## 3rd Qu.:303.0 3rd Qu.:55.00 3rd Qu.:32.00
## Max. :389.0 Max. :55.00 Max. :32.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :38 Min. :18.00 Min. :19.00 Min. : 74
## 1st Qu.:38 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 505
## Median :38 Median :18.00 Median :19.00 Median :1476
## Mean :38 Mean :18.11 Mean :19.65 Mean :1415
## 3rd Qu.:38 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:2369
## Max. :38 Max. :19.00 Max. :21.00 Max. :3220
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 97 Min. :0.0200 Min. :168.0 Min. :6.000
## 1st Qu.: 594 1st Qu.:0.0800 1st Qu.:233.0 1st Qu.:6.000
## Median :2499 Median :0.3100 Median :271.0 Median :7.000
## Mean :1934 Mean :0.4292 Mean :290.7 Mean :6.593
## 3rd Qu.:2997 3rd Qu.:0.6300 3rd Qu.:358.0 3rd Qu.:7.000
## Max. :3563 Max. :1.5600 Max. :427.0 Max. :7.000
## WidthDifference PercentPremiumSeats
## Min. :1.000 Min. : 8.90
## 1st Qu.:1.000 1st Qu.:12.50
## Median :1.000 Median :14.02
## Mean :1.538 Mean :13.88
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :3.000 Max. :20.60mean(INTLairbus$PriceEconomy)## [1] 1414.641mean(INTLairbus$PricePremium)## [1] 1934.145library(plotly)
x1<-c('Jul','Aug','Sept','Oct')
y3<-c(by(INTLairbus$PriceEconomy,INTLairbus$TravelMonth,mean))
y4<-c(by(INTLairbus$PricePremium,INTLairbus$TravelMonth,mean))
data<-data.frame(x1,y3,y4)
data$x1 <- factor(data$x, levels = data[["x1"]])
plot_ly(data, x = ~x1, y = ~y3, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y4, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1796.81 -672.36 -11.04 454.24 1575.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 216.25 191.64 1.128 0.261
## FlightDuration 156.46 23.13 6.765 3.17e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 880.1 on 143 degrees of freedom
## Multiple R-squared: 0.2424, Adjusted R-squared: 0.2371
## F-statistic: 45.76 on 1 and 143 DF, p-value: 3.171e-10INTLairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 336 429 462 557 557 661 676 505 505 505 505 505
## [127] 505 505 505 690 690 690 690 690 690 690 690 1522 1522 2581
## [141] 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1467.9014 1467.9014 1467.9014 1467.9014 1597.7603 1597.7603 1597.7603
## 69 70 71 72 73 99 100
## 1597.7603 1323.9613 1323.9613 1323.9613 1428.7872 1962.3041 1962.3041
## 101 102 103 104 105 106 107
## 1962.3041 1962.3041 1859.0428 1859.0428 1859.0428 1859.0428 2262.7008
## 108 109 110 111 112 113 114
## 2262.7008 2262.7008 1962.3041 1962.3041 1962.3041 854.5916 776.3633
## 115 116 117 118 119 120 121
## 776.3633 593.3091 724.7326 724.7326 776.3633 632.4232 593.3091
## 122 123 124 125 126 127 128
## 593.3091 593.3091 724.7326 502.5643 776.3633 920.3033 593.3091
## 129 130 131 132 133 134 135
## 920.3033 502.5643 593.3091 776.3633 854.5916 659.0208 920.3033
## 136 137 139 140 141 142 143
## 411.8194 854.5916 593.3091 502.5643 502.5643 411.8194 411.8194
## 145 146 150 185 186 187 188
## 776.3633 659.0208 593.3091 1519.5320 1519.5320 1702.5862 1702.5862
## 189 190 191 192 193 194 195
## 1702.5862 1519.5320 1297.3637 1297.3637 1297.3637 1297.3637 1245.7330
## 196 197 198 199 200 201 202
## 1245.7330 1245.7330 1245.7330 1844.9617 1844.9617 1844.9617 1988.9018
## 203 204 205 206 207 208 209
## 1988.9018 1375.5920 1375.5920 1375.5920 1375.5920 1323.9613 1428.7872
## 210 211 212 213 214 215 216
## 1428.7872 1428.7872 1702.5862 1702.5862 1702.5862 1519.5320 1519.5320
## 217 218 219 220 221 222 223
## 1519.5320 1519.5320 1519.5320 1519.5320 1519.5320 1519.5320 1363.0754
## 224 225 226 227 228 229 230
## 1363.0754 1363.0754 1284.8472 1284.8472 1610.2768 1610.2768 1610.2768
## 231 232 233 234 235 236 237
## 1480.4179 1652.5201 1652.5201 1663.4721 1663.4721 1649.3910 1649.3910
## 238 239 308 309 310 311 312
## 1663.4721 1663.4721 1702.5862 1702.5862 1702.5862 1610.2768 1610.2768
## 313 314 410 411 412 413 414
## 1702.5862 1610.2768 2301.8149 2301.8149 2301.8149 2301.8149 1180.0213
## 415 416 417 418 419 420 421
## 1180.0213 1180.0213 1180.0213 2196.9890 2196.9890 2196.9890 2196.9890
## 422 423 424 425 426 427 428
## 1233.2165 1233.2165 1233.2165 1233.2165 2250.1843 2250.1843 1389.6731
## 429 433 434 435 439
## 1389.6731 1532.0486 1546.1296 1546.1296 2250.1843cor(INTLairbus$PriceEconomy,INTLairbus$FlightDuration)## [1] 0.4923677fit<-lm(PriceEconomy~SeatsEconomy,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1688.91 -830.53 -42.62 634.09 2619.76
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2890.3027 260.9009 11.078 < 2e-16 ***
## SeatsEconomy -5.8871 0.9966 -5.907 2.43e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 906.6 on 143 degrees of freedom
## Multiple R-squared: 0.1962, Adjusted R-squared: 0.1905
## F-statistic: 34.9 on 1 and 143 DF, p-value: 2.428e-08INTLairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 336 429 462 557 557 661 676 505 505 505 505 505
## [127] 505 505 505 690 690 690 690 690 690 690 690 1522 1522 2581
## [141] 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1801.1976 1801.1976 1801.1976 1801.1976 1801.1976 1801.1976 1801.1976
## 69 70 71 72 73 99 100
## 1801.1976 1801.1976 1801.1976 1801.1976 1801.1976 1106.5251 1106.5251
## 101 102 103 104 105 106 107
## 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251
## 108 109 110 111 112 113 114
## 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251
## 115 116 117 118 119 120 121
## 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251
## 122 123 124 125 126 127 128
## 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251
## 129 130 131 132 133 134 135
## 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251
## 136 137 139 140 141 142 143
## 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251 1106.5251
## 145 146 150 185 186 187 188
## 1106.5251 1106.5251 1053.5416 1518.6189 1518.6189 1518.6189 1518.6189
## 189 190 191 192 193 194 195
## 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189
## 196 197 198 199 200 201 202
## 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189
## 203 204 205 206 207 208 209
## 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189 1518.6189
## 210 211 212 213 214 215 216
## 1518.6189 1518.6189 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056
## 217 218 219 220 221 222 223
## 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056
## 224 225 226 227 228 229 230
## 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056
## 231 232 233 234 235 236 237
## 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056
## 238 239 308 309 310 311 312
## 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056 2024.9056
## 313 314 410 411 412 413 414
## 2024.9056 2024.9056 929.9135 929.9135 929.9135 929.9135 929.9135
## 415 416 417 418 419 420 421
## 929.9135 929.9135 929.9135 929.9135 929.9135 929.9135 929.9135
## 422 423 424 425 426 427 428
## 929.9135 929.9135 929.9135 929.9135 600.2384 600.2384 600.2384
## 429 433 434 435 439
## 600.2384 600.2384 600.2384 600.2384 600.2384cor(INTLairbus$PriceEconomy,INTLairbus$SeatsEconomy)## [1] -0.4429021fit<-lm(PriceEconomy~PriceRelative,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1421.4 -1073.8 368.0 955.1 1557.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1674.3 123.0 13.613 <2e-16 ***
## PriceRelative -605.0 214.1 -2.825 0.0054 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 984.1 on 143 degrees of freedom
## Multiple R-squared: 0.05287, Adjusted R-squared: 0.04624
## F-statistic: 7.982 on 1 and 143 DF, p-value: 0.005401INTLairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 336 429 462 557 557 661 676 505 505 505 505 505
## [127] 505 505 505 690 690 690 690 690 690 690 690 1522 1522 2581
## [141] 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1232.6858 1232.6858 1232.6858 1232.6858 1438.3820 1438.3820 1438.3820
## 69 70 71 72 73 99 100
## 1438.3820 1517.0305 1517.0305 1517.0305 1613.8287 1377.8831 1377.8831
## 101 102 103 104 105 106 107
## 1377.8831 1377.8831 1123.7878 1123.7878 1123.7878 1123.7878 1389.9829
## 108 109 110 111 112 113 114
## 1389.9829 1389.9829 905.9918 905.9918 1456.5316 1638.0283 1613.8287
## 115 116 117 118 119 120 121
## 1613.8287 1650.1281 1607.7788 1607.7788 1625.9285 1619.8786 1644.0782
## 122 123 124 125 126 127 128
## 1644.0782 1607.7788 1589.6292 1571.4795 1577.5294 1583.5793 1631.9784
## 129 130 131 132 133 134 135
## 1571.4795 1565.4296 1589.6292 1595.6791 1577.5294 1565.4296 1565.4296
## 136 137 139 140 141 142 143
## 1523.0804 1553.3298 1559.3797 1535.1802 1535.1802 1492.8310 1492.8310
## 145 146 150 185 186 187 188
## 1523.0804 1498.8809 1486.7811 1396.0328 1396.0328 1444.4319 1444.4319
## 189 190 191 192 193 194 195
## 1444.4319 1492.8310 1020.9397 1020.9397 1020.9397 1020.9397 1051.1891
## 196 197 198 199 200 201 202
## 1051.1891 1051.1891 1051.1891 1166.1370 1166.1370 1166.1370 1377.8831
## 203 204 205 206 207 208 209
## 1377.8831 1426.2822 1426.2822 1426.2822 1426.2822 1517.0305 1613.8287
## 210 211 212 213 214 215 216
## 1613.8287 1613.8287 730.5451 966.4907 1293.1847 1625.9285 1625.9285
## 217 218 219 220 221 222 223
## 1625.9285 1625.9285 1625.9285 1625.9285 1625.9285 1625.9285 1625.9285
## 224 225 226 227 228 229 230
## 1625.9285 1625.9285 1631.9784 1631.9784 1631.9784 1631.9784 1631.9784
## 231 232 233 234 235 236 237
## 1650.1281 1656.1779 1656.1779 1656.1779 1656.1779 1656.1779 1656.1779
## 238 239 308 309 310 311 312
## 1656.1779 1656.1779 766.8444 1093.5384 1178.2368 1420.2323 1420.2323
## 313 314 410 411 412 413 414
## 1432.3321 1444.4319 1075.3887 1075.3887 1075.3887 1075.3887 1075.3887
## 415 416 417 418 419 420 421
## 1075.3887 1075.3887 1075.3887 1305.2844 1305.2844 1305.2844 1305.2844
## 422 423 424 425 426 427 428
## 1305.2844 1305.2844 1305.2844 1305.2844 972.5406 972.5406 1625.9285
## 429 433 434 435 439
## 1625.9285 1650.1281 1650.1281 1650.1281 1662.2278cor(INTLairbus$PriceEconomy,INTLairbus$PriceRelative)## [1] -0.2299241fit<-lm(PriceEconomy~PercentPremiumSeats,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1297.44 -1035.08 66.91 959.91 1696.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1953.26 414.31 4.714 5.69e-06 ***
## PercentPremiumSeats -38.81 29.24 -1.327 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1005 on 143 degrees of freedom
## Multiple R-squared: 0.01217, Adjusted R-squared: 0.005261
## F-statistic: 1.762 on 1 and 143 DF, p-value: 0.1865INTLairbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 336 429 462 557 557 661 676 505 505 505 505 505
## [127] 505 505 505 690 690 690 690 690 690 690 690 1522 1522 2581
## [141] 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68 69
## 1153.691 1153.691 1153.691 1153.691 1153.691 1153.691 1153.691 1153.691
## 70 71 72 73 99 100 101 102
## 1153.691 1153.691 1153.691 1153.691 1357.076 1357.076 1357.076 1357.076
## 103 104 105 106 107 108 109 110
## 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076
## 111 112 113 114 115 116 117 118
## 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076
## 119 120 121 122 123 124 125 126
## 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076
## 127 128 129 130 131 132 133 134
## 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076
## 135 136 137 139 140 141 142 143
## 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076 1357.076
## 145 146 150 185 186 187 188 189
## 1357.076 1357.076 1371.437 1409.087 1409.087 1409.087 1409.087 1409.087
## 190 191 192 193 194 195 196 197
## 1409.087 1409.087 1409.087 1409.087 1409.087 1409.087 1409.087 1409.087
## 198 199 200 201 202 203 204 205
## 1409.087 1409.087 1409.087 1409.087 1409.087 1409.087 1409.087 1409.087
## 206 207 208 209 210 211 212 213
## 1409.087 1409.087 1409.087 1409.087 1409.087 1409.087 1468.084 1468.084
## 214 215 216 217 218 219 220 221
## 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084
## 222 223 224 225 226 227 228 229
## 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084
## 230 231 232 233 234 235 236 237
## 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084
## 238 239 308 309 310 311 312 313
## 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084 1468.084
## 314 410 411 412 413 414 415 416
## 1468.084 1574.435 1574.435 1574.435 1574.435 1574.435 1574.435 1574.435
## 417 418 419 420 421 422 423 424
## 1574.435 1574.435 1574.435 1574.435 1574.435 1574.435 1574.435 1574.435
## 425 426 427 428 429 433 434 435
## 1574.435 1607.815 1607.815 1607.815 1607.815 1607.815 1607.815 1607.815
## 439
## 1607.815fit<-lm(PricePremium~FlightDuration,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2278.3 -630.8 393.3 884.8 1326.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 113.11 213.82 0.529 0.598
## FlightDuration 237.75 25.81 9.213 3.66e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 982 on 143 degrees of freedom
## Multiple R-squared: 0.3725, Adjusted R-squared: 0.3681
## F-statistic: 84.88 on 1 and 143 DF, p-value: 3.664e-16INTLairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 841 841 841 789 789 928 931 1004 1004 1004 1004 1004
## [127] 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781
## [141] 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68
## 2015.0768 2015.0768 2015.0768 2015.0768 2212.4060 2212.4060 2212.4060
## 69 70 71 72 73 99 100
## 2212.4060 1796.3505 1796.3505 1796.3505 1955.6403 2766.3541 2766.3541
## 101 102 103 104 105 106 107
## 2766.3541 2766.3541 2609.4417 2609.4417 2609.4417 2609.4417 3222.8263
## 108 109 110 111 112 113 114
## 3222.8263 3222.8263 2766.3541 2766.3541 2766.3541 1083.1127 964.2397
## 115 116 117 118 119 120 121
## 964.2397 686.0769 885.7835 885.7835 964.2397 745.5134 686.0769
## 122 123 124 125 126 127 128
## 686.0769 686.0769 885.7835 548.1843 964.2397 1182.9660 686.0769
## 129 130 131 132 133 134 135
## 1182.9660 548.1843 686.0769 964.2397 1083.1127 785.9302 1182.9660
## 136 137 139 140 141 142 143
## 410.2916 1083.1127 686.0769 548.1843 548.1843 410.2916 410.2916
## 145 146 150 185 186 187 188
## 964.2397 785.9302 686.0769 2093.5330 2093.5330 2371.6958 2371.6958
## 189 190 191 192 193 194 195
## 2371.6958 2093.5330 1755.9337 1755.9337 1755.9337 1755.9337 1677.4776
## 196 197 198 199 200 201 202
## 1677.4776 1677.4776 1677.4776 2588.0446 2588.0446 2588.0446 2806.7709
## 203 204 205 206 207 208 209
## 2806.7709 1874.8067 1874.8067 1874.8067 1874.8067 1796.3505 1955.6403
## 210 211 212 213 214 215 216
## 1955.6403 1955.6403 2371.6958 2371.6958 2371.6958 2093.5330 2093.5330
## 217 218 219 220 221 222 223
## 2093.5330 2093.5330 2093.5330 2093.5330 2093.5330 2093.5330 1855.7870
## 224 225 226 227 228 229 230
## 1855.7870 1855.7870 1736.9141 1736.9141 2231.4257 2231.4257 2231.4257
## 231 232 233 234 235 236 237
## 2034.0965 2295.6171 2295.6171 2312.2593 2312.2593 2290.8621 2290.8621
## 238 239 308 309 310 311 312
## 2312.2593 2312.2593 2371.6958 2371.6958 2371.6958 2231.4257 2231.4257
## 313 314 410 411 412 413 414
## 2371.6958 2231.4257 3282.2628 3282.2628 3282.2628 3282.2628 1577.6243
## 415 416 417 418 419 420 421
## 1577.6243 1577.6243 1577.6243 3122.9730 3122.9730 3122.9730 3122.9730
## 422 423 424 425 426 427 428
## 1658.4579 1658.4579 1658.4579 1658.4579 3203.8066 3203.8066 1896.2039
## 429 433 434 435 439
## 1896.2039 2112.5527 2133.9498 2133.9498 3203.8066cor(INTLairbus$PricePremium,INTLairbus$FlightDuration)## [1] 0.6103023fit<-lm(PricePremium~SeatsEconomy,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1750.1 -1305.7 267.9 735.9 2162.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3396.894 333.082 10.198 < 2e-16 ***
## SeatsEconomy -5.836 1.272 -4.587 9.75e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1157 on 143 degrees of freedom
## Multiple R-squared: 0.1282, Adjusted R-squared: 0.1222
## F-statistic: 21.04 on 1 and 143 DF, p-value: 9.753e-06INTLairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 841 841 841 789 789 928 931 1004 1004 1004 1004 1004
## [127] 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781
## [141] 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 2317.319 2317.319 2317.319 2317.319 2317.319 2317.319 2317.319 2317.319
## 70 71 72 73 99 100 101 102
## 2317.319 2317.319 2317.319 2317.319 1628.725 1628.725 1628.725 1628.725
## 103 104 105 106 107 108 109 110
## 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725
## 111 112 113 114 115 116 117 118
## 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725
## 119 120 121 122 123 124 125 126
## 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725
## 127 128 129 130 131 132 133 134
## 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725
## 135 136 137 139 140 141 142 143
## 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725 1628.725
## 145 146 150 185 186 187 188 189
## 1628.725 1628.725 1576.205 2037.213 2037.213 2037.213 2037.213 2037.213
## 190 191 192 193 194 195 196 197
## 2037.213 2037.213 2037.213 2037.213 2037.213 2037.213 2037.213 2037.213
## 198 199 200 201 202 203 204 205
## 2037.213 2037.213 2037.213 2037.213 2037.213 2037.213 2037.213 2037.213
## 206 207 208 209 210 211 212 213
## 2037.213 2037.213 2037.213 2037.213 2037.213 2037.213 2539.069 2539.069
## 214 215 216 217 218 219 220 221
## 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069
## 222 223 224 225 226 227 228 229
## 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069
## 230 231 232 233 234 235 236 237
## 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069
## 238 239 308 309 310 311 312 313
## 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069 2539.069
## 314 410 411 412 413 414 415 416
## 2539.069 1453.658 1453.658 1453.658 1453.658 1453.658 1453.658 1453.658
## 417 418 419 420 421 422 423 424
## 1453.658 1453.658 1453.658 1453.658 1453.658 1453.658 1453.658 1453.658
## 425 426 427 428 429 433 434 435
## 1453.658 1126.868 1126.868 1126.868 1126.868 1126.868 1126.868 1126.868
## 439
## 1126.868cor(INTLairbus$PricePremium,INTLairbus$SeatsEconomy)## [1] -0.3581187fit<-lm(PricePremium~SeatsPremium,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1842.7 -1077.4 227.3 974.0 2178.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3402.141 308.045 11.044 < 2e-16 ***
## SeatsPremium -36.687 7.324 -5.009 1.59e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1143 on 143 degrees of freedom
## Multiple R-squared: 0.1493, Adjusted R-squared: 0.1433
## F-statistic: 25.09 on 1 and 143 DF, p-value: 1.586e-06INTLairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 841 841 841 789 789 928 931 1004 1004 1004 1004 1004
## [127] 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781
## [141] 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 1641.153 1641.153 1641.153 1641.153 1641.153 1641.153 1641.153 1641.153
## 70 71 72 73 99 100 101 102
## 1641.153 1641.153 1641.153 1641.153 1384.342 1384.342 1384.342 1384.342
## 103 104 105 106 107 108 109 110
## 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342
## 111 112 113 114 115 116 117 118
## 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342
## 119 120 121 122 123 124 125 126
## 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342
## 127 128 129 130 131 132 133 134
## 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342
## 135 136 137 139 140 141 142 143
## 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342 1384.342
## 145 146 150 185 186 187 188 189
## 1384.342 1384.342 1384.342 2008.025 2008.025 2008.025 2008.025 2008.025
## 190 191 192 193 194 195 196 197
## 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025
## 198 199 200 201 202 203 204 205
## 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025
## 206 207 208 209 210 211 212 213
## 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025 2631.709 2631.709
## 214 215 216 217 218 219 220 221
## 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709
## 222 223 224 225 226 227 228 229
## 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709
## 230 231 232 233 234 235 236 237
## 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709
## 238 239 308 309 310 311 312 313
## 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709 2631.709
## 314 410 411 412 413 414 415 416
## 2631.709 2081.400 2081.400 2081.400 2081.400 2081.400 2081.400 2081.400
## 417 418 419 420 421 422 423 424
## 2081.400 2081.400 2081.400 2081.400 2081.400 2081.400 2081.400 2081.400
## 425 426 427 428 429 433 434 435
## 2081.400 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025 2008.025
## 439
## 2008.025cor(INTLairbus$PricePremium,INTLairbus$SeatsPremium)## [1] -0.3863785fit<-lm(PricePremium~PriceRelative,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1785.4 -1197.4 573.2 1063.4 1602.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1748.0 153.5 11.386 <2e-16 ***
## PriceRelative 433.6 267.3 1.622 0.107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1228 on 143 degrees of freedom
## Multiple R-squared: 0.01807, Adjusted R-squared: 0.0112
## F-statistic: 2.631 on 1 and 143 DF, p-value: 0.107INTLairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 841 841 841 789 789 928 931 1004 1004 1004 1004 1004
## [127] 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781
## [141] 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 2064.557 2064.557 2064.557 2064.557 1917.129 1917.129 1917.129 1917.129
## 70 71 72 73 99 100 101 102
## 1860.760 1860.760 1860.760 1791.383 1960.490 1960.490 1960.490 1960.490
## 103 104 105 106 107 108 109 110
## 2142.606 2142.606 2142.606 2142.606 1951.818 1951.818 1951.818 2298.706
## 111 112 113 114 115 116 117 118
## 2298.706 1904.121 1774.038 1791.383 1791.383 1765.366 1795.719 1795.719
## 119 120 121 122 123 124 125 126
## 1782.710 1787.046 1769.702 1769.702 1795.719 1808.727 1821.735 1817.399
## 127 128 129 130 131 132 133 134
## 1813.063 1778.374 1821.735 1826.071 1808.727 1804.391 1817.399 1826.071
## 135 136 137 139 140 141 142 143
## 1826.071 1856.424 1834.744 1830.407 1847.752 1847.752 1878.105 1878.105
## 145 146 150 185 186 187 188 189
## 1856.424 1873.768 1882.441 1947.482 1947.482 1912.793 1912.793 1912.793
## 190 191 192 193 194 195 196 197
## 1878.105 2216.320 2216.320 2216.320 2216.320 2194.640 2194.640 2194.640
## 198 199 200 201 202 203 204 205
## 2194.640 2112.254 2112.254 2112.254 1960.490 1960.490 1925.802 1925.802
## 206 207 208 209 210 211 212 213
## 1925.802 1925.802 1860.760 1791.383 1791.383 1791.383 2424.453 2255.345
## 214 215 216 217 218 219 220 221
## 2021.196 1782.710 1782.710 1782.710 1782.710 1782.710 1782.710 1782.710
## 222 223 224 225 226 227 228 229
## 1782.710 1782.710 1782.710 1782.710 1778.374 1778.374 1778.374 1778.374
## 230 231 232 233 234 235 236 237
## 1778.374 1765.366 1761.030 1761.030 1761.030 1761.030 1761.030 1761.030
## 238 239 308 309 310 311 312 313
## 1761.030 1761.030 2398.436 2164.287 2103.582 1930.138 1930.138 1921.465
## 314 410 411 412 413 414 415 416
## 1912.793 2177.295 2177.295 2177.295 2177.295 2177.295 2177.295 2177.295
## 417 418 419 420 421 422 423 424
## 2177.295 2012.524 2012.524 2012.524 2012.524 2012.524 2012.524 2012.524
## 425 426 427 428 429 433 434 435
## 2012.524 2251.009 2251.009 1782.710 1782.710 1765.366 1765.366 1765.366
## 439
## 1756.694cor(INTLairbus$PricePremium,INTLairbus$PriceRelative)## [1] 0.1344226fit<-lm(PricePremium~PercentPremiumSeats,data = INTLairbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = INTLairbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1824.7 -1265.2 566.5 1064.5 1645.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2088.93 510.86 4.089 7.2e-05 ***
## PercentPremiumSeats -11.15 36.06 -0.309 0.758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1239 on 143 degrees of freedom
## Multiple R-squared: 0.0006686, Adjusted R-squared: -0.00632
## F-statistic: 0.09568 on 1 and 143 DF, p-value: 0.7575INTLairbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 841 841 841 789 789 928 931 1004 1004 1004 1004 1004
## [127] 1004 1004 1004 1110 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781
## [141] 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 1859.156 1859.156 1859.156 1859.156 1859.156 1859.156 1859.156 1859.156
## 70 71 72 73 99 100 101 102
## 1859.156 1859.156 1859.156 1859.156 1917.602 1917.602 1917.602 1917.602
## 103 104 105 106 107 108 109 110
## 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602
## 111 112 113 114 115 116 117 118
## 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602
## 119 120 121 122 123 124 125 126
## 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602
## 127 128 129 130 131 132 133 134
## 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602
## 135 136 137 139 140 141 142 143
## 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602 1917.602
## 145 146 150 185 186 187 188 189
## 1917.602 1917.602 1921.729 1932.549 1932.549 1932.549 1932.549 1932.549
## 190 191 192 193 194 195 196 197
## 1932.549 1932.549 1932.549 1932.549 1932.549 1932.549 1932.549 1932.549
## 198 199 200 201 202 203 204 205
## 1932.549 1932.549 1932.549 1932.549 1932.549 1932.549 1932.549 1932.549
## 206 207 208 209 210 211 212 213
## 1932.549 1932.549 1932.549 1932.549 1932.549 1932.549 1949.503 1949.503
## 214 215 216 217 218 219 220 221
## 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503
## 222 223 224 225 226 227 228 229
## 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503
## 230 231 232 233 234 235 236 237
## 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503
## 238 239 308 309 310 311 312 313
## 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503 1949.503
## 314 410 411 412 413 414 415 416
## 1949.503 1980.064 1980.064 1980.064 1980.064 1980.064 1980.064 1980.064
## 417 418 419 420 421 422 423 424
## 1980.064 1980.064 1980.064 1980.064 1980.064 1980.064 1980.064 1980.064
## 425 426 427 428 429 433 434 435
## 1980.064 1989.657 1989.657 1989.657 1989.657 1989.657 1989.657 1989.657
## 439
## 1989.657cor(INTLairbus$PricePremium,INTLairbus$PercentPremiumSeats)## [1] -0.02585822Now It’s time for comparison-
mean(INTL$PriceEconomy)## [1] 1419.943mean(INTL$PricePremium)## [1] 1984.909library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(INTL$PriceEconomy,INTL$TravelMonth,mean))
y2<-c(by(INTL$PricePremium,INTL$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')mean(INTL$PriceEconomy)## [1] 1419.943mean(INTL$PricePremium)## [1] 1984.909library(plotly)
x<-c('British','Virgin','Delta','Jet','AirFrance','Singapore')
y1<-c(by(INTL$PriceEconomy,INTL$Airline,mean))
y2<-c(by(INTL$PricePremium,INTL$Airline,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Airlines", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')short Analysis of all International flights
fit<-lm(PriceEconomy~FlightDuration,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1706.5 -576.5 -125.9 450.5 1849.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236.50 105.25 2.247 0.0252 *
## FlightDuration 148.16 12.12 12.228 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 845.7 on 416 degrees of freedom
## Multiple R-squared: 0.2644, Adjusted R-squared: 0.2626
## F-statistic: 149.5 on 1 and 416 DF, p-value: < 2.2e-16INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 2051.4611 2051.4611 2051.4611 2051.4611 1445.4877 1445.4877 1445.4877
## 8 9 10 11 12 13 14
## 1199.5426 1199.5426 1940.3413 1940.3413 1940.3413 1940.3413 1952.1941
## 15 16 17 18 19 20 21
## 1952.1941 1952.1941 1593.6475 1593.6475 1593.6475 1236.5825 1236.5825
## 22 23 24 25 26 27 28
## 1236.5825 1223.2481 1223.2481 1223.2481 1532.9020 1532.9020 1532.9020
## 29 30 31 32 33 34 35
## 963.9686 963.9686 963.9686 803.9560 803.9560 803.9560 803.9560
## 36 37 38 39 40 41 42
## 2236.6608 2236.6608 2236.6608 803.9560 803.9560 803.9560 803.9560
## 43 44 45 46 47 48 49
## 1038.0484 1038.0484 1038.0484 1458.8221 1458.8221 1458.8221 2125.5410
## 50 51 52 53 54 55 56
## 2125.5410 2125.5410 1199.5426 1878.1142 1878.1142 1878.1142 1137.3155
## 57 58 59 60 61 62 63
## 1137.3155 1137.3155 2088.5010 2021.8291 2088.5010 1421.7822 1421.7822
## 64 65 66 67 68 69 70
## 1421.7822 1421.7822 1544.7548 1544.7548 1544.7548 1544.7548 1285.4752
## 71 72 73 82 83 84 85
## 1285.4752 1285.4752 1384.7422 1248.4353 1248.4353 1248.4353 1248.4353
## 86 87 88 89 90 91 92
## 1359.5551 1359.5551 1359.5551 1359.5551 692.8362 692.8362 692.8362
## 93 94 95 96 97 99 100
## 692.8362 692.8362 692.8362 692.8362 692.8362 1889.9670 1889.9670
## 101 102 103 104 105 106 107
## 1889.9670 1889.9670 1792.1815 1792.1815 1792.1815 1792.1815 2174.4337
## 108 109 110 111 112 113 114
## 2174.4337 2174.4337 1889.9670 1889.9670 1889.9670 840.9960 766.9161
## 115 116 117 118 119 120 121
## 766.9161 593.5692 718.0234 718.0234 766.9161 630.6091 593.5692
## 122 123 124 125 126 127 128
## 593.5692 593.5692 718.0234 507.6365 766.9161 903.2231 593.5692
## 129 130 131 132 133 134 135
## 903.2231 507.6365 593.5692 766.9161 840.9960 655.7963 903.2231
## 136 137 138 139 140 141 142
## 421.7039 840.9960 421.7039 593.5692 507.6365 507.6365 421.7039
## 143 144 145 146 147 148 149
## 421.7039 421.7039 766.9161 655.7963 433.5567 433.5567 421.7039
## 150 151 156 157 158 159 160
## 593.5692 433.5567 1903.3013 1903.3013 1903.3013 1903.3013 2026.2739
## 161 162 163 164 165 166 167
## 2026.2739 2026.2739 2026.2739 1704.7673 1704.7673 1704.7673 1704.7673
## 168 169 170 171 172 173 174
## 1841.0743 1841.0743 1841.0743 1841.0743 1778.8472 2100.3538 1829.2215
## 175 176 177 178 179 180 181
## 1829.2215 1829.2215 1829.2215 2100.3538 1915.1541 1371.4079 1371.4079
## 182 183 184 185 186 187 188
## 1371.4079 1371.4079 2100.3538 1470.6749 1470.6749 1644.0218 1644.0218
## 189 190 191 192 193 194 195
## 1644.0218 1470.6749 1260.2881 1260.2881 1260.2881 1260.2881 1211.3953
## 196 197 198 199 200 201 202
## 1211.3953 1211.3953 1211.3953 1778.8472 1778.8472 1778.8472 1915.1541
## 203 204 205 206 207 208 209
## 1915.1541 1334.3679 1334.3679 1334.3679 1334.3679 1285.4752 1384.7422
## 210 211 212 213 214 215 216
## 1384.7422 1384.7422 1644.0218 1644.0218 1644.0218 1470.6749 1470.6749
## 217 218 219 220 221 222 223
## 1470.6749 1470.6749 1470.6749 1470.6749 1470.6749 1470.6749 1322.5151
## 224 225 226 227 228 229 230
## 1322.5151 1322.5151 1248.4353 1248.4353 1556.6075 1556.6075 1556.6075
## 231 232 233 234 235 236 237
## 1433.6350 1596.6107 1596.6107 1606.9819 1606.9819 1593.6475 1593.6475
## 238 239 240 241 242 243 244
## 1606.9819 1606.9819 1778.8472 1778.8472 1778.8472 1866.2614 1866.2614
## 245 246 247 248 249 250 251
## 1866.2614 1704.7673 1704.7673 1704.7673 1704.7673 1507.7148 1507.7148
## 252 253 254 255 256 257 258
## 1507.7148 1927.0069 1927.0069 1927.0069 1927.0069 1618.8346 1618.8346
## 259 260 261 262 263 264 265
## 1618.8346 1556.6075 1519.5676 1519.5676 1519.5676 1310.6624 1310.6624
## 266 267 268 269 270 271 272
## 1310.6624 1556.6075 1285.4752 1285.4752 1285.4752 1927.0069 1927.0069
## 273 274 275 276 277 278 279
## 1927.0069 1927.0069 1556.6075 1878.1142 1878.1142 1878.1142 1285.4752
## 280 308 309 310 311 312 313
## 1878.1142 1644.0218 1644.0218 1644.0218 1556.6075 1556.6075 1644.0218
## 314 315 316 317 318 319 320
## 1556.6075 2297.4063 2297.4063 2297.4063 2297.4063 2075.1667 2075.1667
## 321 322 323 324 325 326 327
## 2075.1667 1841.0743 1841.0743 1841.0743 2408.5261 2408.5261 2408.5261
## 328 329 330 331 332 333 334
## 1667.7274 1667.7274 1667.7274 803.9560 803.9560 803.9560 803.9560
## 335 336 337 338 339 340 341
## 2125.5410 2125.5410 2125.5410 2125.5410 1470.6749 1470.6749 1470.6749
## 342 343 344 345 346 347 348
## 1347.7023 1248.4353 1532.9020 1532.9020 1532.9020 1371.4079 1248.4353
## 349 350 351 352 353 354 355
## 1248.4353 1644.0218 1644.0218 1644.0218 1644.0218 1384.7422 1384.7422
## 356 357 358 359 360 361 362
## 1384.7422 1396.5950 1630.6874 1630.6874 1630.6874 1977.3812 1977.3812
## 363 364 365 366 367 368 369
## 2001.0868 2001.0868 2001.0868 2001.0868 2285.5535 2285.5535 2285.5535
## 370 371 372 373 374 375 376
## 2211.4736 2211.4736 2211.4736 1556.6075 1556.6075 1556.6075 1655.8746
## 377 378 379 380 381 382 383
## 1655.8746 1655.8746 718.0234 718.0234 718.0234 718.0234 718.0234
## 384 385 386 387 388 389 390
## 852.8488 852.8488 840.9960 606.9036 606.9036 606.9036 630.6091
## 391 392 393 394 395 396 397
## 630.6091 630.6091 852.8488 718.0234 840.9960 852.8488 852.8488
## 398 399 400 401 402 403 404
## 606.9036 630.6091 878.0359 878.0359 878.0359 878.0359 718.0234
## 405 406 407 408 409 410 411
## 718.0234 1260.2881 1260.2881 1260.2881 1260.2881 2211.4736 2211.4736
## 412 413 414 415 416 417 418
## 2211.4736 2211.4736 1149.1682 1149.1682 1149.1682 1149.1682 2112.2066
## 419 420 421 422 423 424 425
## 2112.2066 2112.2066 2112.2066 1199.5426 1199.5426 1199.5426 1199.5426
## 426 427 428 429 430 431 432
## 2162.5809 2162.5809 1347.7023 1347.7023 1815.8871 1815.8871 1815.8871
## 433 434 435 436 437 438 439
## 1482.5277 1495.8620 1495.8620 1495.8620 1940.3413 1940.3413 2162.5809
## 440 441 442 443 444 445 446
## 1075.0884 704.6890 704.6890 1075.0884 1075.0884 1075.0884 1075.0884
## 447 448 449 450 451 452 453
## 1075.0884 704.6890 618.7564 618.7564 618.7564 704.6890 618.7564
## 454 455 456 457 458
## 618.7564 618.7564 618.7564 718.0234 618.7564cor(INTL$PriceEconomy,INTL$FlightDuration)## [1] 0.5141957fit<-lm(PriceEconomy~SeatsEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1393.98 -889.79 10.18 642.51 2188.12
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1331.9162 142.9923 9.315 <2e-16 ***
## SeatsEconomy 0.4193 0.6413 0.654 0.514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 985.5 on 416 degrees of freedom
## Multiple R-squared: 0.001027, Adjusted R-squared: -0.001375
## F-statistic: 0.4276 on 1 and 416 DF, p-value: 0.5136INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076
## 9 10 11 12 13 14 15 16
## 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076
## 17 18 19 20 21 22 23 24
## 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076
## 25 26 27 28 29 30 31 32
## 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076
## 33 34 35 36 37 38 39 40
## 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076
## 41 42 43 44 45 46 47 48
## 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076 1383.076
## 49 50 51 52 53 54 55 56
## 1383.076 1383.076 1383.076 1385.172 1385.172 1385.172 1385.172 1385.172
## 57 58 59 60 61 62 63 64
## 1385.172 1385.172 1385.172 1385.172 1385.172 1409.494 1409.494 1409.494
## 65 66 67 68 69 70 71 72
## 1409.494 1409.494 1409.494 1409.494 1409.494 1409.494 1409.494 1409.494
## 73 82 83 84 85 86 87 88
## 1409.494 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816
## 89 90 91 92 93 94 95 96
## 1433.816 1389.785 1389.785 1389.785 1389.785 1389.785 1389.785 1389.785
## 97 99 100 101 102 103 104 105
## 1389.785 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976
## 106 107 108 109 110 111 112 113
## 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976
## 114 115 116 117 118 119 120 121
## 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976
## 122 123 124 125 126 127 128 129
## 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976
## 130 131 132 133 134 135 136 137
## 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976
## 138 139 140 141 142 143 144 145
## 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976 1458.976
## 146 147 148 149 150 151 156 157
## 1458.976 1458.976 1458.976 1458.976 1462.750 1458.976 1414.946 1414.946
## 158 159 160 161 162 163 164 165
## 1414.946 1414.946 1414.946 1414.946 1414.946 1414.946 1489.169 1489.169
## 166 167 168 169 170 171 172 173
## 1489.169 1489.169 1414.946 1414.946 1414.946 1414.946 1414.946 1414.946
## 174 175 176 177 178 179 180 181
## 1489.169 1489.169 1489.169 1489.169 1414.946 1414.946 1414.946 1414.946
## 182 183 184 185 186 187 188 189
## 1414.946 1414.946 1414.946 1429.623 1429.623 1429.623 1429.623 1429.623
## 190 191 192 193 194 195 196 197
## 1429.623 1429.623 1429.623 1429.623 1429.623 1429.623 1429.623 1429.623
## 198 199 200 201 202 203 204 205
## 1429.623 1429.623 1429.623 1429.623 1429.623 1429.623 1429.623 1429.623
## 206 207 208 209 210 211 212 213
## 1429.623 1429.623 1429.623 1429.623 1429.623 1429.623 1393.559 1393.559
## 214 215 216 217 218 219 220 221
## 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559
## 222 223 224 225 226 227 228 229
## 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559
## 230 231 232 233 234 235 236 237
## 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559 1393.559
## 238 239 240 241 242 243 244 245
## 1393.559 1393.559 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816
## 246 247 248 249 250 251 252 253
## 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816
## 254 255 256 257 258 259 260 261
## 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816
## 262 263 264 265 266 267 268 269
## 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816
## 270 271 272 273 274 275 276 277
## 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816 1433.816
## 278 279 280 308 309 310 311 312
## 1433.816 1433.816 1433.816 1393.559 1393.559 1393.559 1393.559 1393.559
## 313 314 315 316 317 318 319 320
## 1393.559 1393.559 1409.075 1409.075 1409.075 1409.075 1409.075 1409.075
## 321 322 323 324 325 326 327 328
## 1409.075 1409.075 1409.075 1409.075 1409.075 1409.075 1409.075 1409.075
## 329 330 331 332 333 334 335 336
## 1409.075 1409.075 1409.075 1409.075 1409.075 1409.075 1409.075 1409.075
## 337 338 339 340 341 342 343 344
## 1409.075 1409.075 1415.784 1415.784 1415.784 1415.784 1415.784 1415.784
## 345 346 347 348 349 350 351 352
## 1415.784 1415.784 1415.784 1415.784 1415.784 1415.784 1415.784 1415.784
## 353 354 355 356 357 358 359 360
## 1415.784 1404.881 1404.881 1404.881 1404.881 1415.784 1415.784 1415.784
## 361 362 363 364 365 366 367 368
## 1404.881 1404.881 1415.784 1415.784 1415.784 1415.784 1417.042 1417.042
## 369 370 371 372 373 374 375 376
## 1417.042 1417.042 1417.042 1417.042 1417.042 1417.042 1417.042 1417.042
## 377 378 379 380 381 382 383 384
## 1417.042 1417.042 1383.914 1383.914 1383.914 1383.914 1383.914 1383.914
## 385 386 387 388 389 390 391 392
## 1383.914 1383.914 1383.914 1383.914 1383.914 1383.914 1383.914 1383.914
## 393 394 395 396 397 398 399 400
## 1383.914 1383.914 1383.914 1383.914 1383.914 1383.914 1383.914 1383.914
## 401 402 403 404 405 406 407 408
## 1383.914 1383.914 1383.914 1383.914 1383.914 1422.494 1422.494 1422.494
## 409 410 411 412 413 414 415 416
## 1422.494 1471.557 1471.557 1471.557 1471.557 1471.557 1471.557 1471.557
## 417 418 419 420 421 422 423 424
## 1471.557 1471.557 1471.557 1471.557 1471.557 1471.557 1471.557 1471.557
## 425 426 427 428 429 430 431 432
## 1471.557 1495.040 1495.040 1495.040 1495.040 1495.040 1495.040 1495.040
## 433 434 435 436 437 438 439 440
## 1495.040 1495.040 1495.040 1495.040 1495.040 1495.040 1495.040 1399.849
## 441 442 443 444 445 446 447 448
## 1399.849 1399.849 1399.849 1399.849 1399.849 1399.849 1399.849 1399.849
## 449 450 451 452 453 454 455 456
## 1399.849 1399.849 1399.849 1399.849 1399.849 1399.849 1399.849 1399.849
## 457 458
## 1399.849 1399.849cor(INTL$PriceEconomy,INTL$SeatsEconomy)## [1] 0.03204234fit<-lm(PriceEconomy~PriceRelative,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1592.31 -661.40 57.82 740.28 2447.06
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1885.3 67.5 27.929 <2e-16 ***
## PriceRelative -885.1 97.3 -9.097 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 900.5 on 416 degrees of freedom
## Multiple R-squared: 0.1659, Adjusted R-squared: 0.1639
## F-statistic: 82.75 on 1 and 416 DF, p-value: < 2.2e-16INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1548.9223 1548.9223 1548.9223 1548.9223 1292.2334 1292.2334 1292.2334
## 8 9 10 11 12 13 14
## 973.5851 973.5851 1221.4227 1221.4227 1389.5982 1655.1384 1425.0035
## 15 16 17 18 19 20 21
## 1425.0035 1425.0035 1548.9223 1548.9223 1548.9223 1584.3277 1584.3277
## 22 23 24 25 26 27 28
## 1584.3277 1593.1790 1593.1790 1593.1790 1575.4763 1593.1790 1593.1790
## 29 30 31 32 33 34 35
## 1584.3277 1584.3277 1584.3277 1513.5169 1513.5169 1513.5169 1513.5169
## 36 37 38 39 40 41 42
## 1309.9361 1309.9361 1309.9361 1672.8411 1672.8411 1672.8411 1672.8411
## 43 44 45 46 47 48 49
## 1734.8005 1734.8005 1734.8005 1814.4626 1814.4626 1814.4626 1425.0035
## 50 51 52 53 54 55 56
## 1425.0035 1425.0035 973.5851 1566.6250 1566.6250 1566.6250 1584.3277
## 57 58 59 60 61 62 63
## 1584.3277 1584.3277 1699.3951 1699.3951 1345.3414 1239.1253 1239.1253
## 64 65 66 67 68 69 70
## 1239.1253 1239.1253 1540.0710 1540.0710 1540.0710 1540.0710 1655.1384
## 71 72 73 82 83 84 85
## 1655.1384 1655.1384 1796.7599 938.1797 938.1797 938.1797 938.1797
## 86 87 88 89 90 91 92
## 1531.2196 1531.2196 1531.2196 1531.2196 1460.4089 1460.4089 1460.4089
## 93 94 95 96 97 99 100
## 1460.4089 1593.1790 1593.1790 1593.1790 1655.1384 1451.5575 1451.5575
## 101 102 103 104 105 106 107
## 1451.5575 1451.5575 1079.8012 1079.8012 1079.8012 1079.8012 1469.2602
## 108 109 110 111 112 113 114
## 1469.2602 1469.2602 761.1529 761.1529 1566.6250 1832.1653 1796.7599
## 115 116 117 118 119 120 121
## 1796.7599 1849.8679 1787.9085 1787.9085 1814.4626 1805.6112 1841.0166
## 122 123 124 125 126 127 128
## 1841.0166 1787.9085 1761.3545 1734.8005 1743.6518 1752.5032 1823.3139
## 129 130 131 132 133 134 135
## 1734.8005 1725.9491 1761.3545 1770.2059 1743.6518 1725.9491 1725.9491
## 136 137 138 139 140 141 142
## 1663.9898 1708.2465 1655.1384 1717.0978 1681.6924 1681.6924 1619.7330
## 143 144 145 146 147 148 149
## 1619.7330 1619.7330 1663.9898 1628.5844 1628.5844 1628.5844 1531.2196
## 150 151 156 157 158 159 160
## 1610.8817 1593.1790 274.3290 274.3290 274.3290 274.3290 353.9911
## 161 162 163 164 165 166 167
## 353.9911 353.9911 663.7881 1026.6931 1026.6931 1026.6931 1026.6931
## 168 169 170 171 172 173 174
## 1079.8012 1079.8012 1079.8012 1079.8012 1141.7606 1389.5982 1433.8549
## 175 176 177 178 179 180 181
## 1433.8549 1433.8549 1433.8549 1442.7062 1451.5575 1531.2196 1531.2196
## 182 183 184 185 186 187 188
## 1531.2196 1531.2196 1655.1384 1478.1116 1478.1116 1548.9223 1548.9223
## 189 190 191 192 193 194 195
## 1548.9223 1619.7330 929.3284 929.3284 929.3284 929.3284 973.5851
## 196 197 198 199 200 201 202
## 973.5851 973.5851 973.5851 1141.7606 1141.7606 1141.7606 1451.5575
## 203 204 205 206 207 208 209
## 1451.5575 1522.3683 1522.3683 1522.3683 1522.3683 1655.1384 1796.7599
## 210 211 212 213 214 215 216
## 1796.7599 1796.7599 504.4639 849.6663 1327.6388 1814.4626 1814.4626
## 217 218 219 220 221 222 223
## 1814.4626 1814.4626 1814.4626 1814.4626 1814.4626 1814.4626 1814.4626
## 224 225 226 227 228 229 230
## 1814.4626 1814.4626 1823.3139 1823.3139 1823.3139 1823.3139 1823.3139
## 231 232 233 234 235 236 237
## 1849.8679 1858.7193 1858.7193 1858.7193 1858.7193 1858.7193 1858.7193
## 238 239 240 241 242 243 244
## 1858.7193 1858.7193 885.0717 885.0717 1655.1384 1486.9629 1486.9629
## 245 246 247 248 249 250 251
## 1486.9629 1566.6250 1566.6250 1566.6250 1566.6250 1017.8418 1017.8418
## 252 253 254 255 256 257 258
## 1017.8418 1593.1790 1593.1790 1593.1790 1593.1790 1566.6250 1566.6250
## 259 260 261 262 263 264 265
## 1566.6250 885.0717 1513.5169 1513.5169 1513.5169 1531.2196 1531.2196
## 266 267 268 269 270 271 272
## 1531.2196 1177.1659 1823.3139 1823.3139 1823.3139 902.7743 902.7743
## 273 274 275 276 277 278 279
## 1079.8012 1708.2465 1177.1659 1734.8005 1734.8005 1734.8005 1699.3951
## 280 308 309 310 311 312 313
## 1380.7468 557.5720 1035.5445 1159.4633 1513.5169 1513.5169 1531.2196
## 314 315 316 317 318 319 320
## 1548.9223 902.7743 1150.6119 1150.6119 1203.7200 1354.1928 1354.1928
## 321 322 323 324 325 326 327
## 1354.1928 1398.4495 1460.4089 1460.4089 1770.2059 1770.2059 1770.2059
## 328 329 330 331 332 333 334
## 1770.2059 1770.2059 1770.2059 1796.7599 1796.7599 1796.7599 1796.7599
## 335 336 337 338 339 340 341
## 1805.6112 1805.6112 1805.6112 1805.6112 1566.6250 1566.6250 1566.6250
## 342 343 344 345 346 347 348
## 1814.4626 1823.3139 1823.3139 1823.3139 1823.3139 1849.8679 1849.8679
## 349 350 351 352 353 354 355
## 1849.8679 1858.7193 1858.7193 1858.7193 1858.7193 1858.7193 1858.7193
## 356 357 358 359 360 361 362
## 1858.7193 1858.7193 1858.7193 1858.7193 1858.7193 1858.7193 1858.7193
## 363 364 365 366 367 368 369
## 1858.7193 1858.7193 1858.7193 1858.7193 654.9368 654.9368 654.9368
## 370 371 372 373 374 375 376
## 1761.3545 1761.3545 1761.3545 1203.7200 1460.4089 1460.4089 1849.8679
## 377 378 379 380 381 382 383
## 1425.0035 1557.7736 212.3697 212.3697 212.3697 230.0723 407.0992
## 384 385 386 387 388 389 390
## 433.6532 531.0180 743.4502 770.0042 770.0042 770.0042 902.7743
## 391 392 393 394 395 396 397
## 902.7743 902.7743 920.4770 947.0310 964.7337 964.7337 1079.8012
## 398 399 400 401 402 403 404
## 1168.3146 1186.0173 1230.2740 1230.2740 1230.2740 1230.2740 1442.7062
## 405 406 407 408 409 410 411
## 1734.8005 433.6532 433.6532 610.6800 1389.5982 1008.9904 1008.9904
## 412 413 414 415 416 417 418
## 1008.9904 1008.9904 1008.9904 1008.9904 1008.9904 1008.9904 1345.3414
## 419 420 421 422 423 424 425
## 1345.3414 1345.3414 1345.3414 1345.3414 1345.3414 1345.3414 1345.3414
## 426 427 428 429 430 431 432
## 858.5176 858.5176 1814.4626 1814.4626 1823.3139 1823.3139 1823.3139
## 433 434 435 436 437 438 439
## 1849.8679 1849.8679 1849.8679 1849.8679 1858.7193 1858.7193 1867.5706
## 440 441 442 443 444 445 446
## 371.6938 398.2478 398.2478 734.5988 734.5988 734.5988 734.5988
## 447 448 449 450 451 452 453
## 805.4096 938.1797 1203.7200 1203.7200 1203.7200 1309.9361 1354.1928
## 454 455 456 457 458
## 1371.8955 1486.9629 1486.9629 1548.9223 1779.0572cor(INTL$PriceEconomy,INTL$PriceRelative)## [1] -0.4073316fit<-lm(PriceEconomy~PercentPremiumSeats,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1364.60 -850.95 -4.74 584.24 2251.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1219.364 149.643 8.148 4.36e-15 ***
## PercentPremiumSeats 13.687 9.669 1.416 0.158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 983.6 on 416 degrees of freedom
## Multiple R-squared: 0.004794, Adjusted R-squared: 0.002401
## F-statistic: 2.004 on 1 and 416 DF, p-value: 0.1577INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302
## 9 10 11 12 13 14 15 16
## 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302
## 17 18 19 20 21 22 23 24
## 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302
## 25 26 27 28 29 30 31 32
## 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302
## 33 34 35 36 37 38 39 40
## 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302
## 41 42 43 44 45 46 47 48
## 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302 1557.302
## 49 50 51 52 53 54 55 56
## 1557.302 1557.302 1557.302 1540.878 1540.878 1540.878 1540.878 1540.878
## 57 58 59 60 61 62 63 64
## 1540.878 1540.878 1540.878 1540.878 1540.878 1501.321 1501.321 1501.321
## 65 66 67 68 69 70 71 72
## 1501.321 1501.321 1501.321 1501.321 1501.321 1501.321 1501.321 1501.321
## 73 82 83 84 85 86 87 88
## 1501.321 1475.726 1475.726 1475.726 1475.726 1475.726 1475.726 1475.726
## 89 90 91 92 93 94 95 96
## 1475.726 1450.268 1450.268 1450.268 1450.268 1450.268 1450.268 1450.268
## 97 99 100 101 102 103 104 105
## 1450.268 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600
## 106 107 108 109 110 111 112 113
## 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600
## 114 115 116 117 118 119 120 121
## 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600
## 122 123 124 125 126 127 128 129
## 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600
## 130 131 132 133 134 135 136 137
## 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600
## 138 139 140 141 142 143 144 145
## 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600 1429.600
## 146 147 148 149 150 151 156 157
## 1429.600 1429.600 1429.600 1429.600 1424.536 1429.600 1424.947 1424.947
## 158 159 160 161 162 163 164 165
## 1424.947 1424.947 1424.947 1424.947 1424.947 1424.947 1424.262 1424.262
## 166 167 168 169 170 171 172 173
## 1424.262 1424.262 1424.947 1424.947 1424.947 1424.947 1424.947 1424.947
## 174 175 176 177 178 179 180 181
## 1424.262 1424.262 1424.262 1424.262 1424.947 1424.947 1424.947 1424.947
## 182 183 184 185 186 187 188 189
## 1424.947 1424.947 1424.947 1411.259 1411.259 1411.259 1411.259 1411.259
## 190 191 192 193 194 195 196 197
## 1411.259 1411.259 1411.259 1411.259 1411.259 1411.259 1411.259 1411.259
## 198 199 200 201 202 203 204 205
## 1411.259 1411.259 1411.259 1411.259 1411.259 1411.259 1411.259 1411.259
## 206 207 208 209 210 211 212 213
## 1411.259 1411.259 1411.259 1411.259 1411.259 1411.259 1390.455 1390.455
## 214 215 216 217 218 219 220 221
## 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455
## 222 223 224 225 226 227 228 229
## 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455
## 230 231 232 233 234 235 236 237
## 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455 1390.455
## 238 239 240 241 242 243 244 245
## 1390.455 1390.455 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930
## 246 247 248 249 250 251 252 253
## 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930
## 254 255 256 257 258 259 260 261
## 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930
## 262 263 264 265 266 267 268 269
## 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930
## 270 271 272 273 274 275 276 277
## 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930 1395.930
## 278 279 280 308 309 310 311 312
## 1395.930 1395.930 1395.930 1390.455 1390.455 1390.455 1390.455 1390.455
## 313 314 315 316 317 318 319 320
## 1390.455 1390.455 1400.173 1400.173 1400.173 1400.173 1400.173 1400.173
## 321 322 323 324 325 326 327 328
## 1400.173 1400.173 1400.173 1400.173 1400.173 1400.173 1400.173 1400.173
## 329 330 331 332 333 334 335 336
## 1400.173 1400.173 1400.173 1400.173 1400.173 1400.173 1400.173 1400.173
## 337 338 339 340 341 342 343 344
## 1400.173 1400.173 1387.444 1387.444 1387.444 1387.444 1387.444 1387.444
## 345 346 347 348 349 350 351 352
## 1387.444 1387.444 1387.444 1387.444 1387.444 1387.444 1387.444 1387.444
## 353 354 355 356 357 358 359 360
## 1387.444 1385.254 1385.254 1385.254 1385.254 1387.444 1387.444 1387.444
## 361 362 363 364 365 366 367 368
## 1385.254 1385.254 1387.444 1387.444 1387.444 1387.444 1364.038 1364.038
## 369 370 371 372 373 374 375 376
## 1364.038 1364.038 1364.038 1364.038 1364.038 1364.038 1364.038 1364.038
## 377 378 379 380 381 382 383 384
## 1364.038 1364.038 1375.809 1375.809 1375.809 1375.809 1375.809 1375.809
## 385 386 387 388 389 390 391 392
## 1375.809 1375.809 1375.809 1375.809 1375.809 1375.809 1375.809 1375.809
## 393 394 395 396 397 398 399 400
## 1375.809 1375.809 1375.809 1375.809 1375.809 1375.809 1375.809 1375.809
## 401 402 403 404 405 406 407 408
## 1375.809 1375.809 1375.809 1375.809 1375.809 1356.237 1356.237 1356.237
## 409 410 411 412 413 414 415 416
## 1356.237 1352.952 1352.952 1352.952 1352.952 1352.952 1352.952 1352.952
## 417 418 419 420 421 422 423 424
## 1352.952 1352.952 1352.952 1352.952 1352.952 1352.952 1352.952 1352.952
## 425 426 427 428 429 430 431 432
## 1352.952 1341.181 1341.181 1341.181 1341.181 1341.181 1341.181 1341.181
## 433 434 435 436 437 438 439 440
## 1341.181 1341.181 1341.181 1341.181 1341.181 1341.181 1341.181 1283.831
## 441 442 443 444 445 446 447 448
## 1283.831 1283.831 1283.831 1283.831 1283.831 1283.831 1283.831 1283.831
## 449 450 451 452 453 454 455 456
## 1283.831 1283.831 1283.831 1283.831 1283.831 1283.831 1283.831 1283.831
## 457 458
## 1283.831 1283.831cor(INTL$PriceEconomy,INTL$PercentPremiumSeats)## [1] 0.06923571fit<-lm(PricePremium~FlightDuration,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2263.5 -661.9 78.5 798.5 4139.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 221.22 126.06 1.755 0.08 .
## FlightDuration 220.80 14.51 15.215 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1013 on 416 degrees of freedom
## Multiple R-squared: 0.3575, Adjusted R-squared: 0.356
## F-statistic: 231.5 on 1 and 416 DF, p-value: < 2.2e-16INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 2926.0652 2926.0652 2926.0652 2926.0652 2022.9792 2022.9792 2022.9792
## 8 9 10 11 12 13 14
## 1656.4455 1656.4455 2760.4626 2760.4626 2760.4626 2760.4626 2778.1269
## 15 16 17 18 19 20 21
## 2778.1269 2778.1269 2243.7826 2243.7826 2243.7826 1711.6464 1711.6464
## 22 23 24 25 26 27 28
## 1711.6464 1691.7741 1691.7741 1691.7741 2153.2532 2153.2532 2153.2532
## 29 30 31 32 33 34 35
## 1305.3681 1305.3681 1305.3681 1066.9004 1066.9004 1066.9004 1066.9004
## 36 37 38 39 40 41 42
## 3202.0695 3202.0695 3202.0695 1066.9004 1066.9004 1066.9004 1066.9004
## 43 44 45 46 47 48 49
## 1415.7698 1415.7698 1415.7698 2042.8515 2042.8515 2042.8515 3036.4669
## 50 51 52 53 54 55 56
## 3036.4669 3036.4669 1656.4455 2667.7252 2667.7252 2667.7252 1563.7081
## 57 58 59 60 61 62 63
## 1563.7081 1563.7081 2981.2661 2881.9045 2981.2661 1987.6506 1987.6506
## 64 65 66 67 68 69 70
## 1987.6506 1987.6506 2170.9175 2170.9175 2170.9175 2170.9175 1784.5115
## 71 72 73 82 83 84 85
## 1784.5115 1784.5115 1932.4498 1729.3106 1729.3106 1729.3106 1729.3106
## 86 87 88 89 90 91 92
## 1894.9132 1894.9132 1894.9132 1894.9132 901.2978 901.2978 901.2978
## 93 94 95 96 97 99 100
## 901.2978 901.2978 901.2978 901.2978 901.2978 2685.3895 2685.3895
## 101 102 103 104 105 106 107
## 2685.3895 2685.3895 2539.6592 2539.6592 2539.6592 2539.6592 3109.3321
## 108 109 110 111 112 113 114
## 3109.3321 3109.3321 2685.3895 2685.3895 2685.3895 1122.1012 1011.6995
## 115 116 117 118 119 120 121
## 1011.6995 753.3595 938.8344 938.8344 1011.6995 808.5603 753.3595
## 122 123 124 125 126 127 128
## 753.3595 753.3595 938.8344 625.2935 1011.6995 1214.8386 753.3595
## 129 130 131 132 133 134 135
## 1214.8386 625.2935 753.3595 1011.6995 1122.1012 846.0969 1214.8386
## 136 137 138 139 140 141 142
## 497.2275 1122.1012 497.2275 753.3595 625.2935 625.2935 497.2275
## 143 144 145 146 147 148 149
## 497.2275 497.2275 1011.6995 846.0969 514.8918 514.8918 497.2275
## 150 151 156 157 158 159 160
## 753.3595 514.8918 2705.2618 2705.2618 2705.2618 2705.2618 2888.5286
## 161 162 163 164 165 166 167
## 2888.5286 2888.5286 2888.5286 2409.3852 2409.3852 2409.3852 2409.3852
## 168 169 170 171 172 173 174
## 2612.5243 2612.5243 2612.5243 2612.5243 2519.7869 2998.9303 2594.8601
## 175 176 177 178 179 180 181
## 2594.8601 2594.8601 2594.8601 2998.9303 2722.9261 1912.5775 1912.5775
## 182 183 184 185 186 187 188
## 1912.5775 1912.5775 2998.9303 2060.5158 2060.5158 2318.8558 2318.8558
## 189 190 191 192 193 194 195
## 2318.8558 2060.5158 1746.9749 1746.9749 1746.9749 1746.9749 1674.1098
## 196 197 198 199 200 201 202
## 1674.1098 1674.1098 1674.1098 2519.7869 2519.7869 2519.7869 2722.9261
## 203 204 205 206 207 208 209
## 2722.9261 1857.3766 1857.3766 1857.3766 1857.3766 1784.5115 1932.4498
## 210 211 212 213 214 215 216
## 1932.4498 1932.4498 2318.8558 2318.8558 2318.8558 2060.5158 2060.5158
## 217 218 219 220 221 222 223
## 2060.5158 2060.5158 2060.5158 2060.5158 2060.5158 2060.5158 1839.7123
## 224 225 226 227 228 229 230
## 1839.7123 1839.7123 1729.3106 1729.3106 2188.5818 2188.5818 2188.5818
## 231 232 233 234 235 236 237
## 2005.3149 2248.1987 2248.1987 2263.6549 2263.6549 2243.7826 2243.7826
## 238 239 240 241 242 243 244
## 2263.6549 2263.6549 2519.7869 2519.7869 2519.7869 2650.0609 2650.0609
## 245 246 247 248 249 250 251
## 2650.0609 2409.3852 2409.3852 2409.3852 2409.3852 2115.7166 2115.7166
## 252 253 254 255 256 257 258
## 2115.7166 2740.5903 2740.5903 2740.5903 2740.5903 2281.3192 2281.3192
## 259 260 261 262 263 264 265
## 2281.3192 2188.5818 2133.3809 2133.3809 2133.3809 1822.0481 1822.0481
## 266 267 268 269 270 271 272
## 1822.0481 2188.5818 1784.5115 1784.5115 1784.5115 2740.5903 2740.5903
## 273 274 275 276 277 278 279
## 2740.5903 2740.5903 2188.5818 2667.7252 2667.7252 2667.7252 1784.5115
## 280 308 309 310 311 312 313
## 2667.7252 2318.8558 2318.8558 2318.8558 2188.5818 2188.5818 2318.8558
## 314 315 316 317 318 319 320
## 2188.5818 3292.5989 3292.5989 3292.5989 3292.5989 2961.3938 2961.3938
## 321 322 323 324 325 326 327
## 2961.3938 2612.5243 2612.5243 2612.5243 3458.2015 3458.2015 3458.2015
## 328 329 330 331 332 333 334
## 2354.1843 2354.1843 2354.1843 1066.9004 1066.9004 1066.9004 1066.9004
## 335 336 337 338 339 340 341
## 3036.4669 3036.4669 3036.4669 3036.4669 2060.5158 2060.5158 2060.5158
## 342 343 344 345 346 347 348
## 1877.2489 1729.3106 2153.2532 2153.2532 2153.2532 1912.5775 1729.3106
## 349 350 351 352 353 354 355
## 1729.3106 2318.8558 2318.8558 2318.8558 2318.8558 1932.4498 1932.4498
## 356 357 358 359 360 361 362
## 1932.4498 1950.1141 2298.9835 2298.9835 2298.9835 2815.6635 2815.6635
## 363 364 365 366 367 368 369
## 2850.9920 2850.9920 2850.9920 2850.9920 3274.9346 3274.9346 3274.9346
## 370 371 372 373 374 375 376
## 3164.5329 3164.5329 3164.5329 2188.5818 2188.5818 2188.5818 2336.5201
## 377 378 379 380 381 382 383
## 2336.5201 2336.5201 938.8344 938.8344 938.8344 938.8344 938.8344
## 384 385 386 387 388 389 390
## 1139.7655 1139.7655 1122.1012 773.2318 773.2318 773.2318 808.5603
## 391 392 393 394 395 396 397
## 808.5603 808.5603 1139.7655 938.8344 1122.1012 1139.7655 1139.7655
## 398 399 400 401 402 403 404
## 773.2318 808.5603 1177.3021 1177.3021 1177.3021 1177.3021 938.8344
## 405 406 407 408 409 410 411
## 938.8344 1746.9749 1746.9749 1746.9749 1746.9749 3164.5329 3164.5329
## 412 413 414 415 416 417 418
## 3164.5329 3164.5329 1581.3723 1581.3723 1581.3723 1581.3723 3016.5946
## 419 420 421 422 423 424 425
## 3016.5946 3016.5946 3016.5946 1656.4455 1656.4455 1656.4455 1656.4455
## 426 427 428 429 430 431 432
## 3091.6678 3091.6678 1877.2489 1877.2489 2574.9878 2574.9878 2574.9878
## 433 434 435 436 437 438 439
## 2078.1801 2098.0524 2098.0524 2098.0524 2760.4626 2760.4626 3091.6678
## 440 441 442 443 444 445 446
## 1470.9706 918.9621 918.9621 1470.9706 1470.9706 1470.9706 1470.9706
## 447 448 449 450 451 452 453
## 1470.9706 918.9621 790.8961 790.8961 790.8961 918.9621 790.8961
## 454 455 456 457 458
## 790.8961 790.8961 790.8961 938.8344 790.8961cor(INTL$PricePremium,INTL$FlightDuration)## [1] 0.5979467fit<-lm(PricePremium~SeatsEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2009.9 -1144.9 203.9 1047.0 5437.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1734.5948 182.8845 9.485 <2e-16 ***
## SeatsEconomy 1.1924 0.8202 1.454 0.147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1260 on 416 degrees of freedom
## Multiple R-squared: 0.005055, Adjusted R-squared: 0.002663
## F-statistic: 2.114 on 1 and 416 DF, p-value: 0.1468INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073
## 9 10 11 12 13 14 15 16
## 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073
## 17 18 19 20 21 22 23 24
## 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073
## 25 26 27 28 29 30 31 32
## 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073
## 33 34 35 36 37 38 39 40
## 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073
## 41 42 43 44 45 46 47 48
## 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073 1880.073
## 49 50 51 52 53 54 55 56
## 1880.073 1880.073 1880.073 1886.036 1886.036 1886.036 1886.036 1886.036
## 57 58 59 60 61 62 63 64
## 1886.036 1886.036 1886.036 1886.036 1886.036 1955.198 1955.198 1955.198
## 65 66 67 68 69 70 71 72
## 1955.198 1955.198 1955.198 1955.198 1955.198 1955.198 1955.198 1955.198
## 73 82 83 84 85 86 87 88
## 1955.198 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360
## 89 90 91 92 93 94 95 96
## 2024.360 1899.153 1899.153 1899.153 1899.153 1899.153 1899.153 1899.153
## 97 99 100 101 102 103 104 105
## 1899.153 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907
## 106 107 108 109 110 111 112 113
## 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907
## 114 115 116 117 118 119 120 121
## 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907
## 122 123 124 125 126 127 128 129
## 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907
## 130 131 132 133 134 135 136 137
## 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907
## 138 139 140 141 142 143 144 145
## 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907 2095.907
## 146 147 148 149 150 151 156 157
## 2095.907 2095.907 2095.907 2095.907 2106.639 2095.907 1970.700 1970.700
## 158 159 160 161 162 163 164 165
## 1970.700 1970.700 1970.700 1970.700 1970.700 1970.700 2181.763 2181.763
## 166 167 168 169 170 171 172 173
## 2181.763 2181.763 1970.700 1970.700 1970.700 1970.700 1970.700 1970.700
## 174 175 176 177 178 179 180 181
## 2181.763 2181.763 2181.763 2181.763 1970.700 1970.700 1970.700 1970.700
## 182 183 184 185 186 187 188 189
## 1970.700 1970.700 1970.700 2012.435 2012.435 2012.435 2012.435 2012.435
## 190 191 192 193 194 195 196 197
## 2012.435 2012.435 2012.435 2012.435 2012.435 2012.435 2012.435 2012.435
## 198 199 200 201 202 203 204 205
## 2012.435 2012.435 2012.435 2012.435 2012.435 2012.435 2012.435 2012.435
## 206 207 208 209 210 211 212 213
## 2012.435 2012.435 2012.435 2012.435 2012.435 2012.435 1909.885 1909.885
## 214 215 216 217 218 219 220 221
## 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885
## 222 223 224 225 226 227 228 229
## 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885
## 230 231 232 233 234 235 236 237
## 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885 1909.885
## 238 239 240 241 242 243 244 245
## 1909.885 1909.885 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360
## 246 247 248 249 250 251 252 253
## 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360
## 254 255 256 257 258 259 260 261
## 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360
## 262 263 264 265 266 267 268 269
## 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360
## 270 271 272 273 274 275 276 277
## 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360 2024.360
## 278 279 280 308 309 310 311 312
## 2024.360 2024.360 2024.360 1909.885 1909.885 1909.885 1909.885 1909.885
## 313 314 315 316 317 318 319 320
## 1909.885 1909.885 1954.005 1954.005 1954.005 1954.005 1954.005 1954.005
## 321 322 323 324 325 326 327 328
## 1954.005 1954.005 1954.005 1954.005 1954.005 1954.005 1954.005 1954.005
## 329 330 331 332 333 334 335 336
## 1954.005 1954.005 1954.005 1954.005 1954.005 1954.005 1954.005 1954.005
## 337 338 339 340 341 342 343 344
## 1954.005 1954.005 1973.084 1973.084 1973.084 1973.084 1973.084 1973.084
## 345 346 347 348 349 350 351 352
## 1973.084 1973.084 1973.084 1973.084 1973.084 1973.084 1973.084 1973.084
## 353 354 355 356 357 358 359 360
## 1973.084 1942.081 1942.081 1942.081 1942.081 1973.084 1973.084 1973.084
## 361 362 363 364 365 366 367 368
## 1942.081 1942.081 1973.084 1973.084 1973.084 1973.084 1976.662 1976.662
## 369 370 371 372 373 374 375 376
## 1976.662 1976.662 1976.662 1976.662 1976.662 1976.662 1976.662 1976.662
## 377 378 379 380 381 382 383 384
## 1976.662 1976.662 1882.458 1882.458 1882.458 1882.458 1882.458 1882.458
## 385 386 387 388 389 390 391 392
## 1882.458 1882.458 1882.458 1882.458 1882.458 1882.458 1882.458 1882.458
## 393 394 395 396 397 398 399 400
## 1882.458 1882.458 1882.458 1882.458 1882.458 1882.458 1882.458 1882.458
## 401 402 403 404 405 406 407 408
## 1882.458 1882.458 1882.458 1882.458 1882.458 1992.164 1992.164 1992.164
## 409 410 411 412 413 414 415 416
## 1992.164 2131.680 2131.680 2131.680 2131.680 2131.680 2131.680 2131.680
## 417 418 419 420 421 422 423 424
## 2131.680 2131.680 2131.680 2131.680 2131.680 2131.680 2131.680 2131.680
## 425 426 427 428 429 430 431 432
## 2131.680 2198.457 2198.457 2198.457 2198.457 2198.457 2198.457 2198.457
## 433 434 435 436 437 438 439 440
## 2198.457 2198.457 2198.457 2198.457 2198.457 2198.457 2198.457 1927.771
## 441 442 443 444 445 446 447 448
## 1927.771 1927.771 1927.771 1927.771 1927.771 1927.771 1927.771 1927.771
## 449 450 451 452 453 454 455 456
## 1927.771 1927.771 1927.771 1927.771 1927.771 1927.771 1927.771 1927.771
## 457 458
## 1927.771 1927.771cor(INTL$PricePremium,INTL$SeatsEconomy)## [1] 0.07109765fit<-lm(PricePremium~SeatsPremium,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsPremium, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2131.6 -1081.3 47.1 1031.9 5555.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1579.838 174.366 9.060 <2e-16 ***
## SeatsPremium 11.596 4.673 2.482 0.0135 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1254 on 416 degrees of freedom
## Multiple R-squared: 0.01459, Adjusted R-squared: 0.01222
## F-statistic: 6.159 on 1 and 416 DF, p-value: 0.01347INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696
## 9 10 11 12 13 14 15 16
## 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696
## 17 18 19 20 21 22 23 24
## 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696
## 25 26 27 28 29 30 31 32
## 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696
## 33 34 35 36 37 38 39 40
## 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696
## 41 42 43 44 45 46 47 48
## 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696 2043.696
## 49 50 51 52 53 54 55 56
## 2043.696 2043.696 2043.696 2032.099 2032.099 2032.099 2032.099 2032.099
## 57 58 59 60 61 62 63 64
## 2032.099 2032.099 2032.099 2032.099 2032.099 2136.467 2136.467 2136.467
## 65 66 67 68 69 70 71 72
## 2136.467 2136.467 2136.467 2136.467 2136.467 2136.467 2136.467 2136.467
## 73 82 83 84 85 86 87 88
## 2136.467 2229.239 2229.239 2229.239 2229.239 2229.239 2229.239 2229.239
## 89 90 91 92 93 94 95 96
## 2229.239 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539
## 97 99 100 101 102 103 104 105
## 1904.539 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642
## 106 107 108 109 110 111 112 113
## 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642
## 114 115 116 117 118 119 120 121
## 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642
## 122 123 124 125 126 127 128 129
## 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642
## 130 131 132 133 134 135 136 137
## 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642
## 138 139 140 141 142 143 144 145
## 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642
## 146 147 148 149 150 151 156 157
## 2217.642 2217.642 2217.642 2217.642 2217.642 2217.642 1985.714 1985.714
## 158 159 160 161 162 163 164 165
## 1985.714 1985.714 1985.714 1985.714 1985.714 1985.714 2345.203 2345.203
## 166 167 168 169 170 171 172 173
## 2345.203 2345.203 1985.714 1985.714 1985.714 1985.714 1985.714 1985.714
## 174 175 176 177 178 179 180 181
## 2345.203 2345.203 2345.203 2345.203 1985.714 1985.714 1985.714 1985.714
## 182 183 184 185 186 187 188 189
## 1985.714 1985.714 1985.714 2020.503 2020.503 2020.503 2020.503 2020.503
## 190 191 192 193 194 195 196 197
## 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503
## 198 199 200 201 202 203 204 205
## 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503
## 206 207 208 209 210 211 212 213
## 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 1823.364 1823.364
## 214 215 216 217 218 219 220 221
## 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364
## 222 223 224 225 226 227 228 229
## 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364
## 230 231 232 233 234 235 236 237
## 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364 1823.364
## 238 239 240 241 242 243 244 245
## 1823.364 1823.364 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310
## 246 247 248 249 250 251 252 253
## 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310
## 254 255 256 257 258 259 260 261
## 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310
## 262 263 264 265 266 267 268 269
## 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310
## 270 271 272 273 274 275 276 277
## 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310
## 278 279 280 308 309 310 311 312
## 1997.310 1997.310 1997.310 1823.364 1823.364 1823.364 1823.364 1823.364
## 313 314 315 316 317 318 319 320
## 1823.364 1823.364 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539
## 321 322 323 324 325 326 327 328
## 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539
## 329 330 331 332 333 334 335 336
## 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539
## 337 338 339 340 341 342 343 344
## 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539
## 345 346 347 348 349 350 351 352
## 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539 1904.539
## 353 354 355 356 357 358 359 360
## 1904.539 1858.153 1858.153 1858.153 1858.153 1904.539 1904.539 1904.539
## 361 362 363 364 365 366 367 368
## 1858.153 1858.153 1904.539 1904.539 1904.539 1904.539 1858.153 1858.153
## 369 370 371 372 373 374 375 376
## 1858.153 1858.153 1858.153 1858.153 1858.153 1858.153 1858.153 1858.153
## 377 378 379 380 381 382 383 384
## 1858.153 1858.153 1765.381 1765.381 1765.381 1765.381 1765.381 1765.381
## 385 386 387 388 389 390 391 392
## 1765.381 1765.381 1765.381 1765.381 1765.381 1765.381 1765.381 1765.381
## 393 394 395 396 397 398 399 400
## 1765.381 1765.381 1765.381 1765.381 1765.381 1765.381 1765.381 1765.381
## 401 402 403 404 405 406 407 408
## 1765.381 1765.381 1765.381 1765.381 1765.381 1858.153 1858.153 1858.153
## 409 410 411 412 413 414 415 416
## 1858.153 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310
## 417 418 419 420 421 422 423 424
## 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310 1997.310
## 425 426 427 428 429 430 431 432
## 1997.310 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503
## 433 434 435 436 437 438 439 440
## 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 2020.503 1672.610
## 441 442 443 444 445 446 447 448
## 1672.610 1672.610 1672.610 1672.610 1672.610 1672.610 1672.610 1672.610
## 449 450 451 452 453 454 455 456
## 1672.610 1672.610 1672.610 1672.610 1672.610 1672.610 1672.610 1672.610
## 457 458
## 1672.610 1672.610cor(INTL$PricePremium,INTL$SeatsPremium)## [1] 0.1207902fit<-lm(PricePremium~PriceRelative,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PriceRelative, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1938.4 -1213.1 91.5 1108.4 5603.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2090.94 94.47 22.132 <2e-16 ***
## PriceRelative -201.69 136.18 -1.481 0.139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1260 on 416 degrees of freedom
## Multiple R-squared: 0.005245, Adjusted R-squared: 0.002854
## F-statistic: 2.194 on 1 and 416 DF, p-value: 0.1393INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 2014.299 2014.299 2014.299 2014.299 1955.809 1955.809 1955.809 1883.200
## 9 10 11 12 13 14 15 16
## 1883.200 1939.673 1939.673 1977.995 2038.502 1986.062 1986.062 1986.062
## 17 18 19 20 21 22 23 24
## 2014.299 2014.299 2014.299 2022.367 2022.367 2022.367 2024.384 2024.384
## 25 26 27 28 29 30 31 32
## 2024.384 2020.350 2024.384 2024.384 2022.367 2022.367 2022.367 2006.231
## 33 34 35 36 37 38 39 40
## 2006.231 2006.231 2006.231 1959.842 1959.842 1959.842 2042.536 2042.536
## 41 42 43 44 45 46 47 48
## 2042.536 2042.536 2056.654 2056.654 2056.654 2074.806 2074.806 2074.806
## 49 50 51 52 53 54 55 56
## 1986.062 1986.062 1986.062 1883.200 2018.333 2018.333 2018.333 2022.367
## 57 58 59 60 61 62 63 64
## 2022.367 2022.367 2048.587 2048.587 1967.910 1943.707 1943.707 1943.707
## 65 66 67 68 69 70 71 72
## 1943.707 2012.282 2012.282 2012.282 2012.282 2038.502 2038.502 2038.502
## 73 82 83 84 85 86 87 88
## 2070.773 1875.132 1875.132 1875.132 1875.132 2010.265 2010.265 2010.265
## 89 90 91 92 93 94 95 96
## 2010.265 1994.130 1994.130 1994.130 1994.130 2024.384 2024.384 2024.384
## 97 99 100 101 102 103 104 105
## 2038.502 1992.113 1992.113 1992.113 1992.113 1907.403 1907.403 1907.403
## 106 107 108 109 110 111 112 113
## 1907.403 1996.147 1996.147 1996.147 1834.794 1834.794 2018.333 2078.840
## 114 115 116 117 118 119 120 121
## 2070.773 2070.773 2082.874 2068.756 2068.756 2074.806 2072.789 2080.857
## 122 123 124 125 126 127 128 129
## 2080.857 2068.756 2062.705 2056.654 2058.671 2060.688 2076.823 2056.654
## 130 131 132 133 134 135 136 137
## 2054.637 2062.705 2064.722 2058.671 2054.637 2054.637 2040.519 2050.603
## 138 139 140 141 142 143 144 145
## 2038.502 2052.620 2044.553 2044.553 2030.434 2030.434 2030.434 2040.519
## 146 147 148 149 150 151 156 157
## 2032.451 2032.451 2032.451 2010.265 2028.417 2024.384 1723.864 1723.864
## 158 159 160 161 162 163 164 165
## 1723.864 1723.864 1742.016 1742.016 1742.016 1812.608 1895.301 1895.301
## 166 167 168 169 170 171 172 173
## 1895.301 1895.301 1907.403 1907.403 1907.403 1907.403 1921.521 1977.995
## 174 175 176 177 178 179 180 181
## 1988.079 1988.079 1988.079 1988.079 1990.096 1992.113 2010.265 2010.265
## 182 183 184 185 186 187 188 189
## 2010.265 2010.265 2038.502 1998.164 1998.164 2014.299 2014.299 2014.299
## 190 191 192 193 194 195 196 197
## 2030.434 1873.115 1873.115 1873.115 1873.115 1883.200 1883.200 1883.200
## 198 199 200 201 202 203 204 205
## 1883.200 1921.521 1921.521 1921.521 1992.113 1992.113 2008.248 2008.248
## 206 207 208 209 210 211 212 213
## 2008.248 2008.248 2038.502 2070.773 2070.773 2070.773 1776.304 1854.963
## 214 215 216 217 218 219 220 221
## 1963.876 2074.806 2074.806 2074.806 2074.806 2074.806 2074.806 2074.806
## 222 223 224 225 226 227 228 229
## 2074.806 2074.806 2074.806 2074.806 2076.823 2076.823 2076.823 2076.823
## 230 231 232 233 234 235 236 237
## 2076.823 2082.874 2084.891 2084.891 2084.891 2084.891 2084.891 2084.891
## 238 239 240 241 242 243 244 245
## 2084.891 2084.891 1863.031 1863.031 2038.502 2000.181 2000.181 2000.181
## 246 247 248 249 250 251 252 253
## 2018.333 2018.333 2018.333 2018.333 1893.284 1893.284 1893.284 2024.384
## 254 255 256 257 258 259 260 261
## 2024.384 2024.384 2024.384 2018.333 2018.333 2018.333 1863.031 2006.231
## 262 263 264 265 266 267 268 269
## 2006.231 2006.231 2010.265 2010.265 2010.265 1929.589 2076.823 2076.823
## 270 271 272 273 274 275 276 277
## 2076.823 1867.065 1867.065 1907.403 2050.603 1929.589 2056.654 2056.654
## 278 279 280 308 309 310 311 312
## 2056.654 2048.587 1975.978 1788.405 1897.318 1925.555 2006.231 2006.231
## 313 314 315 316 317 318 319 320
## 2010.265 2014.299 1867.065 1923.538 1923.538 1935.640 1969.927 1969.927
## 321 322 323 324 325 326 327 328
## 1969.927 1980.012 1994.130 1994.130 2064.722 2064.722 2064.722 2064.722
## 329 330 331 332 333 334 335 336
## 2064.722 2064.722 2070.773 2070.773 2070.773 2070.773 2072.789 2072.789
## 337 338 339 340 341 342 343 344
## 2072.789 2072.789 2018.333 2018.333 2018.333 2074.806 2076.823 2076.823
## 345 346 347 348 349 350 351 352
## 2076.823 2076.823 2082.874 2082.874 2082.874 2084.891 2084.891 2084.891
## 353 354 355 356 357 358 359 360
## 2084.891 2084.891 2084.891 2084.891 2084.891 2084.891 2084.891 2084.891
## 361 362 363 364 365 366 367 368
## 2084.891 2084.891 2084.891 2084.891 2084.891 2084.891 1810.591 1810.591
## 369 370 371 372 373 374 375 376
## 1810.591 2062.705 2062.705 2062.705 1935.640 1994.130 1994.130 2082.874
## 377 378 379 380 381 382 383 384
## 1986.062 2016.316 1709.745 1709.745 1709.745 1713.779 1754.118 1760.168
## 385 386 387 388 389 390 391 392
## 1782.354 1830.760 1836.811 1836.811 1836.811 1867.065 1867.065 1867.065
## 393 394 395 396 397 398 399 400
## 1871.098 1877.149 1881.183 1881.183 1907.403 1927.572 1931.606 1941.690
## 401 402 403 404 405 406 407 408
## 1941.690 1941.690 1941.690 1990.096 2056.654 1760.168 1760.168 1800.506
## 409 410 411 412 413 414 415 416
## 1977.995 1891.267 1891.267 1891.267 1891.267 1891.267 1891.267 1891.267
## 417 418 419 420 421 422 423 424
## 1891.267 1967.910 1967.910 1967.910 1967.910 1967.910 1967.910 1967.910
## 425 426 427 428 429 430 431 432
## 1967.910 1856.980 1856.980 2074.806 2074.806 2076.823 2076.823 2076.823
## 433 434 435 436 437 438 439 440
## 2082.874 2082.874 2082.874 2082.874 2084.891 2084.891 2086.908 1746.050
## 441 442 443 444 445 446 447 448
## 1752.101 1752.101 1828.743 1828.743 1828.743 1828.743 1844.879 1875.132
## 449 450 451 452 453 454 455 456
## 1935.640 1935.640 1935.640 1959.842 1969.927 1973.961 2000.181 2000.181
## 457 458
## 2014.299 2066.739cor(INTL$PricePremium,INTL$PriceRelative)## [1] -0.07242428fit<-lm(PricePremium~PercentPremiumSeats,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PercentPremiumSeats, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1921.4 -1113.7 -90.2 1017.3 5559.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1518.56 190.72 7.962 1.63e-14 ***
## PercentPremiumSeats 31.82 12.32 2.582 0.0102 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1254 on 416 degrees of freedom
## Multiple R-squared: 0.01578, Adjusted R-squared: 0.01341
## F-statistic: 6.669 on 1 and 416 DF, p-value: 0.01015INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275
## 9 10 11 12 13 14 15 16
## 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275
## 17 18 19 20 21 22 23 24
## 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275
## 25 26 27 28 29 30 31 32
## 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275
## 33 34 35 36 37 38 39 40
## 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275
## 41 42 43 44 45 46 47 48
## 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275 2304.275
## 49 50 51 52 53 54 55 56
## 2304.275 2304.275 2304.275 2266.087 2266.087 2266.087 2266.087 2266.087
## 57 58 59 60 61 62 63 64
## 2266.087 2266.087 2266.087 2266.087 2266.087 2174.118 2174.118 2174.118
## 65 66 67 68 69 70 71 72
## 2174.118 2174.118 2174.118 2174.118 2174.118 2174.118 2174.118 2174.118
## 73 82 83 84 85 86 87 88
## 2174.118 2114.608 2114.608 2114.608 2114.608 2114.608 2114.608 2114.608
## 89 90 91 92 93 94 95 96
## 2114.608 2055.417 2055.417 2055.417 2055.417 2055.417 2055.417 2055.417
## 97 99 100 101 102 103 104 105
## 2055.417 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364
## 106 107 108 109 110 111 112 113
## 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364
## 114 115 116 117 118 119 120 121
## 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364
## 122 123 124 125 126 127 128 129
## 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364
## 130 131 132 133 134 135 136 137
## 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364
## 138 139 140 141 142 143 144 145
## 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364 2007.364
## 146 147 148 149 150 151 156 157
## 2007.364 2007.364 2007.364 2007.364 1995.589 2007.364 1996.544 1996.544
## 158 159 160 161 162 163 164 165
## 1996.544 1996.544 1996.544 1996.544 1996.544 1996.544 1994.952 1994.952
## 166 167 168 169 170 171 172 173
## 1994.952 1994.952 1996.544 1996.544 1996.544 1996.544 1996.544 1996.544
## 174 175 176 177 178 179 180 181
## 1994.952 1994.952 1994.952 1994.952 1996.544 1996.544 1996.544 1996.544
## 182 183 184 185 186 187 188 189
## 1996.544 1996.544 1996.544 1964.720 1964.720 1964.720 1964.720 1964.720
## 190 191 192 193 194 195 196 197
## 1964.720 1964.720 1964.720 1964.720 1964.720 1964.720 1964.720 1964.720
## 198 199 200 201 202 203 204 205
## 1964.720 1964.720 1964.720 1964.720 1964.720 1964.720 1964.720 1964.720
## 206 207 208 209 210 211 212 213
## 1964.720 1964.720 1964.720 1964.720 1964.720 1964.720 1916.349 1916.349
## 214 215 216 217 218 219 220 221
## 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349
## 222 223 224 225 226 227 228 229
## 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349
## 230 231 232 233 234 235 236 237
## 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349 1916.349
## 238 239 240 241 242 243 244 245
## 1916.349 1916.349 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078
## 246 247 248 249 250 251 252 253
## 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078
## 254 255 256 257 258 259 260 261
## 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078
## 262 263 264 265 266 267 268 269
## 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078
## 270 271 272 273 274 275 276 277
## 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078 1929.078
## 278 279 280 308 309 310 311 312
## 1929.078 1929.078 1929.078 1916.349 1916.349 1916.349 1916.349 1916.349
## 313 314 315 316 317 318 319 320
## 1916.349 1916.349 1938.943 1938.943 1938.943 1938.943 1938.943 1938.943
## 321 322 323 324 325 326 327 328
## 1938.943 1938.943 1938.943 1938.943 1938.943 1938.943 1938.943 1938.943
## 329 330 331 332 333 334 335 336
## 1938.943 1938.943 1938.943 1938.943 1938.943 1938.943 1938.943 1938.943
## 337 338 339 340 341 342 343 344
## 1938.943 1938.943 1909.348 1909.348 1909.348 1909.348 1909.348 1909.348
## 345 346 347 348 349 350 351 352
## 1909.348 1909.348 1909.348 1909.348 1909.348 1909.348 1909.348 1909.348
## 353 354 355 356 357 358 359 360
## 1909.348 1904.256 1904.256 1904.256 1904.256 1909.348 1909.348 1909.348
## 361 362 363 364 365 366 367 368
## 1904.256 1904.256 1909.348 1909.348 1909.348 1909.348 1854.930 1854.930
## 369 370 371 372 373 374 375 376
## 1854.930 1854.930 1854.930 1854.930 1854.930 1854.930 1854.930 1854.930
## 377 378 379 380 381 382 383 384
## 1854.930 1854.930 1882.298 1882.298 1882.298 1882.298 1882.298 1882.298
## 385 386 387 388 389 390 391 392
## 1882.298 1882.298 1882.298 1882.298 1882.298 1882.298 1882.298 1882.298
## 393 394 395 396 397 398 399 400
## 1882.298 1882.298 1882.298 1882.298 1882.298 1882.298 1882.298 1882.298
## 401 402 403 404 405 406 407 408
## 1882.298 1882.298 1882.298 1882.298 1882.298 1836.791 1836.791 1836.791
## 409 410 411 412 413 414 415 416
## 1836.791 1829.153 1829.153 1829.153 1829.153 1829.153 1829.153 1829.153
## 417 418 419 420 421 422 423 424
## 1829.153 1829.153 1829.153 1829.153 1829.153 1829.153 1829.153 1829.153
## 425 426 427 428 429 430 431 432
## 1829.153 1801.785 1801.785 1801.785 1801.785 1801.785 1801.785 1801.785
## 433 434 435 436 437 438 439 440
## 1801.785 1801.785 1801.785 1801.785 1801.785 1801.785 1801.785 1668.445
## 441 442 443 444 445 446 447 448
## 1668.445 1668.445 1668.445 1668.445 1668.445 1668.445 1668.445 1668.445
## 449 450 451 452 453 454 455 456
## 1668.445 1668.445 1668.445 1668.445 1668.445 1668.445 1668.445 1668.445
## 457 458
## 1668.445 1668.445cor(INTL$PricePremium,INTL$PercentPremiumSeats)## [1] 0.1256081fit<-lm(PricePremium~PitchEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2009.4 -905.7 155.6 870.2 5551.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -21892.47 2841.02 -7.706 9.63e-14 ***
## PitchEconomy 766.28 91.16 8.406 6.81e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1168 on 416 degrees of freedom
## Multiple R-squared: 0.1452, Adjusted R-squared: 0.1431
## F-statistic: 70.66 on 1 and 416 DF, p-value: 6.806e-16INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 9 10 11 12 13 14 15 16
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 17 18 19 20 21 22 23 24
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 25 26 27 28 29 30 31 32
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 33 34 35 36 37 38 39 40
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 41 42 43 44 45 46 47 48
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 49 50 51 52 53 54 55 56
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 57 58 59 60 61 62 63 64
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 65 66 67 68 69 70 71 72
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 73 82 83 84 85 86 87 88
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 89 90 91 92 93 94 95 96
## 1862.085 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809
## 97 99 100 101 102 103 104 105
## 1095.809 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 106 107 108 109 110 111 112 113
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 114 115 116 117 118 119 120 121
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 122 123 124 125 126 127 128 129
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 130 131 132 133 134 135 136 137
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 138 139 140 141 142 143 144 145
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 146 147 148 149 150 151 156 157
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 158 159 160 161 162 163 164 165
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 166 167 168 169 170 171 172 173
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 174 175 176 177 178 179 180 181
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 182 183 184 185 186 187 188 189
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 190 191 192 193 194 195 196 197
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 198 199 200 201 202 203 204 205
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 206 207 208 209 210 211 212 213
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 2628.361 2628.361
## 214 215 216 217 218 219 220 221
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 222 223 224 225 226 227 228 229
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 230 231 232 233 234 235 236 237
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 238 239 240 241 242 243 244 245
## 2628.361 2628.361 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 246 247 248 249 250 251 252 253
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 254 255 256 257 258 259 260 261
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 262 263 264 265 266 267 268 269
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 270 271 272 273 274 275 276 277
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 278 279 280 308 309 310 311 312
## 1862.085 1862.085 1862.085 2628.361 2628.361 2628.361 2628.361 2628.361
## 313 314 315 316 317 318 319 320
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 321 322 323 324 325 326 327 328
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 329 330 331 332 333 334 335 336
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 337 338 339 340 341 342 343 344
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 345 346 347 348 349 350 351 352
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 353 354 355 356 357 358 359 360
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 361 362 363 364 365 366 367 368
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 1862.085 1862.085
## 369 370 371 372 373 374 375 376
## 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085 1862.085
## 377 378 379 380 381 382 383 384
## 1862.085 1862.085 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809
## 385 386 387 388 389 390 391 392
## 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809
## 393 394 395 396 397 398 399 400
## 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809
## 401 402 403 404 405 406 407 408
## 1095.809 1095.809 1095.809 1095.809 1095.809 2628.361 2628.361 2628.361
## 409 410 411 412 413 414 415 416
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 417 418 419 420 421 422 423 424
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 425 426 427 428 429 430 431 432
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361
## 433 434 435 436 437 438 439 440
## 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 2628.361 1095.809
## 441 442 443 444 445 446 447 448
## 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809
## 449 450 451 452 453 454 455 456
## 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809 1095.809
## 457 458
## 1095.809 1095.809cor(INTL$PricePremium,INTL$PitchEconomy)## [1] 0.3810529fit<-lm(PriceEconomy~PitchEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1772.78 -702.78 96.71 559.55 1813.55
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24141.74 2043.97 -11.81 <2e-16 ***
## PitchEconomy 820.33 65.58 12.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 840.5 on 416 degrees of freedom
## Multiple R-squared: 0.2733, Adjusted R-squared: 0.2716
## F-statistic: 156.5 on 1 and 416 DF, p-value: < 2.2e-16INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 8 9 10 11 12 13 14
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 15 16 17 18 19 20 21
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 22 23 24 25 26 27 28
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 29 30 31 32 33 34 35
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 36 37 38 39 40 41 42
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 43 44 45 46 47 48 49
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 50 51 52 53 54 55 56
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 57 58 59 60 61 62 63
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 64 65 66 67 68 69 70
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 71 72 73 82 83 84 85
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 86 87 88 89 90 91 92
## 1288.4545 1288.4545 1288.4545 1288.4545 468.1256 468.1256 468.1256
## 93 94 95 96 97 99 100
## 468.1256 468.1256 468.1256 468.1256 468.1256 1288.4545 1288.4545
## 101 102 103 104 105 106 107
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 108 109 110 111 112 113 114
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 115 116 117 118 119 120 121
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 122 123 124 125 126 127 128
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 129 130 131 132 133 134 135
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 136 137 138 139 140 141 142
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 143 144 145 146 147 148 149
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 150 151 156 157 158 159 160
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 161 162 163 164 165 166 167
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 168 169 170 171 172 173 174
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 175 176 177 178 179 180 181
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 182 183 184 185 186 187 188
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 189 190 191 192 193 194 195
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 196 197 198 199 200 201 202
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 203 204 205 206 207 208 209
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 210 211 212 213 214 215 216
## 1288.4545 1288.4545 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 217 218 219 220 221 222 223
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 224 225 226 227 228 229 230
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 231 232 233 234 235 236 237
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 238 239 240 241 242 243 244
## 2108.7833 2108.7833 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 245 246 247 248 249 250 251
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 252 253 254 255 256 257 258
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 259 260 261 262 263 264 265
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 266 267 268 269 270 271 272
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 273 274 275 276 277 278 279
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 280 308 309 310 311 312 313
## 1288.4545 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 314 315 316 317 318 319 320
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 321 322 323 324 325 326 327
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 328 329 330 331 332 333 334
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 335 336 337 338 339 340 341
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 342 343 344 345 346 347 348
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 349 350 351 352 353 354 355
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 356 357 358 359 360 361 362
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 363 364 365 366 367 368 369
## 2108.7833 2108.7833 2108.7833 2108.7833 1288.4545 1288.4545 1288.4545
## 370 371 372 373 374 375 376
## 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545 1288.4545
## 377 378 379 380 381 382 383
## 1288.4545 1288.4545 468.1256 468.1256 468.1256 468.1256 468.1256
## 384 385 386 387 388 389 390
## 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256
## 391 392 393 394 395 396 397
## 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256
## 398 399 400 401 402 403 404
## 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256
## 405 406 407 408 409 410 411
## 468.1256 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 412 413 414 415 416 417 418
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 419 420 421 422 423 424 425
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 426 427 428 429 430 431 432
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 433 434 435 436 437 438 439
## 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833 2108.7833
## 440 441 442 443 444 445 446
## 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256
## 447 448 449 450 451 452 453
## 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256 468.1256
## 454 455 456 457 458
## 468.1256 468.1256 468.1256 468.1256 468.1256cor(INTL$PriceEconomy,INTL$PitchEconomy)## [1] 0.5227929fit<-lm(PricePremium~PitchPremium,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchPremium, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2128.7 -702.7 88.4 784.3 5199.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36017.58 3105.53 11.60 <2e-16 ***
## PitchPremium -889.55 81.16 -10.96 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1113 on 416 degrees of freedom
## Multiple R-squared: 0.2241, Adjusted R-squared: 0.2222
## F-statistic: 120.1 on 1 and 416 DF, p-value: < 2.2e-16INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 8 9 10 11 12 13 14
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 15 16 17 18 19 20 21
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 22 23 24 25 26 27 28
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 29 30 31 32 33 34 35
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 36 37 38 39 40 41 42
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 43 44 45 46 47 48 49
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 50 51 52 53 54 55 56
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 57 58 59 60 61 62 63
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 64 65 66 67 68 69 70
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 71 72 73 82 83 84 85
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 86 87 88 89 90 91 92
## 2214.7445 2214.7445 2214.7445 2214.7445 435.6481 435.6481 435.6481
## 93 94 95 96 97 99 100
## 435.6481 435.6481 435.6481 435.6481 435.6481 2214.7445 2214.7445
## 101 102 103 104 105 106 107
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 108 109 110 111 112 113 114
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 115 116 117 118 119 120 121
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 122 123 124 125 126 127 128
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 129 130 131 132 133 134 135
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 136 137 138 139 140 141 142
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 143 144 145 146 147 148 149
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 150 151 156 157 158 159 160
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 161 162 163 164 165 166 167
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 168 169 170 171 172 173 174
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 175 176 177 178 179 180 181
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 182 183 184 185 186 187 188
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 189 190 191 192 193 194 195
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 196 197 198 199 200 201 202
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 203 204 205 206 207 208 209
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 210 211 212 213 214 215 216
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 217 218 219 220 221 222 223
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 224 225 226 227 228 229 230
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 231 232 233 234 235 236 237
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 238 239 240 241 242 243 244
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 245 246 247 248 249 250 251
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 252 253 254 255 256 257 258
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 259 260 261 262 263 264 265
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 266 267 268 269 270 271 272
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 273 274 275 276 277 278 279
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 280 308 309 310 311 312 313
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 314 315 316 317 318 319 320
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 321 322 323 324 325 326 327
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 328 329 330 331 332 333 334
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 335 336 337 338 339 340 341
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 342 343 344 345 346 347 348
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 349 350 351 352 353 354 355
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 356 357 358 359 360 361 362
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 363 364 365 366 367 368 369
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 370 371 372 373 374 375 376
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 377 378 379 380 381 382 383
## 2214.7445 2214.7445 435.6481 435.6481 435.6481 435.6481 435.6481
## 384 385 386 387 388 389 390
## 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481
## 391 392 393 394 395 396 397
## 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481
## 398 399 400 401 402 403 404
## 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481
## 405 406 407 408 409 410 411
## 435.6481 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 412 413 414 415 416 417 418
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 419 420 421 422 423 424 425
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 426 427 428 429 430 431 432
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 433 434 435 436 437 438 439
## 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445 2214.7445
## 440 441 442 443 444 445 446
## 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481
## 447 448 449 450 451 452 453
## 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481 435.6481
## 454 455 456 457 458
## 435.6481 435.6481 435.6481 435.6481 435.6481cor(INTL$PricePremium,INTL$PitchPremium)## [1] -0.4733605fit<-lm(PriceEconomy~PitchPremium,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchPremium, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1529.42 -654.42 -28.42 457.58 1998.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27255.17 2441.88 11.16 <2e-16 ***
## PitchPremium -675.28 63.82 -10.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 875.2 on 416 degrees of freedom
## Multiple R-squared: 0.2121, Adjusted R-squared: 0.2102
## F-statistic: 112 on 1 and 416 DF, p-value: < 2.2e-16INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 8 9 10 11 12 13 14
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 15 16 17 18 19 20 21
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 22 23 24 25 26 27 28
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 29 30 31 32 33 34 35
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 36 37 38 39 40 41 42
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 43 44 45 46 47 48 49
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 50 51 52 53 54 55 56
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 57 58 59 60 61 62 63
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 64 65 66 67 68 69 70
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 71 72 73 82 83 84 85
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 86 87 88 89 90 91 92
## 1594.4176 1594.4176 1594.4176 1594.4176 243.8519 243.8519 243.8519
## 93 94 95 96 97 99 100
## 243.8519 243.8519 243.8519 243.8519 243.8519 1594.4176 1594.4176
## 101 102 103 104 105 106 107
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 108 109 110 111 112 113 114
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 115 116 117 118 119 120 121
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 122 123 124 125 126 127 128
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 129 130 131 132 133 134 135
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 136 137 138 139 140 141 142
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 143 144 145 146 147 148 149
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 150 151 156 157 158 159 160
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 161 162 163 164 165 166 167
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 168 169 170 171 172 173 174
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 175 176 177 178 179 180 181
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 182 183 184 185 186 187 188
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 189 190 191 192 193 194 195
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 196 197 198 199 200 201 202
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 203 204 205 206 207 208 209
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 210 211 212 213 214 215 216
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 217 218 219 220 221 222 223
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 224 225 226 227 228 229 230
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 231 232 233 234 235 236 237
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 238 239 240 241 242 243 244
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 245 246 247 248 249 250 251
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 252 253 254 255 256 257 258
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 259 260 261 262 263 264 265
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 266 267 268 269 270 271 272
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 273 274 275 276 277 278 279
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 280 308 309 310 311 312 313
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 314 315 316 317 318 319 320
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 321 322 323 324 325 326 327
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 328 329 330 331 332 333 334
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 335 336 337 338 339 340 341
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 342 343 344 345 346 347 348
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 349 350 351 352 353 354 355
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 356 357 358 359 360 361 362
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 363 364 365 366 367 368 369
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 370 371 372 373 374 375 376
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 377 378 379 380 381 382 383
## 1594.4176 1594.4176 243.8519 243.8519 243.8519 243.8519 243.8519
## 384 385 386 387 388 389 390
## 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519
## 391 392 393 394 395 396 397
## 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519
## 398 399 400 401 402 403 404
## 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519
## 405 406 407 408 409 410 411
## 243.8519 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 412 413 414 415 416 417 418
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 419 420 421 422 423 424 425
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 426 427 428 429 430 431 432
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 433 434 435 436 437 438 439
## 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176 1594.4176
## 440 441 442 443 444 445 446
## 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519
## 447 448 449 450 451 452 453
## 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519 243.8519
## 454 455 456 457 458
## 243.8519 243.8519 243.8519 243.8519 243.8519cor(INTL$PriceEconomy,INTL$PitchPremium)## [1] -0.4605215fit<-lm(PriceEconomy~WidthPremium,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthPremium, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1599.51 -755.08 -57.55 836.42 1928.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8503.84 1002.61 8.482 3.92e-16 ***
## WidthPremium -359.96 50.89 -7.073 6.47e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 931.6 on 416 degrees of freedom
## Multiple R-squared: 0.1073, Adjusted R-squared: 0.1052
## F-statistic: 50.02 on 1 and 416 DF, p-value: 6.471e-12INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 8 9 10 11 12 13 14
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 15 16 17 18 19 20 21
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 22 23 24 25 26 27 28
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 29 30 31 32 33 34 35
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 36 37 38 39 40 41 42
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 43 44 45 46 47 48 49
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 50 51 52 53 54 55 56
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 57 58 59 60 61 62 63
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 944.5824 944.5824
## 64 65 66 67 68 69 70
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 71 72 73 82 83 84 85
## 944.5824 944.5824 944.5824 1664.5119 1664.5119 1664.5119 1664.5119
## 86 87 88 89 90 91 92
## 1664.5119 1664.5119 1664.5119 1664.5119 944.5824 944.5824 944.5824
## 93 94 95 96 97 99 100
## 944.5824 944.5824 944.5824 944.5824 944.5824 1664.5119 1664.5119
## 101 102 103 104 105 106 107
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 108 109 110 111 112 113 114
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 115 116 117 118 119 120 121
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 122 123 124 125 126 127 128
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 129 130 131 132 133 134 135
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 136 137 138 139 140 141 142
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 143 144 145 146 147 148 149
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 150 151 156 157 158 159 160
## 1664.5119 1664.5119 944.5824 944.5824 944.5824 944.5824 944.5824
## 161 162 163 164 165 166 167
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 168 169 170 171 172 173 174
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 175 176 177 178 179 180 181
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 182 183 184 185 186 187 188
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 189 190 191 192 193 194 195
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 196 197 198 199 200 201 202
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 203 204 205 206 207 208 209
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 210 211 212 213 214 215 216
## 944.5824 944.5824 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 217 218 219 220 221 222 223
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 224 225 226 227 228 229 230
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 231 232 233 234 235 236 237
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 238 239 240 241 242 243 244
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 245 246 247 248 249 250 251
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 252 253 254 255 256 257 258
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 259 260 261 262 263 264 265
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 266 267 268 269 270 271 272
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 273 274 275 276 277 278 279
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 280 308 309 310 311 312 313
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 314 315 316 317 318 319 320
## 1664.5119 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472
## 321 322 323 324 325 326 327
## 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472
## 328 329 330 331 332 333 334
## 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472
## 335 336 337 338 339 340 341
## 1304.5472 1304.5472 1304.5472 1304.5472 1664.5119 1664.5119 1664.5119
## 342 343 344 345 346 347 348
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 349 350 351 352 353 354 355
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 356 357 358 359 360 361 362
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 363 364 365 366 367 368 369
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 370 371 372 373 374 375 376
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 377 378 379 380 381 382 383
## 1664.5119 1664.5119 944.5824 944.5824 944.5824 944.5824 944.5824
## 384 385 386 387 388 389 390
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 391 392 393 394 395 396 397
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 398 399 400 401 402 403 404
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 405 406 407 408 409 410 411
## 944.5824 1664.5119 1664.5119 1664.5119 1664.5119 1304.5472 1304.5472
## 412 413 414 415 416 417 418
## 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472
## 419 420 421 422 423 424 425
## 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472 1304.5472
## 426 427 428 429 430 431 432
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 433 434 435 436 437 438 439
## 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119 1664.5119
## 440 441 442 443 444 445 446
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 447 448 449 450 451 452 453
## 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824 944.5824
## 454 455 456 457 458
## 944.5824 944.5824 944.5824 944.5824 944.5824cor(INTL$PriceEconomy,INTL$WidthPremium)## [1] -0.327631fit<-lm(PricePremium~WidthPremium,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthPremium, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2096.5 -1050.8 12.5 1013.5 5231.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7709.37 1330.64 5.794 1.36e-08 ***
## WidthPremium -290.89 67.55 -4.306 2.07e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1236 on 416 degrees of freedom
## Multiple R-squared: 0.04268, Adjusted R-squared: 0.04038
## F-statistic: 18.55 on 1 and 416 DF, p-value: 2.071e-05INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 9 10 11 12 13 14 15 16
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 17 18 19 20 21 22 23 24
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 25 26 27 28 29 30 31 32
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 33 34 35 36 37 38 39 40
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 41 42 43 44 45 46 47 48
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 49 50 51 52 53 54 55 56
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 57 58 59 60 61 62 63 64
## 2182.544 2182.544 2182.544 2182.544 2182.544 1600.773 1600.773 1600.773
## 65 66 67 68 69 70 71 72
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 73 82 83 84 85 86 87 88
## 1600.773 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 89 90 91 92 93 94 95 96
## 2182.544 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 97 99 100 101 102 103 104 105
## 1600.773 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 106 107 108 109 110 111 112 113
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 114 115 116 117 118 119 120 121
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 122 123 124 125 126 127 128 129
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 130 131 132 133 134 135 136 137
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 138 139 140 141 142 143 144 145
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 146 147 148 149 150 151 156 157
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 1600.773 1600.773
## 158 159 160 161 162 163 164 165
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 166 167 168 169 170 171 172 173
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 174 175 176 177 178 179 180 181
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 182 183 184 185 186 187 188 189
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 190 191 192 193 194 195 196 197
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 198 199 200 201 202 203 204 205
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 206 207 208 209 210 211 212 213
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 2182.544 2182.544
## 214 215 216 217 218 219 220 221
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 222 223 224 225 226 227 228 229
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 230 231 232 233 234 235 236 237
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 238 239 240 241 242 243 244 245
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 246 247 248 249 250 251 252 253
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 254 255 256 257 258 259 260 261
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 262 263 264 265 266 267 268 269
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 270 271 272 273 274 275 276 277
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 278 279 280 308 309 310 311 312
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 313 314 315 316 317 318 319 320
## 2182.544 2182.544 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659
## 321 322 323 324 325 326 327 328
## 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659
## 329 330 331 332 333 334 335 336
## 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659
## 337 338 339 340 341 342 343 344
## 1891.659 1891.659 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 345 346 347 348 349 350 351 352
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 353 354 355 356 357 358 359 360
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 361 362 363 364 365 366 367 368
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 369 370 371 372 373 374 375 376
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 377 378 379 380 381 382 383 384
## 2182.544 2182.544 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 385 386 387 388 389 390 391 392
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 393 394 395 396 397 398 399 400
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 401 402 403 404 405 406 407 408
## 1600.773 1600.773 1600.773 1600.773 1600.773 2182.544 2182.544 2182.544
## 409 410 411 412 413 414 415 416
## 2182.544 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659
## 417 418 419 420 421 422 423 424
## 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659 1891.659
## 425 426 427 428 429 430 431 432
## 1891.659 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544
## 433 434 435 436 437 438 439 440
## 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 2182.544 1600.773
## 441 442 443 444 445 446 447 448
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 449 450 451 452 453 454 455 456
## 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773 1600.773
## 457 458
## 1600.773 1600.773cor(INTL$PricePremium,INTL$WidthPremium)## [1] -0.2065882fit<-lm(PriceEconomy~WidthEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1354.27 -873.10 34.08 637.29 2178.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2270.91 1604.19 1.416 0.158
## WidthEconomy -47.57 89.63 -0.531 0.596
##
## Residual standard error: 985.7 on 416 degrees of freedom
## Multiple R-squared: 0.0006766, Adjusted R-squared: -0.001726
## F-statistic: 0.2816 on 1 and 416 DF, p-value: 0.5959INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 9 10 11 12 13 14 15 16
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 17 18 19 20 21 22 23 24
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 25 26 27 28 29 30 31 32
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 33 34 35 36 37 38 39 40
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 41 42 43 44 45 46 47 48
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 49 50 51 52 53 54 55 56
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 57 58 59 60 61 62 63 64
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 65 66 67 68 69 70 71 72
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 73 82 83 84 85 86 87 88
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 89 90 91 92 93 94 95 96
## 1414.708 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 97 99 100 101 102 103 104 105
## 1462.275 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 106 107 108 109 110 111 112 113
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 114 115 116 117 118 119 120 121
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 122 123 124 125 126 127 128 129
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 130 131 132 133 134 135 136 137
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 138 139 140 141 142 143 144 145
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 146 147 148 149 150 151 156 157
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 158 159 160 161 162 163 164 165
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 166 167 168 169 170 171 172 173
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 174 175 176 177 178 179 180 181
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 182 183 184 185 186 187 188 189
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 190 191 192 193 194 195 196 197
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 198 199 200 201 202 203 204 205
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 206 207 208 209 210 211 212 213
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 214 215 216 217 218 219 220 221
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 222 223 224 225 226 227 228 229
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 230 231 232 233 234 235 236 237
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 238 239 240 241 242 243 244 245
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 246 247 248 249 250 251 252 253
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 254 255 256 257 258 259 260 261
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 262 263 264 265 266 267 268 269
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 270 271 272 273 274 275 276 277
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 278 279 280 308 309 310 311 312
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 313 314 315 316 317 318 319 320
## 1414.708 1414.708 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 321 322 323 324 325 326 327 328
## 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 329 330 331 332 333 334 335 336
## 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 337 338 339 340 341 342 343 344
## 1367.141 1367.141 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 345 346 347 348 349 350 351 352
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 353 354 355 356 357 358 359 360
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 361 362 363 364 365 366 367 368
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1414.708 1414.708
## 369 370 371 372 373 374 375 376
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 377 378 379 380 381 382 383 384
## 1414.708 1414.708 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 385 386 387 388 389 390 391 392
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 393 394 395 396 397 398 399 400
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 401 402 403 404 405 406 407 408
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 409 410 411 412 413 414 415 416
## 1462.275 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 417 418 419 420 421 422 423 424
## 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 425 426 427 428 429 430 431 432
## 1367.141 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 433 434 435 436 437 438 439 440
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1462.275
## 441 442 443 444 445 446 447 448
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 449 450 451 452 453 454 455 456
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 457 458
## 1462.275 1462.275cor(INTL$PriceEconomy,INTL$WidthEconomy)## [1] -0.02601124fit<-lm(PricePremium~WidthEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1912.6 -1209.6 85.4 1000.4 5415.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -242.8 2053.7 -0.118 0.906
## WidthEconomy 124.5 114.7 1.085 0.278
##
## Residual standard error: 1262 on 416 degrees of freedom
## Multiple R-squared: 0.002823, Adjusted R-squared: 0.0004262
## F-statistic: 1.178 on 1 and 416 DF, p-value: 0.2784INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 9 10 11 12 13 14 15 16
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 17 18 19 20 21 22 23 24
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 25 26 27 28 29 30 31 32
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 33 34 35 36 37 38 39 40
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 41 42 43 44 45 46 47 48
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 49 50 51 52 53 54 55 56
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 57 58 59 60 61 62 63 64
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 65 66 67 68 69 70 71 72
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 73 82 83 84 85 86 87 88
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 89 90 91 92 93 94 95 96
## 1998.613 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 97 99 100 101 102 103 104 105
## 1874.088 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 106 107 108 109 110 111 112 113
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 114 115 116 117 118 119 120 121
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 122 123 124 125 126 127 128 129
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 130 131 132 133 134 135 136 137
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 138 139 140 141 142 143 144 145
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 146 147 148 149 150 151 156 157
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 158 159 160 161 162 163 164 165
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 166 167 168 169 170 171 172 173
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 174 175 176 177 178 179 180 181
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 182 183 184 185 186 187 188 189
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 190 191 192 193 194 195 196 197
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 198 199 200 201 202 203 204 205
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 206 207 208 209 210 211 212 213
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 214 215 216 217 218 219 220 221
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 222 223 224 225 226 227 228 229
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 230 231 232 233 234 235 236 237
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 238 239 240 241 242 243 244 245
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 246 247 248 249 250 251 252 253
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 254 255 256 257 258 259 260 261
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 262 263 264 265 266 267 268 269
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 270 271 272 273 274 275 276 277
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 278 279 280 308 309 310 311 312
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 313 314 315 316 317 318 319 320
## 1998.613 1998.613 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 321 322 323 324 325 326 327 328
## 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 329 330 331 332 333 334 335 336
## 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 337 338 339 340 341 342 343 344
## 2123.138 2123.138 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 345 346 347 348 349 350 351 352
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 353 354 355 356 357 358 359 360
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 361 362 363 364 365 366 367 368
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1998.613 1998.613
## 369 370 371 372 373 374 375 376
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 377 378 379 380 381 382 383 384
## 1998.613 1998.613 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 385 386 387 388 389 390 391 392
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 393 394 395 396 397 398 399 400
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 401 402 403 404 405 406 407 408
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 409 410 411 412 413 414 415 416
## 1874.088 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 417 418 419 420 421 422 423 424
## 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 425 426 427 428 429 430 431 432
## 2123.138 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 433 434 435 436 437 438 439 440
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1874.088
## 441 442 443 444 445 446 447 448
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 449 450 451 452 453 454 455 456
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 457 458
## 1874.088 1874.088cor(INTL$PricePremium,INTL$WidthEconomy)## [1] 0.05313392fit<-lm(PriceEconomy~WidthEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1354.27 -873.10 34.08 637.29 2178.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2270.91 1604.19 1.416 0.158
## WidthEconomy -47.57 89.63 -0.531 0.596
##
## Residual standard error: 985.7 on 416 degrees of freedom
## Multiple R-squared: 0.0006766, Adjusted R-squared: -0.001726
## F-statistic: 0.2816 on 1 and 416 DF, p-value: 0.5959INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 9 10 11 12 13 14 15 16
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 17 18 19 20 21 22 23 24
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 25 26 27 28 29 30 31 32
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 33 34 35 36 37 38 39 40
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 41 42 43 44 45 46 47 48
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 49 50 51 52 53 54 55 56
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 57 58 59 60 61 62 63 64
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 65 66 67 68 69 70 71 72
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 73 82 83 84 85 86 87 88
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 89 90 91 92 93 94 95 96
## 1414.708 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 97 99 100 101 102 103 104 105
## 1462.275 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 106 107 108 109 110 111 112 113
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 114 115 116 117 118 119 120 121
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 122 123 124 125 126 127 128 129
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 130 131 132 133 134 135 136 137
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 138 139 140 141 142 143 144 145
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 146 147 148 149 150 151 156 157
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 158 159 160 161 162 163 164 165
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 166 167 168 169 170 171 172 173
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 174 175 176 177 178 179 180 181
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 182 183 184 185 186 187 188 189
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 190 191 192 193 194 195 196 197
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 198 199 200 201 202 203 204 205
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 206 207 208 209 210 211 212 213
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 214 215 216 217 218 219 220 221
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 222 223 224 225 226 227 228 229
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 230 231 232 233 234 235 236 237
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 238 239 240 241 242 243 244 245
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 246 247 248 249 250 251 252 253
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 254 255 256 257 258 259 260 261
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 262 263 264 265 266 267 268 269
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 270 271 272 273 274 275 276 277
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 278 279 280 308 309 310 311 312
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 313 314 315 316 317 318 319 320
## 1414.708 1414.708 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 321 322 323 324 325 326 327 328
## 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 329 330 331 332 333 334 335 336
## 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 337 338 339 340 341 342 343 344
## 1367.141 1367.141 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 345 346 347 348 349 350 351 352
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 353 354 355 356 357 358 359 360
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 361 362 363 364 365 366 367 368
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1414.708 1414.708
## 369 370 371 372 373 374 375 376
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 377 378 379 380 381 382 383 384
## 1414.708 1414.708 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 385 386 387 388 389 390 391 392
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 393 394 395 396 397 398 399 400
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 401 402 403 404 405 406 407 408
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 409 410 411 412 413 414 415 416
## 1462.275 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 417 418 419 420 421 422 423 424
## 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141 1367.141
## 425 426 427 428 429 430 431 432
## 1367.141 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708
## 433 434 435 436 437 438 439 440
## 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1414.708 1462.275
## 441 442 443 444 445 446 447 448
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 449 450 451 452 453 454 455 456
## 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275 1462.275
## 457 458
## 1462.275 1462.275cor(INTL$PriceEconomy,INTL$WidthEconomy)## [1] -0.02601124fit<-lm(PricePremium~WidthEconomy,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthEconomy, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1912.6 -1209.6 85.4 1000.4 5415.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -242.8 2053.7 -0.118 0.906
## WidthEconomy 124.5 114.7 1.085 0.278
##
## Residual standard error: 1262 on 416 degrees of freedom
## Multiple R-squared: 0.002823, Adjusted R-squared: 0.0004262
## F-statistic: 1.178 on 1 and 416 DF, p-value: 0.2784INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 9 10 11 12 13 14 15 16
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 17 18 19 20 21 22 23 24
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 25 26 27 28 29 30 31 32
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 33 34 35 36 37 38 39 40
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 41 42 43 44 45 46 47 48
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 49 50 51 52 53 54 55 56
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 57 58 59 60 61 62 63 64
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 65 66 67 68 69 70 71 72
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 73 82 83 84 85 86 87 88
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 89 90 91 92 93 94 95 96
## 1998.613 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 97 99 100 101 102 103 104 105
## 1874.088 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 106 107 108 109 110 111 112 113
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 114 115 116 117 118 119 120 121
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 122 123 124 125 126 127 128 129
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 130 131 132 133 134 135 136 137
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 138 139 140 141 142 143 144 145
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 146 147 148 149 150 151 156 157
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 158 159 160 161 162 163 164 165
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 166 167 168 169 170 171 172 173
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 174 175 176 177 178 179 180 181
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 182 183 184 185 186 187 188 189
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 190 191 192 193 194 195 196 197
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 198 199 200 201 202 203 204 205
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 206 207 208 209 210 211 212 213
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 214 215 216 217 218 219 220 221
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 222 223 224 225 226 227 228 229
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 230 231 232 233 234 235 236 237
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 238 239 240 241 242 243 244 245
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 246 247 248 249 250 251 252 253
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 254 255 256 257 258 259 260 261
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 262 263 264 265 266 267 268 269
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 270 271 272 273 274 275 276 277
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 278 279 280 308 309 310 311 312
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 313 314 315 316 317 318 319 320
## 1998.613 1998.613 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 321 322 323 324 325 326 327 328
## 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 329 330 331 332 333 334 335 336
## 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 337 338 339 340 341 342 343 344
## 2123.138 2123.138 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 345 346 347 348 349 350 351 352
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 353 354 355 356 357 358 359 360
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 361 362 363 364 365 366 367 368
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1998.613 1998.613
## 369 370 371 372 373 374 375 376
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 377 378 379 380 381 382 383 384
## 1998.613 1998.613 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 385 386 387 388 389 390 391 392
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 393 394 395 396 397 398 399 400
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 401 402 403 404 405 406 407 408
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 409 410 411 412 413 414 415 416
## 1874.088 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 417 418 419 420 421 422 423 424
## 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138 2123.138
## 425 426 427 428 429 430 431 432
## 2123.138 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613
## 433 434 435 436 437 438 439 440
## 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1998.613 1874.088
## 441 442 443 444 445 446 447 448
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 449 450 451 452 453 454 455 456
## 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088 1874.088
## 457 458
## 1874.088 1874.088cor(INTL$PricePremium,INTL$WidthEconomy)## [1] 0.05313392fit<-lm(PriceEconomy~SeatsTotal,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsTotal, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1397.46 -889.64 4.23 636.51 2190.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1327.9383 149.3201 8.893 <2e-16 ***
## SeatsTotal 0.3758 0.5772 0.651 0.515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 985.5 on 416 degrees of freedom
## Multiple R-squared: 0.001018, Adjusted R-squared: -0.001384
## F-statistic: 0.4238 on 1 and 416 DF, p-value: 0.5154INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812
## 9 10 11 12 13 14 15 16
## 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812
## 17 18 19 20 21 22 23 24
## 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812
## 25 26 27 28 29 30 31 32
## 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812
## 33 34 35 36 37 38 39 40
## 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812
## 41 42 43 44 45 46 47 48
## 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812 1388.812
## 49 50 51 52 53 54 55 56
## 1388.812 1388.812 1388.812 1390.315 1390.315 1390.315 1390.315 1390.315
## 57 58 59 60 61 62 63 64
## 1390.315 1390.315 1390.315 1390.315 1390.315 1415.491 1415.491 1415.491
## 65 66 67 68 69 70 71 72
## 1415.491 1415.491 1415.491 1415.491 1415.491 1415.491 1415.491 1415.491
## 73 82 83 84 85 86 87 88
## 1415.491 1440.291 1440.291 1440.291 1440.291 1440.291 1440.291 1440.291
## 89 90 91 92 93 94 95 96
## 1440.291 1390.315 1390.315 1390.315 1390.315 1390.315 1390.315 1390.315
## 97 99 100 101 102 103 104 105
## 1390.315 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461
## 106 107 108 109 110 111 112 113
## 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461
## 114 115 116 117 118 119 120 121
## 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461
## 122 123 124 125 126 127 128 129
## 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461
## 130 131 132 133 134 135 136 137
## 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461
## 138 139 140 141 142 143 144 145
## 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461 1462.461
## 146 147 148 149 150 151 156 157
## 1462.461 1462.461 1462.461 1462.461 1465.843 1462.461 1415.491 1415.491
## 158 159 160 161 162 163 164 165
## 1415.491 1415.491 1415.491 1415.491 1415.491 1415.491 1493.650 1493.650
## 166 167 168 169 170 171 172 173
## 1493.650 1493.650 1415.491 1415.491 1415.491 1415.491 1415.491 1415.491
## 174 175 176 177 178 179 180 181
## 1493.650 1493.650 1493.650 1493.650 1415.491 1415.491 1415.491 1415.491
## 182 183 184 185 186 187 188 189
## 1415.491 1415.491 1415.491 1429.770 1429.770 1429.770 1429.770 1429.770
## 190 191 192 193 194 195 196 197
## 1429.770 1429.770 1429.770 1429.770 1429.770 1429.770 1429.770 1429.770
## 198 199 200 201 202 203 204 205
## 1429.770 1429.770 1429.770 1429.770 1429.770 1429.770 1429.770 1429.770
## 206 207 208 209 210 211 212 213
## 1429.770 1429.770 1429.770 1429.770 1429.770 1429.770 1391.066 1391.066
## 214 215 216 217 218 219 220 221
## 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066
## 222 223 224 225 226 227 228 229
## 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066
## 230 231 232 233 234 235 236 237
## 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066 1391.066
## 238 239 240 241 242 243 244 245
## 1391.066 1391.066 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776
## 246 247 248 249 250 251 252 253
## 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776
## 254 255 256 257 258 259 260 261
## 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776
## 262 263 264 265 266 267 268 269
## 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776
## 270 271 272 273 274 275 276 277
## 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776 1432.776
## 278 279 280 308 309 310 311 312
## 1432.776 1432.776 1432.776 1391.066 1391.066 1391.066 1391.066 1391.066
## 313 314 315 316 317 318 319 320
## 1391.066 1391.066 1407.600 1407.600 1407.600 1407.600 1407.600 1407.600
## 321 322 323 324 325 326 327 328
## 1407.600 1407.600 1407.600 1407.600 1407.600 1407.600 1407.600 1407.600
## 329 330 331 332 333 334 335 336
## 1407.600 1407.600 1407.600 1407.600 1407.600 1407.600 1407.600 1407.600
## 337 338 339 340 341 342 343 344
## 1407.600 1407.600 1413.612 1413.612 1413.612 1413.612 1413.612 1413.612
## 345 346 347 348 349 350 351 352
## 1413.612 1413.612 1413.612 1413.612 1413.612 1413.612 1413.612 1413.612
## 353 354 355 356 357 358 359 360
## 1413.612 1402.339 1402.339 1402.339 1402.339 1413.612 1413.612 1413.612
## 361 362 363 364 365 366 367 368
## 1402.339 1402.339 1413.612 1413.612 1413.612 1413.612 1413.236 1413.236
## 369 370 371 372 373 374 375 376
## 1413.236 1413.236 1413.236 1413.236 1413.236 1413.236 1413.236 1413.236
## 377 378 379 380 381 382 383 384
## 1413.236 1413.236 1380.545 1380.545 1380.545 1380.545 1380.545 1380.545
## 385 386 387 388 389 390 391 392
## 1380.545 1380.545 1380.545 1380.545 1380.545 1380.545 1380.545 1380.545
## 393 394 395 396 397 398 399 400
## 1380.545 1380.545 1380.545 1380.545 1380.545 1380.545 1380.545 1380.545
## 401 402 403 404 405 406 407 408
## 1380.545 1380.545 1380.545 1380.545 1380.545 1418.121 1418.121 1418.121
## 409 410 411 412 413 414 415 416
## 1418.121 1466.595 1466.595 1466.595 1466.595 1466.595 1466.595 1466.595
## 417 418 419 420 421 422 423 424
## 1466.595 1466.595 1466.595 1466.595 1466.595 1466.595 1466.595 1466.595
## 425 426 427 428 429 430 431 432
## 1466.595 1488.389 1488.389 1488.389 1488.389 1488.389 1488.389 1488.389
## 433 434 435 436 437 438 439 440
## 1488.389 1488.389 1488.389 1488.389 1488.389 1488.389 1488.389 1391.818
## 441 442 443 444 445 446 447 448
## 1391.818 1391.818 1391.818 1391.818 1391.818 1391.818 1391.818 1391.818
## 449 450 451 452 453 454 455 456
## 1391.818 1391.818 1391.818 1391.818 1391.818 1391.818 1391.818 1391.818
## 457 458
## 1391.818 1391.818cor(INTL$PriceEconomy,INTL$SeatsTotal)## [1] 0.03190204fit<-lm(PricePremium~SeatsTotal,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsTotal, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2040.9 -1136.5 202.9 1046.9 5451.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1678.2099 190.8009 8.796 <2e-16 ***
## SeatsTotal 1.2526 0.7375 1.698 0.0902 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1259 on 416 degrees of freedom
## Multiple R-squared: 0.006886, Adjusted R-squared: 0.004499
## F-statistic: 2.884 on 1 and 416 DF, p-value: 0.09019INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134
## 9 10 11 12 13 14 15 16
## 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134
## 17 18 19 20 21 22 23 24
## 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134
## 25 26 27 28 29 30 31 32
## 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134
## 33 34 35 36 37 38 39 40
## 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134
## 41 42 43 44 45 46 47 48
## 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134 1881.134
## 49 50 51 52 53 54 55 56
## 1881.134 1881.134 1881.134 1886.144 1886.144 1886.144 1886.144 1886.144
## 57 58 59 60 61 62 63 64
## 1886.144 1886.144 1886.144 1886.144 1886.144 1970.069 1970.069 1970.069
## 65 66 67 68 69 70 71 72
## 1970.069 1970.069 1970.069 1970.069 1970.069 1970.069 1970.069 1970.069
## 73 82 83 84 85 86 87 88
## 1970.069 2052.742 2052.742 2052.742 2052.742 2052.742 2052.742 2052.742
## 89 90 91 92 93 94 95 96
## 2052.742 1886.144 1886.144 1886.144 1886.144 1886.144 1886.144 1886.144
## 97 99 100 101 102 103 104 105
## 1886.144 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647
## 106 107 108 109 110 111 112 113
## 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647
## 114 115 116 117 118 119 120 121
## 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647
## 122 123 124 125 126 127 128 129
## 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647
## 130 131 132 133 134 135 136 137
## 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647
## 138 139 140 141 142 143 144 145
## 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647 2126.647
## 146 147 148 149 150 151 156 157
## 2126.647 2126.647 2126.647 2126.647 2137.920 2126.647 1970.069 1970.069
## 158 159 160 161 162 163 164 165
## 1970.069 1970.069 1970.069 1970.069 1970.069 1970.069 2230.614 2230.614
## 166 167 168 169 170 171 172 173
## 2230.614 2230.614 1970.069 1970.069 1970.069 1970.069 1970.069 1970.069
## 174 175 176 177 178 179 180 181
## 2230.614 2230.614 2230.614 2230.614 1970.069 1970.069 1970.069 1970.069
## 182 183 184 185 186 187 188 189
## 1970.069 1970.069 1970.069 2017.669 2017.669 2017.669 2017.669 2017.669
## 190 191 192 193 194 195 196 197
## 2017.669 2017.669 2017.669 2017.669 2017.669 2017.669 2017.669 2017.669
## 198 199 200 201 202 203 204 205
## 2017.669 2017.669 2017.669 2017.669 2017.669 2017.669 2017.669 2017.669
## 206 207 208 209 210 211 212 213
## 2017.669 2017.669 2017.669 2017.669 2017.669 2017.669 1888.649 1888.649
## 214 215 216 217 218 219 220 221
## 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649
## 222 223 224 225 226 227 228 229
## 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649
## 230 231 232 233 234 235 236 237
## 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649 1888.649
## 238 239 240 241 242 243 244 245
## 1888.649 1888.649 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690
## 246 247 248 249 250 251 252 253
## 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690
## 254 255 256 257 258 259 260 261
## 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690
## 262 263 264 265 266 267 268 269
## 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690
## 270 271 272 273 274 275 276 277
## 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690 2027.690
## 278 279 280 308 309 310 311 312
## 2027.690 2027.690 2027.690 1888.649 1888.649 1888.649 1888.649 1888.649
## 313 314 315 316 317 318 319 320
## 1888.649 1888.649 1943.765 1943.765 1943.765 1943.765 1943.765 1943.765
## 321 322 323 324 325 326 327 328
## 1943.765 1943.765 1943.765 1943.765 1943.765 1943.765 1943.765 1943.765
## 329 330 331 332 333 334 335 336
## 1943.765 1943.765 1943.765 1943.765 1943.765 1943.765 1943.765 1943.765
## 337 338 339 340 341 342 343 344
## 1943.765 1943.765 1963.806 1963.806 1963.806 1963.806 1963.806 1963.806
## 345 346 347 348 349 350 351 352
## 1963.806 1963.806 1963.806 1963.806 1963.806 1963.806 1963.806 1963.806
## 353 354 355 356 357 358 359 360
## 1963.806 1926.228 1926.228 1926.228 1926.228 1963.806 1963.806 1963.806
## 361 362 363 364 365 366 367 368
## 1926.228 1926.228 1963.806 1963.806 1963.806 1963.806 1962.554 1962.554
## 369 370 371 372 373 374 375 376
## 1962.554 1962.554 1962.554 1962.554 1962.554 1962.554 1962.554 1962.554
## 377 378 379 380 381 382 383 384
## 1962.554 1962.554 1853.576 1853.576 1853.576 1853.576 1853.576 1853.576
## 385 386 387 388 389 390 391 392
## 1853.576 1853.576 1853.576 1853.576 1853.576 1853.576 1853.576 1853.576
## 393 394 395 396 397 398 399 400
## 1853.576 1853.576 1853.576 1853.576 1853.576 1853.576 1853.576 1853.576
## 401 402 403 404 405 406 407 408
## 1853.576 1853.576 1853.576 1853.576 1853.576 1978.838 1978.838 1978.838
## 409 410 411 412 413 414 415 416
## 1978.838 2140.425 2140.425 2140.425 2140.425 2140.425 2140.425 2140.425
## 417 418 419 420 421 422 423 424
## 2140.425 2140.425 2140.425 2140.425 2140.425 2140.425 2140.425 2140.425
## 425 426 427 428 429 430 431 432
## 2140.425 2213.077 2213.077 2213.077 2213.077 2213.077 2213.077 2213.077
## 433 434 435 436 437 438 439 440
## 2213.077 2213.077 2213.077 2213.077 2213.077 2213.077 2213.077 1891.155
## 441 442 443 444 445 446 447 448
## 1891.155 1891.155 1891.155 1891.155 1891.155 1891.155 1891.155 1891.155
## 449 450 451 452 453 454 455 456
## 1891.155 1891.155 1891.155 1891.155 1891.155 1891.155 1891.155 1891.155
## 457 458
## 1891.155 1891.155cor(INTL$PricePremium,INTL$SeatsTotal)## [1] 0.08298154fit<-lm(PriceEconomy~PitchDifference,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchDifference, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1560.64 -661.55 99.48 589.48 1696.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4501.37 245.11 18.36 <2e-16 ***
## PitchDifference -434.12 34.05 -12.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 836.1 on 416 degrees of freedom
## Multiple R-squared: 0.281, Adjusted R-squared: 0.2793
## F-statistic: 162.6 on 1 and 416 DF, p-value: < 2.2e-16INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 8 9 10 11 12 13 14
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 15 16 17 18 19 20 21
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 22 23 24 25 26 27 28
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 29 30 31 32 33 34 35
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 36 37 38 39 40 41 42
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 43 44 45 46 47 48 49
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 50 51 52 53 54 55 56
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 57 58 59 60 61 62 63
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 64 65 66 67 68 69 70
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 71 72 73 82 83 84 85
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 86 87 88 89 90 91 92
## 1462.5238 1462.5238 1462.5238 1462.5238 160.1607 160.1607 160.1607
## 93 94 95 96 97 99 100
## 160.1607 160.1607 160.1607 160.1607 160.1607 1462.5238 1462.5238
## 101 102 103 104 105 106 107
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 108 109 110 111 112 113 114
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 115 116 117 118 119 120 121
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 122 123 124 125 126 127 128
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 129 130 131 132 133 134 135
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 136 137 138 139 140 141 142
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 143 144 145 146 147 148 149
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 150 151 156 157 158 159 160
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 161 162 163 164 165 166 167
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 168 169 170 171 172 173 174
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 175 176 177 178 179 180 181
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 182 183 184 185 186 187 188
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 189 190 191 192 193 194 195
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 196 197 198 199 200 201 202
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 203 204 205 206 207 208 209
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 210 211 212 213 214 215 216
## 1462.5238 1462.5238 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 217 218 219 220 221 222 223
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 224 225 226 227 228 229 230
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 231 232 233 234 235 236 237
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 238 239 240 241 242 243 244
## 1896.6449 1896.6449 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 245 246 247 248 249 250 251
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 252 253 254 255 256 257 258
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 259 260 261 262 263 264 265
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 266 267 268 269 270 271 272
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 273 274 275 276 277 278 279
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 280 308 309 310 311 312 313
## 1462.5238 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 314 315 316 317 318 319 320
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 321 322 323 324 325 326 327
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 328 329 330 331 332 333 334
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 335 336 337 338 339 340 341
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 342 343 344 345 346 347 348
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 349 350 351 352 353 354 355
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 356 357 358 359 360 361 362
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 363 364 365 366 367 368 369
## 1896.6449 1896.6449 1896.6449 1896.6449 1462.5238 1462.5238 1462.5238
## 370 371 372 373 374 375 376
## 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238 1462.5238
## 377 378 379 380 381 382 383
## 1462.5238 1462.5238 160.1607 160.1607 160.1607 160.1607 160.1607
## 384 385 386 387 388 389 390
## 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607
## 391 392 393 394 395 396 397
## 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607
## 398 399 400 401 402 403 404
## 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607
## 405 406 407 408 409 410 411
## 160.1607 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 412 413 414 415 416 417 418
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 419 420 421 422 423 424 425
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 426 427 428 429 430 431 432
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 433 434 435 436 437 438 439
## 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449 1896.6449
## 440 441 442 443 444 445 446
## 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607
## 447 448 449 450 451 452 453
## 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607 160.1607
## 454 455 456 457 458
## 160.1607 160.1607 160.1607 160.1607 160.1607cor(INTL$PriceEconomy,INTL$PitchDifference)## [1] -0.530074fit<-lm(PricePremium~PitchDifference,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchDifference, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1946.6 -819.6 122.1 806.2 5381.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5436.2 328.3 16.56 <2e-16 ***
## PitchDifference -486.2 45.6 -10.66 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1120 on 416 degrees of freedom
## Multiple R-squared: 0.2146, Adjusted R-squared: 0.2127
## F-statistic: 113.7 on 1 and 416 DF, p-value: < 2.2e-16INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 8 9 10 11 12 13 14
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 15 16 17 18 19 20 21
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 22 23 24 25 26 27 28
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 29 30 31 32 33 34 35
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 36 37 38 39 40 41 42
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 43 44 45 46 47 48 49
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 50 51 52 53 54 55 56
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 57 58 59 60 61 62 63
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 64 65 66 67 68 69 70
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 71 72 73 82 83 84 85
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 86 87 88 89 90 91 92
## 2032.6020 2032.6020 2032.6020 2032.6020 573.8968 573.8968 573.8968
## 93 94 95 96 97 99 100
## 573.8968 573.8968 573.8968 573.8968 573.8968 2032.6020 2032.6020
## 101 102 103 104 105 106 107
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 108 109 110 111 112 113 114
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 115 116 117 118 119 120 121
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 122 123 124 125 126 127 128
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 129 130 131 132 133 134 135
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 136 137 138 139 140 141 142
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 143 144 145 146 147 148 149
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 150 151 156 157 158 159 160
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 161 162 163 164 165 166 167
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 168 169 170 171 172 173 174
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 175 176 177 178 179 180 181
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 182 183 184 185 186 187 188
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 189 190 191 192 193 194 195
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 196 197 198 199 200 201 202
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 203 204 205 206 207 208 209
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 210 211 212 213 214 215 216
## 2032.6020 2032.6020 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 217 218 219 220 221 222 223
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 224 225 226 227 228 229 230
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 231 232 233 234 235 236 237
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 238 239 240 241 242 243 244
## 2518.8371 2518.8371 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 245 246 247 248 249 250 251
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 252 253 254 255 256 257 258
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 259 260 261 262 263 264 265
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 266 267 268 269 270 271 272
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 273 274 275 276 277 278 279
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 280 308 309 310 311 312 313
## 2032.6020 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 314 315 316 317 318 319 320
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 321 322 323 324 325 326 327
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 328 329 330 331 332 333 334
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 335 336 337 338 339 340 341
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 342 343 344 345 346 347 348
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 349 350 351 352 353 354 355
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 356 357 358 359 360 361 362
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 363 364 365 366 367 368 369
## 2518.8371 2518.8371 2518.8371 2518.8371 2032.6020 2032.6020 2032.6020
## 370 371 372 373 374 375 376
## 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020 2032.6020
## 377 378 379 380 381 382 383
## 2032.6020 2032.6020 573.8968 573.8968 573.8968 573.8968 573.8968
## 384 385 386 387 388 389 390
## 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968
## 391 392 393 394 395 396 397
## 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968
## 398 399 400 401 402 403 404
## 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968
## 405 406 407 408 409 410 411
## 573.8968 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 412 413 414 415 416 417 418
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 419 420 421 422 423 424 425
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 426 427 428 429 430 431 432
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 433 434 435 436 437 438 439
## 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371 2518.8371
## 440 441 442 443 444 445 446
## 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968
## 447 448 449 450 451 452 453
## 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968 573.8968
## 454 455 456 457 458
## 573.8968 573.8968 573.8968 573.8968 573.8968cor(INTL$PricePremium,INTL$PitchDifference)## [1] -0.4632659fit<-lm(PriceEconomy~WidthDifference,data = INTL)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthDifference, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1526.2 -786.0 -110.7 731.7 2218.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1808.11 87.75 20.605 < 2e-16 ***
## WidthDifference -216.92 41.51 -5.226 2.75e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 955.2 on 416 degrees of freedom
## Multiple R-squared: 0.0616, Adjusted R-squared: 0.05934
## F-statistic: 27.31 on 1 and 416 DF, p-value: 2.751e-07INTL$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 1813 1813 1813 1813 2052 2052 2052 2052 1919
## [71] 1919 1919 540 1444 1444 1444 1444 1824 1824 1824 1823 354 354 354
## [85] 354 464 464 464 489 2384 2384 2384 2384 1848 1848 1848 1848 1758
## [99] 1758 1758 719 719 1198 457 402 402 392 356 356 322 297 303
## [113] 303 276 249 238 238 228 231 203 201 207 207 182 171 168
## [127] 140 147 137 138 126 126 109 109 109 104 97 77 77 69
## [141] 74 65 574 574 574 574 1086 1086 1086 1247 1781 1781 1781 1781
## [155] 1580 1580 1580 1580 1903 1096 2445 2445 2445 2445 975 2369 1811 1811
## [169] 1811 1811 1356 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [183] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [197] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [211] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [225] 3165 3165 1651 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562
## [239] 1562 2281 2281 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632
## [253] 1632 1140 1736 1736 1736 846 846 937 1485 891 1323 1023 1023 757
## [267] 533 336 429 462 557 557 661 676 794 794 794 794 1215 1215
## [281] 1215 876 609 609 1406 1406 1406 1247 1247 1247 563 563 563 563
## [295] 1431 1431 1431 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057
## [309] 3057 3414 3414 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593
## [323] 3159 3159 3159 3159 3102 3102 3102 2166 2166 2166 649 575 575 797
## [337] 524 582 167 167 167 139 149 197 211 139 118 118 118 108
## [351] 108 108 297 234 156 156 324 147 127 154 154 154 154 322
## [365] 594 648 648 700 1094 505 505 505 505 505 505 505 505 690
## [379] 690 690 690 690 690 690 690 1522 1522 2581 2581 2996 2996 2996
## [393] 2979 2979 2979 2979 3593 3593 3220 201 148 148 187 187 187 187
## [407] 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 8 9 10 11 12 13 14
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 15 16 17 18 19 20 21
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 22 23 24 25 26 27 28
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 29 30 31 32 33 34 35
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 36 37 38 39 40 41 42
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 43 44 45 46 47 48 49
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 50 51 52 53 54 55 56
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 57 58 59 60 61 62 63
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1157.3607 1157.3607
## 64 65 66 67 68 69 70
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 71 72 73 82 83 84 85
## 1157.3607 1157.3607 1157.3607 1591.1917 1591.1917 1591.1917 1591.1917
## 86 87 88 89 90 91 92
## 1591.1917 1591.1917 1591.1917 1591.1917 940.4451 940.4451 940.4451
## 93 94 95 96 97 99 100
## 940.4451 940.4451 940.4451 940.4451 940.4451 1591.1917 1591.1917
## 101 102 103 104 105 106 107
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 108 109 110 111 112 113 114
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 115 116 117 118 119 120 121
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 122 123 124 125 126 127 128
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 129 130 131 132 133 134 135
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 136 137 138 139 140 141 142
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 143 144 145 146 147 148 149
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 150 151 156 157 158 159 160
## 1591.1917 1591.1917 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 161 162 163 164 165 166 167
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 168 169 170 171 172 173 174
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 175 176 177 178 179 180 181
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 182 183 184 185 186 187 188
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 189 190 191 192 193 194 195
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 196 197 198 199 200 201 202
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 203 204 205 206 207 208 209
## 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607 1157.3607
## 210 211 212 213 214 215 216
## 1157.3607 1157.3607 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 217 218 219 220 221 222 223
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 224 225 226 227 228 229 230
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 231 232 233 234 235 236 237
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 238 239 240 241 242 243 244
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 245 246 247 248 249 250 251
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 252 253 254 255 256 257 258
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 259 260 261 262 263 264 265
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 266 267 268 269 270 271 272
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 273 274 275 276 277 278 279
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 280 308 309 310 311 312 313
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 314 315 316 317 318 319 320
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 321 322 323 324 325 326 327
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 328 329 330 331 332 333 334
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 335 336 337 338 339 340 341
## 1591.1917 1591.1917 1591.1917 1591.1917 1374.2762 1374.2762 1374.2762
## 342 343 344 345 346 347 348
## 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762
## 349 350 351 352 353 354 355
## 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762
## 356 357 358 359 360 361 362
## 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762 1374.2762
## 363 364 365 366 367 368 369
## 1374.2762 1374.2762 1374.2762 1374.2762 1591.1917 1591.1917 1591.1917
## 370 371 372 373 374 375 376
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 377 378 379 380 381 382 383
## 1591.1917 1591.1917 940.4451 940.4451 940.4451 940.4451 940.4451
## 384 385 386 387 388 389 390
## 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451
## 391 392 393 394 395 396 397
## 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451
## 398 399 400 401 402 403 404
## 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451
## 405 406 407 408 409 410 411
## 940.4451 1374.2762 1374.2762 1374.2762 1374.2762 1591.1917 1591.1917
## 412 413 414 415 416 417 418
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 419 420 421 422 423 424 425
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 426 427 428 429 430 431 432
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 433 434 435 436 437 438 439
## 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917 1591.1917
## 440 441 442 443 444 445 446
## 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451
## 447 448 449 450 451 452 453
## 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451 940.4451
## 454 455 456 457 458
## 940.4451 940.4451 940.4451 940.4451 940.4451cor(INTL$PriceEconomy,INTL$WidthDifference)## [1] -0.2481919fit<-lm(PricePremium~WidthDifference,data = INTL)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthDifference, data = INTL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2066.7 -1042.7 -17.7 1043.3 5261.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2365.19 113.98 20.751 < 2e-16 ***
## WidthDifference -212.51 53.92 -3.941 9.5e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1241 on 416 degrees of freedom
## Multiple R-squared: 0.036, Adjusted R-squared: 0.03368
## F-statistic: 15.53 on 1 and 416 DF, p-value: 9.503e-05INTL$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 3128 3128 3128 3128 2856 2856 2856 2856 2409
## [71] 2409 2409 594 2982 2982 2982 2982 2549 2549 2549 2548 524 524 524
## [85] 524 616 616 616 616 3563 3563 3563 3563 3536 3536 3536 3536 2592
## [99] 2592 2592 1634 1634 1634 486 442 442 407 396 396 348 323 319
## [113] 319 306 285 278 276 263 247 238 237 237 234 211 201 198
## [127] 175 175 172 165 156 156 141 141 141 131 125 99 99 97
## [141] 97 86 1619 1619 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509
## [155] 3019 3019 3019 3019 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531
## [169] 2531 2531 1710 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [183] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [197] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [211] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [225] 3275 3275 3509 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099
## [239] 3099 3025 3025 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278
## [253] 2278 2049 1866 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912
## [267] 837 841 841 841 789 789 928 931 1671 1452 1452 1408 1947 1947
## [281] 1947 1356 900 900 1584 1584 1584 1407 1407 1407 619 619 619 619
## [295] 1564 1564 1564 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167
## [309] 3167 3524 3524 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702
## [323] 3243 3243 3243 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826
## [337] 797 797 483 483 483 398 398 520 534 318 267 267 267 228
## [351] 228 228 620 483 318 318 620 267 228 267 267 267 267 483
## [365] 696 1710 1710 1710 1710 1004 1004 1004 1004 1004 1004 1004 1004 1110
## [379] 1110 1110 1110 1110 1110 1110 1110 3289 3289 2781 2781 3196 3196 3196
## [393] 3088 3088 3088 3088 3702 3702 3289 545 397 397 430 430 430 430
## [407] 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 9 10 11 12 13 14 15 16
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 17 18 19 20 21 22 23 24
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 25 26 27 28 29 30 31 32
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 33 34 35 36 37 38 39 40
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 41 42 43 44 45 46 47 48
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 49 50 51 52 53 54 55 56
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 57 58 59 60 61 62 63 64
## 2152.681 2152.681 2152.681 2152.681 2152.681 1727.659 1727.659 1727.659
## 65 66 67 68 69 70 71 72
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659
## 73 82 83 84 85 86 87 88
## 1727.659 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 89 90 91 92 93 94 95 96
## 2152.681 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148
## 97 99 100 101 102 103 104 105
## 1515.148 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 106 107 108 109 110 111 112 113
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 114 115 116 117 118 119 120 121
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 122 123 124 125 126 127 128 129
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 130 131 132 133 134 135 136 137
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 138 139 140 141 142 143 144 145
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 146 147 148 149 150 151 156 157
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 1727.659 1727.659
## 158 159 160 161 162 163 164 165
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659
## 166 167 168 169 170 171 172 173
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659
## 174 175 176 177 178 179 180 181
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659
## 182 183 184 185 186 187 188 189
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659
## 190 191 192 193 194 195 196 197
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659
## 198 199 200 201 202 203 204 205
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659
## 206 207 208 209 210 211 212 213
## 1727.659 1727.659 1727.659 1727.659 1727.659 1727.659 2152.681 2152.681
## 214 215 216 217 218 219 220 221
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 222 223 224 225 226 227 228 229
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 230 231 232 233 234 235 236 237
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 238 239 240 241 242 243 244 245
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 246 247 248 249 250 251 252 253
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 254 255 256 257 258 259 260 261
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 262 263 264 265 266 267 268 269
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 270 271 272 273 274 275 276 277
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 278 279 280 308 309 310 311 312
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 313 314 315 316 317 318 319 320
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 321 322 323 324 325 326 327 328
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 329 330 331 332 333 334 335 336
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 337 338 339 340 341 342 343 344
## 2152.681 2152.681 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170
## 345 346 347 348 349 350 351 352
## 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170
## 353 354 355 356 357 358 359 360
## 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170
## 361 362 363 364 365 366 367 368
## 1940.170 1940.170 1940.170 1940.170 1940.170 1940.170 2152.681 2152.681
## 369 370 371 372 373 374 375 376
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 377 378 379 380 381 382 383 384
## 2152.681 2152.681 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148
## 385 386 387 388 389 390 391 392
## 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148
## 393 394 395 396 397 398 399 400
## 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148
## 401 402 403 404 405 406 407 408
## 1515.148 1515.148 1515.148 1515.148 1515.148 1940.170 1940.170 1940.170
## 409 410 411 412 413 414 415 416
## 1940.170 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 417 418 419 420 421 422 423 424
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 425 426 427 428 429 430 431 432
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681
## 433 434 435 436 437 438 439 440
## 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 2152.681 1515.148
## 441 442 443 444 445 446 447 448
## 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148
## 449 450 451 452 453 454 455 456
## 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148 1515.148
## 457 458
## 1515.148 1515.148cor(INTL$PricePremium,INTL$WidthDifference)## [1] -0.1897303Boeing VS AirBus
Boeing Analyse all about Boeing Aircrafts:-
Boeing <- airline[ which(airline$Aircraft=='Boeing'),]
View(Boeing)
summary(Boeing)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 38 AirBus: 0 Min. : 1.250 Aug:88
## British :128 Boeing:307 1st Qu.: 4.250 Jul:50
## Delta : 34 Median : 7.750 Oct:86
## Jet : 54 Mean : 7.648 Sep:83
## Singapore: 24 3rd Qu.:11.000
## Virgin : 29 Max. :14.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 34 Min. : 78 Min. : 8.00 Min. :30.00
## International:273 1st Qu.:124 1st Qu.:24.00 1st Qu.:31.00
## Median :174 Median :29.00 Median :31.00
## Mean :181 Mean :30.94 Mean :31.11
## 3rd Qu.:203 3rd Qu.:39.00 3rd Qu.:32.00
## Max. :389 Max. :66.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 65
## 1st Qu.:38.00 1st Qu.:17.00 1st Qu.:19.00 1st Qu.: 413
## Median :38.00 Median :18.00 Median :19.00 Median :1224
## Mean :37.93 Mean :17.73 Mean :19.44 Mean :1306
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:1812
## Max. :40.00 Max. :19.00 Max. :21.00 Max. :3593
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86.0 Min. :0.0300 Min. : 98 Min. : 2.000
## 1st Qu.: 539.5 1st Qu.:0.1200 1st Qu.:162 1st Qu.: 6.000
## Median :1710.0 Median :0.3800 Median :200 Median : 7.000
## Mean :1833.3 Mean :0.5228 Mean :212 Mean : 6.824
## 3rd Qu.:2993.0 3rd Qu.:0.7700 3rd Qu.:233 3rd Qu.: 7.000
## Max. :7414.0 Max. :1.8900 Max. :441 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :0.00 Min. : 4.71
## 1st Qu.:1.00 1st Qu.:12.28
## Median :1.00 Median :12.90
## Mean :1.71 Mean :15.04
## 3rd Qu.:3.00 3rd Qu.:17.80
## Max. :4.00 Max. :24.69Check the all the means now all Boeing aircrafts
mean(Boeing$PriceEconomy)## [1] 1305.987mean(Boeing$PricePremium)## [1] 1833.332mean(Boeing$FlightDuration)## [1] 7.64759mean(Boeing$PitchEconomy)## [1] 31.11075mean(Boeing$PitchPremium)## [1] 37.93485mean(Boeing$WidthEconomy)## [1] 17.72638mean(Boeing$WidthPremium)## [1] 19.43648mean(Boeing$PriceRelative)## [1] 0.5228339mean(Boeing$PitchDifference)## [1] 6.824104mean(Boeing$WidthDifference)## [1] 1.710098mean(Boeing$PriceEconomy)## [1] 1305.987mean(Boeing$PricePremium)## [1] 1833.332library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Boeing$PriceEconomy,Boeing$TravelMonth,mean))
y2<-c(by(Boeing$PricePremium,Boeing$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')mean(Boeing$PriceEconomy)## [1] 1305.987mean(Boeing$PricePremium)## [1] 1833.332library(plotly)
x<-c('British','Virgin','Delta','Jet','AirFrance','Singapore')
y1<-c(by(Boeing$PriceEconomy,Boeing$Airline,mean))
y2<-c(by(Boeing$PricePremium,Boeing$Airline,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Aircrafts", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1599.9 -466.0 -142.3 480.8 1896.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102.60 104.75 0.98 0.328
## FlightDuration 157.35 12.36 12.73 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 791.2 on 305 degrees of freedom
## Multiple R-squared: 0.347, Adjusted R-squared: 0.3449
## F-statistic: 162.1 on 1 and 305 DF, p-value: < 2.2e-16Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 2030.1986 2030.1986 2030.1986 2030.1986 1386.6172 1386.6172 1386.6172
## 8 9 10 11 12 13 14
## 1125.4082 1125.4082 1912.1825 1912.1825 1912.1825 1912.1825 1924.7708
## 15 16 17 18 19 20 21
## 1924.7708 1924.7708 1543.9721 1543.9721 1543.9721 1164.7469 1164.7469
## 22 23 24 25 26 27 28
## 1164.7469 1150.5850 1150.5850 1150.5850 1479.4566 1479.4566 1479.4566
## 29 30 31 32 33 34 35
## 875.2140 875.2140 875.2140 705.2707 705.2707 705.2707 705.2707
## 36 37 38 39 40 41 42
## 2226.8922 2226.8922 2226.8922 705.2707 705.2707 705.2707 705.2707
## 43 44 45 46 47 48 49
## 953.8914 953.8914 953.8914 1400.7792 1400.7792 1400.7792 2108.8760
## 50 51 52 53 54 55 56
## 2108.8760 2108.8760 1125.4082 1846.0934 1846.0934 1846.0934 1059.3191
## 57 58 59 60 61 74 75
## 1059.3191 1059.3191 2069.5373 1998.7276 2069.5373 426.7526 469.2384
## 76 77 78 79 80 81 82
## 426.7526 418.8849 469.2384 377.9726 464.5178 464.5178 1177.3353
## 83 84 85 86 87 88 89
## 1177.3353 1177.3353 1177.3353 1295.3514 1295.3514 1295.3514 1295.3514
## 90 91 92 93 94 95 96
## 587.2546 587.2546 587.2546 587.2546 587.2546 587.2546 587.2546
## 97 98 138 144 147 148 149
## 587.2546 772.9333 299.2952 299.2952 311.8836 311.8836 299.2952
## 151 152 153 154 155 156 157
## 311.8836 783.9481 812.2720 783.9481 812.2720 1872.8437 1872.8437
## 158 159 160 161 162 163 164
## 1872.8437 1872.8437 2003.4483 2003.4483 2003.4483 2003.4483 1661.9882
## 165 166 167 168 169 170 171
## 1661.9882 1661.9882 1661.9882 1806.7547 1806.7547 1806.7547 1806.7547
## 172 173 174 175 176 177 178
## 1740.6657 2082.1257 1794.1663 1794.1663 1794.1663 1794.1663 2082.1257
## 179 180 181 182 183 184 240
## 1885.4321 1307.9398 1307.9398 1307.9398 1307.9398 2082.1257 1740.6657
## 241 242 243 244 245 246 247
## 1740.6657 1740.6657 1833.5050 1833.5050 1833.5050 1661.9882 1661.9882
## 248 249 250 251 252 253 254
## 1661.9882 1661.9882 1452.7063 1452.7063 1452.7063 1898.0205 1898.0205
## 255 256 257 258 259 260 261
## 1898.0205 1898.0205 1570.7224 1570.7224 1570.7224 1504.6334 1465.2947
## 262 263 264 265 266 267 268
## 1465.2947 1465.2947 1243.4243 1243.4243 1243.4243 1504.6334 1216.6740
## 269 270 271 272 273 274 275
## 1216.6740 1216.6740 1898.0205 1898.0205 1898.0205 1898.0205 1504.6334
## 276 277 278 279 280 281 282
## 1846.0934 1846.0934 1846.0934 1216.6740 1846.0934 776.0804 771.3598
## 283 284 285 286 287 288 289
## 831.1546 834.3017 835.8752 842.1694 842.1694 842.1694 788.6688
## 290 291 292 293 294 295 299
## 771.3598 799.6836 842.1694 772.9333 794.9630 403.1494 503.8565
## 300 301 304 305 307 315 316
## 831.1546 552.6365 349.6487 495.9888 349.6487 2291.4077 2291.4077
## 317 318 319 320 321 322 323
## 2291.4077 2291.4077 2055.3754 2055.3754 2055.3754 1806.7547 1806.7547
## 324 325 326 327 328 329 330
## 1806.7547 2409.4238 2409.4238 2409.4238 1622.6495 1622.6495 1622.6495
## 331 332 333 334 335 336 337
## 705.2707 705.2707 705.2707 705.2707 2108.8760 2108.8760 2108.8760
## 338 339 340 341 342 343 344
## 2108.8760 1413.3676 1413.3676 1413.3676 1282.7630 1177.3353 1479.4566
## 345 346 347 348 349 350 351
## 1479.4566 1479.4566 1307.9398 1177.3353 1177.3353 1597.4727 1597.4727
## 352 353 354 355 356 357 358
## 1597.4727 1597.4727 1322.1017 1322.1017 1322.1017 1334.6901 1583.3108
## 359 360 361 362 363 364 365
## 1583.3108 1583.3108 1951.5212 1951.5212 1976.6979 1976.6979 1976.6979
## 366 367 368 369 370 371 372
## 1976.6979 2278.8193 2278.8193 2278.8193 2200.1418 2200.1418 2200.1418
## 373 374 375 376 377 378 379
## 1504.6334 1504.6334 1504.6334 1610.0611 1610.0611 1610.0611 614.0049
## 380 381 382 383 384 385 386
## 614.0049 614.0049 614.0049 614.0049 757.1978 757.1978 744.6094
## 387 388 389 390 391 392 393
## 495.9888 495.9888 495.9888 521.1655 521.1655 521.1655 757.1978
## 394 395 396 397 398 399 400
## 614.0049 744.6094 757.1978 757.1978 495.9888 521.1655 783.9481
## 401 402 403 404 405 406 407
## 783.9481 783.9481 783.9481 614.0049 614.0049 1189.9237 1189.9237
## 408 409 430 431 432 436 437
## 1189.9237 1189.9237 1780.0044 1780.0044 1780.0044 1440.1179 1912.1825
## 438 440 441 442 443 444 445
## 1912.1825 993.2301 599.8430 599.8430 993.2301 993.2301 993.2301
## 446 447 448 449 450 451 452
## 993.2301 993.2301 599.8430 508.5771 508.5771 508.5771 599.8430
## 453 454 455 456 457 458
## 508.5771 508.5771 508.5771 508.5771 614.0049 508.5771cor(Boeing$PriceEconomy,Boeing$FlightDuration)## [1] 0.589106fit<-lm(PriceEconomy~SeatsEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1983.5 -705.8 -109.0 596.6 2329.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 203.919 149.622 1.363 0.174
## SeatsEconomy 6.088 0.777 7.835 7.88e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 893.3 on 305 degrees of freedom
## Multiple R-squared: 0.1675, Adjusted R-squared: 0.1648
## F-statistic: 61.38 on 1 and 305 DF, p-value: 7.881e-14Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 8 9 10 11 12 13 14
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 15 16 17 18 19 20 21
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 22 23 24 25 26 27 28
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 29 30 31 32 33 34 35
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 36 37 38 39 40 41 42
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 43 44 45 46 47 48 49
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 50 51 52 53 54 55 56
## 946.6157 946.6157 977.0541 977.0541 977.0541 977.0541 977.0541
## 57 58 59 60 61 74 75
## 977.0541 977.0541 977.0541 977.0541 977.0541 678.7578 678.7578
## 76 77 78 79 80 81 82
## 678.7578 678.7578 678.7578 678.7578 678.7578 678.7578 1683.2247
## 83 84 85 86 87 88 89
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 90 91 92 93 94 95 96
## 1044.0185 1044.0185 1044.0185 1044.0185 1044.0185 1044.0185 1044.0185
## 97 98 138 144 147 148 149
## 1044.0185 1007.4924 2048.4854 2048.4854 2048.4854 2048.4854 2048.4854
## 151 152 153 154 155 156 157
## 2048.4854 1244.9119 1244.9119 1244.9119 1244.9119 1409.2792 1409.2792
## 158 159 160 161 162 163 164
## 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 2486.7982
## 165 166 167 168 169 170 171
## 2486.7982 2486.7982 2486.7982 1409.2792 1409.2792 1409.2792 1409.2792
## 172 173 174 175 176 177 178
## 1409.2792 1409.2792 2486.7982 2486.7982 2486.7982 2486.7982 1409.2792
## 179 180 181 182 183 184 240
## 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 1683.2247
## 241 242 243 244 245 246 247
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 248 249 250 251 252 253 254
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 255 256 257 258 259 260 261
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 262 263 264 265 266 267 268
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 269 270 271 272 273 274 275
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 276 277 278 279 280 281 282
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 970.9664 970.9664
## 283 284 285 286 287 288 289
## 970.9664 970.9664 970.9664 1050.1062 1050.1062 1050.1062 970.9664
## 290 291 292 293 294 295 299
## 970.9664 970.9664 1050.1062 970.9664 970.9664 1031.8432 1031.8432
## 300 301 304 305 307 315 316
## 970.9664 1031.8432 970.9664 1031.8432 970.9664 1324.0517 1324.0517
## 317 318 319 320 321 322 323
## 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517
## 324 325 326 327 328 329 330
## 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517
## 331 332 333 334 335 336 337
## 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517
## 338 339 340 341 342 343 344
## 1324.0517 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546
## 345 346 347 348 349 350 351
## 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546
## 352 353 354 355 356 357 358
## 1421.4546 1421.4546 1263.1749 1263.1749 1263.1749 1263.1749 1421.4546
## 359 360 361 362 363 364 365
## 1421.4546 1421.4546 1263.1749 1263.1749 1421.4546 1421.4546 1421.4546
## 366 367 368 369 370 371 372
## 1421.4546 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176
## 373 374 375 376 377 378 379
## 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176 958.7910
## 380 381 382 383 384 385 386
## 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910
## 387 388 389 390 391 392 393
## 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910
## 394 395 396 397 398 399 400
## 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910
## 401 402 403 404 405 406 407
## 958.7910 958.7910 958.7910 958.7910 958.7910 1518.8574 1518.8574
## 408 409 430 431 432 436 437
## 1518.8574 1518.8574 2572.0257 2572.0257 2572.0257 2572.0257 2572.0257
## 438 440 441 442 443 444 445
## 2572.0257 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228
## 446 447 448 449 450 451 452
## 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228
## 453 454 455 456 457 458
## 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228cor(Boeing$PriceEconomy,Boeing$SeatsEconomy)## [1] 0.4093083fit<-lm(PriceEconomy~PriceRelative,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1443.5 -797.1 -111.1 587.5 2388.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1662.97 78.21 21.263 < 2e-16 ***
## PriceRelative -682.79 110.55 -6.176 2.1e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 923.1 on 305 degrees of freedom
## Multiple R-squared: 0.1112, Adjusted R-squared: 0.1082
## F-statistic: 38.14 on 1 and 305 DF, p-value: 2.096e-09Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1403.5124 1403.5124 1403.5124 1403.5124 1205.5035 1205.5035 1205.5035
## 8 9 10 11 12 13 14
## 959.6994 959.6994 1150.8804 1150.8804 1280.6103 1485.4471 1307.9219
## 15 16 17 18 19 20 21
## 1307.9219 1307.9219 1403.5124 1403.5124 1403.5124 1430.8240 1430.8240
## 22 23 24 25 26 27 28
## 1430.8240 1437.6519 1437.6519 1437.6519 1423.9961 1437.6519 1437.6519
## 29 30 31 32 33 34 35
## 1430.8240 1430.8240 1430.8240 1376.2008 1376.2008 1376.2008 1376.2008
## 36 37 38 39 40 41 42
## 1219.1593 1219.1593 1219.1593 1499.1029 1499.1029 1499.1029 1499.1029
## 43 44 45 46 47 48 49
## 1546.8981 1546.8981 1546.8981 1608.3492 1608.3492 1608.3492 1307.9219
## 50 51 52 53 54 55 56
## 1307.9219 1307.9219 959.6994 1417.1682 1417.1682 1417.1682 1430.8240
## 57 58 59 60 61 74 75
## 1430.8240 1430.8240 1519.5866 1519.5866 1246.4709 1601.5213 1608.3492
## 76 77 78 79 80 81 82
## 1615.1771 1615.1771 1615.1771 1635.6607 1635.6607 1642.4886 932.3878
## 83 84 85 86 87 88 89
## 932.3878 932.3878 932.3878 1389.8566 1389.8566 1389.8566 1389.8566
## 90 91 92 93 94 95 96
## 1335.2335 1335.2335 1335.2335 1335.2335 1437.6519 1437.6519 1437.6519
## 97 98 138 144 147 148 149
## 1485.4471 1601.5213 1485.4471 1458.1355 1464.9634 1464.9634 1389.8566
## 151 152 153 154 155 156 157
## 1437.6519 1574.2097 1594.6934 1601.5213 1622.0049 420.2959 420.2959
## 158 159 160 161 162 163 164
## 420.2959 420.2959 481.7470 481.7470 481.7470 720.7232 1000.6668
## 165 166 167 168 169 170 171
## 1000.6668 1000.6668 1000.6668 1041.6341 1041.6341 1041.6341 1041.6341
## 172 173 174 175 176 177 178
## 1089.4294 1280.6103 1314.7498 1314.7498 1314.7498 1314.7498 1321.5777
## 179 180 181 182 183 184 240
## 1328.4056 1389.8566 1389.8566 1389.8566 1389.8566 1485.4471 891.4205
## 241 242 243 244 245 246 247
## 891.4205 1485.4471 1355.7172 1355.7172 1355.7172 1417.1682 1417.1682
## 248 249 250 251 252 253 254
## 1417.1682 1417.1682 993.8389 993.8389 993.8389 1437.6519 1437.6519
## 255 256 257 258 259 260 261
## 1437.6519 1437.6519 1417.1682 1417.1682 1417.1682 891.4205 1376.2008
## 262 263 264 265 266 267 268
## 1376.2008 1376.2008 1389.8566 1389.8566 1389.8566 1116.7409 1615.1771
## 269 270 271 272 273 274 275
## 1615.1771 1615.1771 905.0763 905.0763 1041.6341 1526.4145 1116.7409
## 276 277 278 279 280 281 282
## 1546.8981 1546.8981 1546.8981 1519.5866 1273.7825 1567.3818 1567.3818
## 283 284 285 286 287 288 289
## 1581.0376 1581.0376 1581.0376 1587.8655 1587.8655 1587.8655 1587.8655
## 290 291 292 293 294 295 299
## 1587.8655 1587.8655 1594.6934 1594.6934 1594.6934 1601.5213 1608.3492
## 300 301 304 305 307 315 316
## 1615.1771 1615.1771 1628.8328 1635.6607 1635.6607 905.0763 1096.2573
## 317 318 319 320 321 322 323
## 1096.2573 1137.2246 1253.2988 1253.2988 1253.2988 1287.4382 1335.2335
## 324 325 326 327 328 329 330
## 1335.2335 1574.2097 1574.2097 1574.2097 1574.2097 1574.2097 1574.2097
## 331 332 333 334 335 336 337
## 1594.6934 1594.6934 1594.6934 1594.6934 1601.5213 1601.5213 1601.5213
## 338 339 340 341 342 343 344
## 1601.5213 1417.1682 1417.1682 1417.1682 1608.3492 1615.1771 1615.1771
## 345 346 347 348 349 350 351
## 1615.1771 1615.1771 1635.6607 1635.6607 1635.6607 1642.4886 1642.4886
## 352 353 354 355 356 357 358
## 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886
## 359 360 361 362 363 364 365
## 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886
## 366 367 368 369 370 371 372
## 1642.4886 713.8953 713.8953 713.8953 1567.3818 1567.3818 1567.3818
## 373 374 375 376 377 378 379
## 1137.2246 1335.2335 1335.2335 1635.6607 1307.9219 1410.3403 372.5007
## 380 381 382 383 384 385 386
## 372.5007 372.5007 386.1565 522.7143 543.1980 618.3048 782.1742
## 387 388 389 390 391 392 393
## 802.6579 802.6579 802.6579 905.0763 905.0763 905.0763 918.7321
## 394 395 396 397 398 399 400
## 939.2157 952.8715 952.8715 1041.6341 1109.9130 1123.5688 1157.7083
## 401 402 403 404 405 406 407
## 1157.7083 1157.7083 1157.7083 1321.5777 1546.8981 543.1980 543.1980
## 408 409 430 431 432 436 437
## 679.7558 1280.6103 1615.1771 1615.1771 1615.1771 1635.6607 1642.4886
## 438 440 441 442 443 444 445
## 1642.4886 495.4028 515.8864 515.8864 775.3463 775.3463 775.3463
## 446 447 448 449 450 451 452
## 775.3463 829.9695 932.3878 1137.2246 1137.2246 1137.2246 1219.1593
## 453 454 455 456 457 458
## 1253.2988 1266.9546 1355.7172 1355.7172 1403.5124 1581.0376cor(Boeing$PriceEconomy,Boeing$PriceRelative)## [1] -0.3334061fit<-lm(PriceEconomy~PercentPremiumSeats,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1257.1 -858.1 -209.6 475.0 2411.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1000.24 160.88 6.217 1.66e-09 ***
## PercentPremiumSeats 20.33 10.04 2.025 0.0437 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 972.6 on 305 degrees of freedom
## Multiple R-squared: 0.01326, Adjusted R-squared: 0.01003
## F-statistic: 4.1 on 1 and 305 DF, p-value: 0.04375Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 9 10 11 12 13 14 15 16
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 17 18 19 20 21 22 23 24
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 25 26 27 28 29 30 31 32
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 33 34 35 36 37 38 39 40
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 41 42 43 44 45 46 47 48
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 49 50 51 52 53 54 55 56
## 1502.147 1502.147 1502.147 1477.752 1477.752 1477.752 1477.752 1477.752
## 57 58 59 60 61 74 75 76
## 1477.752 1477.752 1477.752 1477.752 1477.752 1415.141 1415.141 1415.141
## 77 78 79 80 81 82 83 84
## 1415.141 1415.141 1415.141 1415.141 1415.141 1380.989 1380.989 1380.989
## 85 86 87 88 89 90 91 92
## 1380.989 1380.989 1380.989 1380.989 1380.989 1343.178 1343.178 1343.178
## 93 94 95 96 97 98 138 144
## 1343.178 1343.178 1343.178 1343.178 1343.178 1334.843 1312.482 1312.482
## 147 148 149 151 152 153 154 155
## 1312.482 1312.482 1312.482 1312.482 1295.000 1295.000 1295.000 1295.000
## 156 157 158 159 160 161 162 163
## 1305.570 1305.570 1305.570 1305.570 1305.570 1305.570 1305.570 1305.570
## 164 165 166 167 168 169 170 171
## 1304.554 1304.554 1304.554 1304.554 1305.570 1305.570 1305.570 1305.570
## 172 173 174 175 176 177 178 179
## 1305.570 1305.570 1304.554 1304.554 1304.554 1304.554 1305.570 1305.570
## 180 181 182 183 184 240 241 242
## 1305.570 1305.570 1305.570 1305.570 1305.570 1262.474 1262.474 1262.474
## 243 244 245 246 247 248 249 250
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 251 252 253 254 255 256 257 258
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 259 260 261 262 263 264 265 266
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 267 268 269 270 271 272 273 274
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 275 276 277 278 279 280 281 282
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1254.343 1254.343
## 283 284 285 286 287 288 289 290
## 1254.343 1254.343 1254.343 1267.150 1267.150 1267.150 1254.343 1254.343
## 291 292 293 294 295 299 300 301
## 1254.343 1267.150 1254.343 1254.343 1260.848 1260.848 1254.343 1260.848
## 304 305 307 315 316 317 318 319
## 1254.343 1260.848 1254.343 1268.776 1268.776 1268.776 1268.776 1268.776
## 320 321 322 323 324 325 326 327
## 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776
## 328 329 330 331 332 333 334 335
## 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776
## 336 337 338 339 340 341 342 343
## 1268.776 1268.776 1268.776 1249.871 1249.871 1249.871 1249.871 1249.871
## 344 345 346 347 348 349 350 351
## 1249.871 1249.871 1249.871 1249.871 1249.871 1249.871 1249.871 1249.871
## 352 353 354 355 356 357 358 359
## 1249.871 1249.871 1246.618 1246.618 1246.618 1246.618 1249.871 1249.871
## 360 361 362 363 364 365 366 367
## 1249.871 1246.618 1246.618 1249.871 1249.871 1249.871 1249.871 1215.109
## 368 369 370 371 372 373 374 375
## 1215.109 1215.109 1215.109 1215.109 1215.109 1215.109 1215.109 1215.109
## 376 377 378 379 380 381 382 383
## 1215.109 1215.109 1215.109 1232.591 1232.591 1232.591 1232.591 1232.591
## 384 385 386 387 388 389 390 391
## 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591
## 392 393 394 395 396 397 398 399
## 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591
## 400 401 402 403 404 405 406 407
## 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1203.522 1203.522
## 408 409 430 431 432 436 437 438
## 1203.522 1203.522 1181.160 1181.160 1181.160 1181.160 1181.160 1181.160
## 440 441 442 443 444 445 446 447
## 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984
## 448 449 450 451 452 453 454 455
## 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984
## 456 457 458
## 1095.984 1095.984 1095.984cor(Boeing$PriceEconomy,Boeing$PercentPremiumSeats)## [1] 0.1151734fit<-lm(PricePremium~FlightDuration,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2251.2 -688.4 -95.2 694.8 4137.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.39 131.28 0.369 0.713
## FlightDuration 233.40 15.49 15.068 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 991.6 on 305 degrees of freedom
## Multiple R-squared: 0.4268, Adjusted R-squared: 0.4249
## F-statistic: 227.1 on 1 and 305 DF, p-value: < 2.2e-16Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 2907.5299 2907.5299 2907.5299 2907.5299 1952.9283 1952.9283 1952.9283
## 8 9 10 11 12 13 14
## 1565.4861 1565.4861 2732.4807 2732.4807 2732.4807 2732.4807 2751.1526
## 15 16 17 18 19 20 21
## 2751.1526 2751.1526 2186.3272 2186.3272 2186.3272 1623.8358 1623.8358
## 22 23 24 25 26 27 28
## 1623.8358 1602.8299 1602.8299 1602.8299 2090.6337 2090.6337 2090.6337
## 29 30 31 32 33 34 35
## 1194.3818 1194.3818 1194.3818 942.3109 942.3109 942.3109 942.3109
## 36 37 38 39 40 41 42
## 3199.2786 3199.2786 3199.2786 942.3109 942.3109 942.3109 942.3109
## 43 44 45 46 47 48 49
## 1311.0812 1311.0812 1311.0812 1973.9342 1973.9342 1973.9342 3024.2294
## 50 51 52 53 54 55 56
## 3024.2294 3024.2294 1565.4861 2634.4532 2634.4532 2634.4532 1467.4585
## 57 58 59 60 61 74 75
## 1467.4585 1467.4585 2965.8796 2860.8501 2965.8796 529.1948 592.2125
## 76 77 78 79 80 81 82
## 529.1948 517.5249 592.2125 456.8412 585.2106 585.2106 1642.5077
## 83 84 85 86 87 88 89
## 1642.5077 1642.5077 1642.5077 1817.5569 1817.5569 1817.5569 1817.5569
## 90 91 92 93 94 95 96
## 767.2617 767.2617 767.2617 767.2617 767.2617 767.2617 767.2617
## 97 98 138 144 147 148 149
## 767.2617 1042.6725 340.1417 340.1417 358.8136 358.8136 340.1417
## 151 152 153 154 155 156 157
## 358.8136 1059.0104 1101.0222 1059.0104 1101.0222 2674.1310 2674.1310
## 158 159 160 161 162 163 164
## 2674.1310 2674.1310 2867.8521 2867.8521 2867.8521 2867.8521 2361.3764
## 165 166 167 168 169 170 171
## 2361.3764 2361.3764 2361.3764 2576.1034 2576.1034 2576.1034 2576.1034
## 172 173 174 175 176 177 178
## 2478.0759 2984.5516 2557.4315 2557.4315 2557.4315 2557.4315 2984.5516
## 179 180 181 182 183 184 240
## 2692.8029 1836.2288 1836.2288 1836.2288 1836.2288 2984.5516 2478.0759
## 241 242 243 244 245 246 247
## 2478.0759 2478.0759 2615.7812 2615.7812 2615.7812 2361.3764 2361.3764
## 248 249 250 251 252 253 254
## 2361.3764 2361.3764 2050.9558 2050.9558 2050.9558 2711.4748 2711.4748
## 255 256 257 258 259 260 261
## 2711.4748 2711.4748 2226.0050 2226.0050 2226.0050 2127.9775 2069.6278
## 262 263 264 265 266 267 268
## 2069.6278 2069.6278 1740.5353 1740.5353 1740.5353 2127.9775 1700.8574
## 269 270 271 272 273 274 275
## 1700.8574 1700.8574 2711.4748 2711.4748 2711.4748 2711.4748 2127.9775
## 276 277 278 279 280 281 282
## 2634.4532 2634.4532 2634.4532 1700.8574 2634.4532 1047.3405 1040.3385
## 283 284 285 286 287 288 289
## 1129.0301 1133.6981 1136.0320 1145.3680 1145.3680 1145.3680 1066.0124
## 290 291 292 293 294 295 299
## 1040.3385 1082.3503 1145.3680 1042.6725 1075.3483 494.1850 643.5603
## 300 301 304 305 307 315 316
## 1129.0301 715.9140 414.8294 631.8904 414.8294 3294.9721 3294.9721
## 317 318 319 320 321 322 323
## 3294.9721 3294.9721 2944.8737 2944.8737 2944.8737 2576.1034 2576.1034
## 324 325 326 327 328 329 330
## 2576.1034 3470.0213 3470.0213 3470.0213 2303.0267 2303.0267 2303.0267
## 331 332 333 334 335 336 337
## 942.3109 942.3109 942.3109 942.3109 3024.2294 3024.2294 3024.2294
## 338 339 340 341 342 343 344
## 3024.2294 1992.6061 1992.6061 1992.6061 1798.8850 1642.5077 2090.6337
## 345 346 347 348 349 350 351
## 2090.6337 2090.6337 1836.2288 1642.5077 1642.5077 2265.6829 2265.6829
## 352 353 354 355 356 357 358
## 2265.6829 2265.6829 1857.2347 1857.2347 1857.2347 1875.9066 2244.6770
## 359 360 361 362 363 364 365
## 2244.6770 2244.6770 2790.8304 2790.8304 2828.1743 2828.1743 2828.1743
## 366 367 368 369 370 371 372
## 2828.1743 3276.3002 3276.3002 3276.3002 3159.6007 3159.6007 3159.6007
## 373 374 375 376 377 378 379
## 2127.9775 2127.9775 2127.9775 2284.3548 2284.3548 2284.3548 806.9396
## 380 381 382 383 384 385 386
## 806.9396 806.9396 806.9396 806.9396 1019.3326 1019.3326 1000.6607
## 387 388 389 390 391 392 393
## 631.8904 631.8904 631.8904 669.2342 669.2342 669.2342 1019.3326
## 394 395 396 397 398 399 400
## 806.9396 1000.6607 1019.3326 1019.3326 631.8904 669.2342 1059.0104
## 401 402 403 404 405 406 407
## 1059.0104 1059.0104 1059.0104 806.9396 806.9396 1661.1796 1661.1796
## 408 409 430 431 432 436 437
## 1661.1796 1661.1796 2536.4256 2536.4256 2536.4256 2032.2839 2732.4807
## 438 440 441 442 443 444 445
## 2732.4807 1369.4310 785.9337 785.9337 1369.4310 1369.4310 1369.4310
## 446 447 448 449 450 451 452
## 1369.4310 1369.4310 785.9337 650.5623 650.5623 650.5623 785.9337
## 453 454 455 456 457 458
## 650.5623 650.5623 650.5623 650.5623 806.9396 650.5623cor(Boeing$PricePremium,Boeing$FlightDuration)## [1] 0.6532631fit<-lm(PriceEconomy~SeatsEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1983.5 -705.8 -109.0 596.6 2329.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 203.919 149.622 1.363 0.174
## SeatsEconomy 6.088 0.777 7.835 7.88e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 893.3 on 305 degrees of freedom
## Multiple R-squared: 0.1675, Adjusted R-squared: 0.1648
## F-statistic: 61.38 on 1 and 305 DF, p-value: 7.881e-14Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 8 9 10 11 12 13 14
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 15 16 17 18 19 20 21
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 22 23 24 25 26 27 28
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 29 30 31 32 33 34 35
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 36 37 38 39 40 41 42
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 43 44 45 46 47 48 49
## 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157 946.6157
## 50 51 52 53 54 55 56
## 946.6157 946.6157 977.0541 977.0541 977.0541 977.0541 977.0541
## 57 58 59 60 61 74 75
## 977.0541 977.0541 977.0541 977.0541 977.0541 678.7578 678.7578
## 76 77 78 79 80 81 82
## 678.7578 678.7578 678.7578 678.7578 678.7578 678.7578 1683.2247
## 83 84 85 86 87 88 89
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 90 91 92 93 94 95 96
## 1044.0185 1044.0185 1044.0185 1044.0185 1044.0185 1044.0185 1044.0185
## 97 98 138 144 147 148 149
## 1044.0185 1007.4924 2048.4854 2048.4854 2048.4854 2048.4854 2048.4854
## 151 152 153 154 155 156 157
## 2048.4854 1244.9119 1244.9119 1244.9119 1244.9119 1409.2792 1409.2792
## 158 159 160 161 162 163 164
## 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 2486.7982
## 165 166 167 168 169 170 171
## 2486.7982 2486.7982 2486.7982 1409.2792 1409.2792 1409.2792 1409.2792
## 172 173 174 175 176 177 178
## 1409.2792 1409.2792 2486.7982 2486.7982 2486.7982 2486.7982 1409.2792
## 179 180 181 182 183 184 240
## 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 1409.2792 1683.2247
## 241 242 243 244 245 246 247
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 248 249 250 251 252 253 254
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 255 256 257 258 259 260 261
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 262 263 264 265 266 267 268
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 269 270 271 272 273 274 275
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247
## 276 277 278 279 280 281 282
## 1683.2247 1683.2247 1683.2247 1683.2247 1683.2247 970.9664 970.9664
## 283 284 285 286 287 288 289
## 970.9664 970.9664 970.9664 1050.1062 1050.1062 1050.1062 970.9664
## 290 291 292 293 294 295 299
## 970.9664 970.9664 1050.1062 970.9664 970.9664 1031.8432 1031.8432
## 300 301 304 305 307 315 316
## 970.9664 1031.8432 970.9664 1031.8432 970.9664 1324.0517 1324.0517
## 317 318 319 320 321 322 323
## 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517
## 324 325 326 327 328 329 330
## 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517
## 331 332 333 334 335 336 337
## 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517 1324.0517
## 338 339 340 341 342 343 344
## 1324.0517 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546
## 345 346 347 348 349 350 351
## 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546 1421.4546
## 352 353 354 355 356 357 358
## 1421.4546 1421.4546 1263.1749 1263.1749 1263.1749 1263.1749 1421.4546
## 359 360 361 362 363 364 365
## 1421.4546 1421.4546 1263.1749 1263.1749 1421.4546 1421.4546 1421.4546
## 366 367 368 369 370 371 372
## 1421.4546 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176
## 373 374 375 376 377 378 379
## 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176 1439.7176 958.7910
## 380 381 382 383 384 385 386
## 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910
## 387 388 389 390 391 392 393
## 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910
## 394 395 396 397 398 399 400
## 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910 958.7910
## 401 402 403 404 405 406 407
## 958.7910 958.7910 958.7910 958.7910 958.7910 1518.8574 1518.8574
## 408 409 430 431 432 436 437
## 1518.8574 1518.8574 2572.0257 2572.0257 2572.0257 2572.0257 2572.0257
## 438 440 441 442 443 444 445
## 2572.0257 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228
## 446 447 448 449 450 451 452
## 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228
## 453 454 455 456 457 458
## 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228 1190.1228cor(Boeing$PricePremium,Boeing$SeatsEconomy)## [1] 0.4317098fit<-lm(PriceEconomy~SeatsPremium,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1995.4 -610.6 -324.6 217.2 2504.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 335.801 138.934 2.417 0.0162 *
## SeatsPremium 31.356 4.173 7.515 6.38e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 899.4 on 305 degrees of freedom
## Multiple R-squared: 0.1562, Adjusted R-squared: 0.1535
## F-statistic: 56.47 on 1 and 305 DF, p-value: 6.377e-13Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260
## 8 9 10 11 12 13 14
## 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260
## 15 16 17 18 19 20 21
## 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260
## 22 23 24 25 26 27 28
## 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260
## 29 30 31 32 33 34 35
## 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260
## 36 37 38 39 40 41 42
## 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260
## 43 44 45 46 47 48 49
## 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260 1590.0260
## 50 51 52 53 54 55 56
## 1590.0260 1590.0260 1558.6704 1558.6704 1558.6704 1558.6704 1558.6704
## 57 58 59 60 61 74 75
## 1558.6704 1558.6704 1558.6704 1558.6704 1558.6704 962.9136 962.9136
## 76 77 78 79 80 81 82
## 962.9136 962.9136 962.9136 962.9136 962.9136 962.9136 2091.7159
## 83 84 85 86 87 88 89
## 2091.7159 2091.7159 2091.7159 2091.7159 2091.7159 2091.7159 2091.7159
## 90 91 92 93 94 95 96
## 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585
## 97 98 138 144 147 148 149
## 1213.7585 1151.0473 2060.3603 2060.3603 2060.3603 2060.3603 2060.3603
## 151 152 153 154 155 156 157
## 2060.3603 1245.1142 1245.1142 1245.1142 1245.1142 1433.2479 1433.2479
## 158 159 160 161 162 163 164
## 1433.2479 1433.2479 1433.2479 1433.2479 1433.2479 1433.2479 2405.2721
## 165 166 167 168 169 170 171
## 2405.2721 2405.2721 2405.2721 1433.2479 1433.2479 1433.2479 1433.2479
## 172 173 174 175 176 177 178
## 1433.2479 1433.2479 2405.2721 2405.2721 2405.2721 2405.2721 1433.2479
## 179 180 181 182 183 184 240
## 1433.2479 1433.2479 1433.2479 1433.2479 1433.2479 1433.2479 1464.6035
## 241 242 243 244 245 246 247
## 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035
## 248 249 250 251 252 253 254
## 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035
## 255 256 257 258 259 260 261
## 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035
## 262 263 264 265 266 267 268
## 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035
## 269 270 271 272 273 274 275
## 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035
## 276 277 278 279 280 281 282
## 1464.6035 1464.6035 1464.6035 1464.6035 1464.6035 900.2023 900.2023
## 283 284 285 286 287 288 289
## 900.2023 900.2023 900.2023 994.2692 994.2692 994.2692 900.2023
## 290 291 292 293 294 295 299
## 900.2023 900.2023 994.2692 900.2023 900.2023 962.9136 962.9136
## 300 301 304 305 307 315 316
## 900.2023 962.9136 900.2023 962.9136 900.2023 1213.7585 1213.7585
## 317 318 319 320 321 322 323
## 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585
## 324 325 326 327 328 329 330
## 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585
## 331 332 333 334 335 336 337
## 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585
## 338 339 340 341 342 343 344
## 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585
## 345 346 347 348 349 350 351
## 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585 1213.7585
## 352 353 354 355 356 357 358
## 1213.7585 1213.7585 1088.3361 1088.3361 1088.3361 1088.3361 1213.7585
## 359 360 361 362 363 364 365
## 1213.7585 1213.7585 1088.3361 1088.3361 1213.7585 1213.7585 1213.7585
## 366 367 368 369 370 371 372
## 1213.7585 1088.3361 1088.3361 1088.3361 1088.3361 1088.3361 1088.3361
## 373 374 375 376 377 378 379
## 1088.3361 1088.3361 1088.3361 1088.3361 1088.3361 1088.3361 837.4911
## 380 381 382 383 384 385 386
## 837.4911 837.4911 837.4911 837.4911 837.4911 837.4911 837.4911
## 387 388 389 390 391 392 393
## 837.4911 837.4911 837.4911 837.4911 837.4911 837.4911 837.4911
## 394 395 396 397 398 399 400
## 837.4911 837.4911 837.4911 837.4911 837.4911 837.4911 837.4911
## 401 402 403 404 405 406 407
## 837.4911 837.4911 837.4911 837.4911 837.4911 1088.3361 1088.3361
## 408 409 430 431 432 436 437
## 1088.3361 1088.3361 1527.3148 1527.3148 1527.3148 1527.3148 1527.3148
## 438 440 441 442 443 444 445
## 1527.3148 586.6461 586.6461 586.6461 586.6461 586.6461 586.6461
## 446 447 448 449 450 451 452
## 586.6461 586.6461 586.6461 586.6461 586.6461 586.6461 586.6461
## 453 454 455 456 457 458
## 586.6461 586.6461 586.6461 586.6461 586.6461 586.6461cor(Boeing$PricePremium,Boeing$SeatsPremium)## [1] 0.478673fit<-lm(PriceEconomy~PriceRelative,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1443.5 -797.1 -111.1 587.5 2388.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1662.97 78.21 21.263 < 2e-16 ***
## PriceRelative -682.79 110.55 -6.176 2.1e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 923.1 on 305 degrees of freedom
## Multiple R-squared: 0.1112, Adjusted R-squared: 0.1082
## F-statistic: 38.14 on 1 and 305 DF, p-value: 2.096e-09Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 1403.5124 1403.5124 1403.5124 1403.5124 1205.5035 1205.5035 1205.5035
## 8 9 10 11 12 13 14
## 959.6994 959.6994 1150.8804 1150.8804 1280.6103 1485.4471 1307.9219
## 15 16 17 18 19 20 21
## 1307.9219 1307.9219 1403.5124 1403.5124 1403.5124 1430.8240 1430.8240
## 22 23 24 25 26 27 28
## 1430.8240 1437.6519 1437.6519 1437.6519 1423.9961 1437.6519 1437.6519
## 29 30 31 32 33 34 35
## 1430.8240 1430.8240 1430.8240 1376.2008 1376.2008 1376.2008 1376.2008
## 36 37 38 39 40 41 42
## 1219.1593 1219.1593 1219.1593 1499.1029 1499.1029 1499.1029 1499.1029
## 43 44 45 46 47 48 49
## 1546.8981 1546.8981 1546.8981 1608.3492 1608.3492 1608.3492 1307.9219
## 50 51 52 53 54 55 56
## 1307.9219 1307.9219 959.6994 1417.1682 1417.1682 1417.1682 1430.8240
## 57 58 59 60 61 74 75
## 1430.8240 1430.8240 1519.5866 1519.5866 1246.4709 1601.5213 1608.3492
## 76 77 78 79 80 81 82
## 1615.1771 1615.1771 1615.1771 1635.6607 1635.6607 1642.4886 932.3878
## 83 84 85 86 87 88 89
## 932.3878 932.3878 932.3878 1389.8566 1389.8566 1389.8566 1389.8566
## 90 91 92 93 94 95 96
## 1335.2335 1335.2335 1335.2335 1335.2335 1437.6519 1437.6519 1437.6519
## 97 98 138 144 147 148 149
## 1485.4471 1601.5213 1485.4471 1458.1355 1464.9634 1464.9634 1389.8566
## 151 152 153 154 155 156 157
## 1437.6519 1574.2097 1594.6934 1601.5213 1622.0049 420.2959 420.2959
## 158 159 160 161 162 163 164
## 420.2959 420.2959 481.7470 481.7470 481.7470 720.7232 1000.6668
## 165 166 167 168 169 170 171
## 1000.6668 1000.6668 1000.6668 1041.6341 1041.6341 1041.6341 1041.6341
## 172 173 174 175 176 177 178
## 1089.4294 1280.6103 1314.7498 1314.7498 1314.7498 1314.7498 1321.5777
## 179 180 181 182 183 184 240
## 1328.4056 1389.8566 1389.8566 1389.8566 1389.8566 1485.4471 891.4205
## 241 242 243 244 245 246 247
## 891.4205 1485.4471 1355.7172 1355.7172 1355.7172 1417.1682 1417.1682
## 248 249 250 251 252 253 254
## 1417.1682 1417.1682 993.8389 993.8389 993.8389 1437.6519 1437.6519
## 255 256 257 258 259 260 261
## 1437.6519 1437.6519 1417.1682 1417.1682 1417.1682 891.4205 1376.2008
## 262 263 264 265 266 267 268
## 1376.2008 1376.2008 1389.8566 1389.8566 1389.8566 1116.7409 1615.1771
## 269 270 271 272 273 274 275
## 1615.1771 1615.1771 905.0763 905.0763 1041.6341 1526.4145 1116.7409
## 276 277 278 279 280 281 282
## 1546.8981 1546.8981 1546.8981 1519.5866 1273.7825 1567.3818 1567.3818
## 283 284 285 286 287 288 289
## 1581.0376 1581.0376 1581.0376 1587.8655 1587.8655 1587.8655 1587.8655
## 290 291 292 293 294 295 299
## 1587.8655 1587.8655 1594.6934 1594.6934 1594.6934 1601.5213 1608.3492
## 300 301 304 305 307 315 316
## 1615.1771 1615.1771 1628.8328 1635.6607 1635.6607 905.0763 1096.2573
## 317 318 319 320 321 322 323
## 1096.2573 1137.2246 1253.2988 1253.2988 1253.2988 1287.4382 1335.2335
## 324 325 326 327 328 329 330
## 1335.2335 1574.2097 1574.2097 1574.2097 1574.2097 1574.2097 1574.2097
## 331 332 333 334 335 336 337
## 1594.6934 1594.6934 1594.6934 1594.6934 1601.5213 1601.5213 1601.5213
## 338 339 340 341 342 343 344
## 1601.5213 1417.1682 1417.1682 1417.1682 1608.3492 1615.1771 1615.1771
## 345 346 347 348 349 350 351
## 1615.1771 1615.1771 1635.6607 1635.6607 1635.6607 1642.4886 1642.4886
## 352 353 354 355 356 357 358
## 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886
## 359 360 361 362 363 364 365
## 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886 1642.4886
## 366 367 368 369 370 371 372
## 1642.4886 713.8953 713.8953 713.8953 1567.3818 1567.3818 1567.3818
## 373 374 375 376 377 378 379
## 1137.2246 1335.2335 1335.2335 1635.6607 1307.9219 1410.3403 372.5007
## 380 381 382 383 384 385 386
## 372.5007 372.5007 386.1565 522.7143 543.1980 618.3048 782.1742
## 387 388 389 390 391 392 393
## 802.6579 802.6579 802.6579 905.0763 905.0763 905.0763 918.7321
## 394 395 396 397 398 399 400
## 939.2157 952.8715 952.8715 1041.6341 1109.9130 1123.5688 1157.7083
## 401 402 403 404 405 406 407
## 1157.7083 1157.7083 1157.7083 1321.5777 1546.8981 543.1980 543.1980
## 408 409 430 431 432 436 437
## 679.7558 1280.6103 1615.1771 1615.1771 1615.1771 1635.6607 1642.4886
## 438 440 441 442 443 444 445
## 1642.4886 495.4028 515.8864 515.8864 775.3463 775.3463 775.3463
## 446 447 448 449 450 451 452
## 775.3463 829.9695 932.3878 1137.2246 1137.2246 1137.2246 1219.1593
## 453 454 455 456 457 458
## 1253.2988 1266.9546 1355.7172 1355.7172 1403.5124 1581.0376cor(Boeing$PricePremium,Boeing$PriceRelative)## [1] -0.01949137fit<-lm(PriceEconomy~PercentPremiumSeats,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1257.1 -858.1 -209.6 475.0 2411.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1000.24 160.88 6.217 1.66e-09 ***
## PercentPremiumSeats 20.33 10.04 2.025 0.0437 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 972.6 on 305 degrees of freedom
## Multiple R-squared: 0.01326, Adjusted R-squared: 0.01003
## F-statistic: 4.1 on 1 and 305 DF, p-value: 0.04375Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 9 10 11 12 13 14 15 16
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 17 18 19 20 21 22 23 24
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 25 26 27 28 29 30 31 32
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 33 34 35 36 37 38 39 40
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 41 42 43 44 45 46 47 48
## 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147 1502.147
## 49 50 51 52 53 54 55 56
## 1502.147 1502.147 1502.147 1477.752 1477.752 1477.752 1477.752 1477.752
## 57 58 59 60 61 74 75 76
## 1477.752 1477.752 1477.752 1477.752 1477.752 1415.141 1415.141 1415.141
## 77 78 79 80 81 82 83 84
## 1415.141 1415.141 1415.141 1415.141 1415.141 1380.989 1380.989 1380.989
## 85 86 87 88 89 90 91 92
## 1380.989 1380.989 1380.989 1380.989 1380.989 1343.178 1343.178 1343.178
## 93 94 95 96 97 98 138 144
## 1343.178 1343.178 1343.178 1343.178 1343.178 1334.843 1312.482 1312.482
## 147 148 149 151 152 153 154 155
## 1312.482 1312.482 1312.482 1312.482 1295.000 1295.000 1295.000 1295.000
## 156 157 158 159 160 161 162 163
## 1305.570 1305.570 1305.570 1305.570 1305.570 1305.570 1305.570 1305.570
## 164 165 166 167 168 169 170 171
## 1304.554 1304.554 1304.554 1304.554 1305.570 1305.570 1305.570 1305.570
## 172 173 174 175 176 177 178 179
## 1305.570 1305.570 1304.554 1304.554 1304.554 1304.554 1305.570 1305.570
## 180 181 182 183 184 240 241 242
## 1305.570 1305.570 1305.570 1305.570 1305.570 1262.474 1262.474 1262.474
## 243 244 245 246 247 248 249 250
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 251 252 253 254 255 256 257 258
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 259 260 261 262 263 264 265 266
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 267 268 269 270 271 272 273 274
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474
## 275 276 277 278 279 280 281 282
## 1262.474 1262.474 1262.474 1262.474 1262.474 1262.474 1254.343 1254.343
## 283 284 285 286 287 288 289 290
## 1254.343 1254.343 1254.343 1267.150 1267.150 1267.150 1254.343 1254.343
## 291 292 293 294 295 299 300 301
## 1254.343 1267.150 1254.343 1254.343 1260.848 1260.848 1254.343 1260.848
## 304 305 307 315 316 317 318 319
## 1254.343 1260.848 1254.343 1268.776 1268.776 1268.776 1268.776 1268.776
## 320 321 322 323 324 325 326 327
## 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776
## 328 329 330 331 332 333 334 335
## 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776 1268.776
## 336 337 338 339 340 341 342 343
## 1268.776 1268.776 1268.776 1249.871 1249.871 1249.871 1249.871 1249.871
## 344 345 346 347 348 349 350 351
## 1249.871 1249.871 1249.871 1249.871 1249.871 1249.871 1249.871 1249.871
## 352 353 354 355 356 357 358 359
## 1249.871 1249.871 1246.618 1246.618 1246.618 1246.618 1249.871 1249.871
## 360 361 362 363 364 365 366 367
## 1249.871 1246.618 1246.618 1249.871 1249.871 1249.871 1249.871 1215.109
## 368 369 370 371 372 373 374 375
## 1215.109 1215.109 1215.109 1215.109 1215.109 1215.109 1215.109 1215.109
## 376 377 378 379 380 381 382 383
## 1215.109 1215.109 1215.109 1232.591 1232.591 1232.591 1232.591 1232.591
## 384 385 386 387 388 389 390 391
## 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591
## 392 393 394 395 396 397 398 399
## 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591
## 400 401 402 403 404 405 406 407
## 1232.591 1232.591 1232.591 1232.591 1232.591 1232.591 1203.522 1203.522
## 408 409 430 431 432 436 437 438
## 1203.522 1203.522 1181.160 1181.160 1181.160 1181.160 1181.160 1181.160
## 440 441 442 443 444 445 446 447
## 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984
## 448 449 450 451 452 453 454 455
## 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984 1095.984
## 456 457 458
## 1095.984 1095.984 1095.984cor(Boeing$PricePremium,Boeing$PercentPremiumSeats)## [1] 0.1539623fit<-lm(PricePremium~PitchEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2686.0 -907.6 -169.7 964.2 5641.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15187.5 3226.1 -4.708 3.81e-06 ***
## PitchEconomy 547.1 103.7 5.277 2.49e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1254 on 305 degrees of freedom
## Multiple R-squared: 0.08367, Adjusted R-squared: 0.08067
## F-statistic: 27.85 on 1 and 305 DF, p-value: 2.492e-07Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 9 10 11 12 13 14 15 16
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 17 18 19 20 21 22 23 24
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 25 26 27 28 29 30 31 32
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 33 34 35 36 37 38 39 40
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 41 42 43 44 45 46 47 48
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 49 50 51 52 53 54 55 56
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 57 58 59 60 61 74 75 76
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 77 78 79 80 81 82 83 84
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 85 86 87 88 89 90 91 92
## 1772.741 1772.741 1772.741 1772.741 1772.741 1225.635 1225.635 1225.635
## 93 94 95 96 97 98 138 144
## 1225.635 1225.635 1225.635 1225.635 1225.635 2319.846 1772.741 1772.741
## 147 148 149 151 152 153 154 155
## 1772.741 1772.741 1772.741 1772.741 2319.846 2319.846 2319.846 2319.846
## 156 157 158 159 160 161 162 163
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 164 165 166 167 168 169 170 171
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 172 173 174 175 176 177 178 179
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 180 181 182 183 184 240 241 242
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 243 244 245 246 247 248 249 250
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 251 252 253 254 255 256 257 258
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 259 260 261 262 263 264 265 266
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 267 268 269 270 271 272 273 274
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 275 276 277 278 279 280 281 282
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 2319.846 2319.846
## 283 284 285 286 287 288 289 290
## 2319.846 2319.846 2319.846 1772.741 1772.741 1772.741 2319.846 2319.846
## 291 292 293 294 295 299 300 301
## 2319.846 1772.741 2319.846 2319.846 2866.952 2866.952 2319.846 2866.952
## 304 305 307 315 316 317 318 319
## 2319.846 2866.952 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846
## 320 321 322 323 324 325 326 327
## 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846
## 328 329 330 331 332 333 334 335
## 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846
## 336 337 338 339 340 341 342 343
## 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846
## 344 345 346 347 348 349 350 351
## 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846
## 352 353 354 355 356 357 358 359
## 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846
## 360 361 362 363 364 365 366 367
## 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 1772.741
## 368 369 370 371 372 373 374 375
## 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741 1772.741
## 376 377 378 379 380 381 382 383
## 1772.741 1772.741 1772.741 1225.635 1225.635 1225.635 1225.635 1225.635
## 384 385 386 387 388 389 390 391
## 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635
## 392 393 394 395 396 397 398 399
## 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635
## 400 401 402 403 404 405 406 407
## 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 2319.846 2319.846
## 408 409 430 431 432 436 437 438
## 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846 2319.846
## 440 441 442 443 444 445 446 447
## 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635
## 448 449 450 451 452 453 454 455
## 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635 1225.635
## 456 457 458
## 1225.635 1225.635 1225.635cor(Boeing$PricePremium,Boeing$PitchEconomy)## [1] 0.2892619fit<-lm(PriceEconomy~PitchEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2285.6 -524.5 -115.8 573.2 1863.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17558.47 2276.13 -7.714 1.74e-13 ***
## PitchEconomy 606.36 73.14 8.290 3.65e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 884.5 on 305 degrees of freedom
## Multiple R-squared: 0.1839, Adjusted R-squared: 0.1812
## F-statistic: 68.72 on 1 and 305 DF, p-value: 3.655e-15Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 9 10 11 12 13 14 15 16
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 17 18 19 20 21 22 23 24
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 25 26 27 28 29 30 31 32
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 33 34 35 36 37 38 39 40
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 41 42 43 44 45 46 47 48
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 49 50 51 52 53 54 55 56
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 57 58 59 60 61 74 75 76
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 77 78 79 80 81 82 83 84
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 85 86 87 88 89 90 91 92
## 1238.833 1238.833 1238.833 1238.833 1238.833 632.468 632.468 632.468
## 93 94 95 96 97 98 138 144
## 632.468 632.468 632.468 632.468 632.468 1845.197 1238.833 1238.833
## 147 148 149 151 152 153 154 155
## 1238.833 1238.833 1238.833 1238.833 1845.197 1845.197 1845.197 1845.197
## 156 157 158 159 160 161 162 163
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 164 165 166 167 168 169 170 171
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 172 173 174 175 176 177 178 179
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 180 181 182 183 184 240 241 242
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 243 244 245 246 247 248 249 250
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 251 252 253 254 255 256 257 258
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 259 260 261 262 263 264 265 266
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 267 268 269 270 271 272 273 274
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 275 276 277 278 279 280 281 282
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1845.197 1845.197
## 283 284 285 286 287 288 289 290
## 1845.197 1845.197 1845.197 1238.833 1238.833 1238.833 1845.197 1845.197
## 291 292 293 294 295 299 300 301
## 1845.197 1238.833 1845.197 1845.197 2451.562 2451.562 1845.197 2451.562
## 304 305 307 315 316 317 318 319
## 1845.197 2451.562 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197
## 320 321 322 323 324 325 326 327
## 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197
## 328 329 330 331 332 333 334 335
## 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197
## 336 337 338 339 340 341 342 343
## 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197
## 344 345 346 347 348 349 350 351
## 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197
## 352 353 354 355 356 357 358 359
## 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197
## 360 361 362 363 364 365 366 367
## 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1238.833
## 368 369 370 371 372 373 374 375
## 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833 1238.833
## 376 377 378 379 380 381 382 383
## 1238.833 1238.833 1238.833 632.468 632.468 632.468 632.468 632.468
## 384 385 386 387 388 389 390 391
## 632.468 632.468 632.468 632.468 632.468 632.468 632.468 632.468
## 392 393 394 395 396 397 398 399
## 632.468 632.468 632.468 632.468 632.468 632.468 632.468 632.468
## 400 401 402 403 404 405 406 407
## 632.468 632.468 632.468 632.468 632.468 632.468 1845.197 1845.197
## 408 409 430 431 432 436 437 438
## 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197 1845.197
## 440 441 442 443 444 445 446 447
## 632.468 632.468 632.468 632.468 632.468 632.468 632.468 632.468
## 448 449 450 451 452 453 454 455
## 632.468 632.468 632.468 632.468 632.468 632.468 632.468 632.468
## 456 457 458
## 632.468 632.468 632.468cor(Boeing$PriceEconomy,Boeing$PitchEconomy)## [1] 0.428823fit<-lm(PricePremium~PitchPremium,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchPremium, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1750.4 -1242.0 -126.4 1156.6 5577.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56.28 1872.89 0.03 0.976
## PitchPremium 46.84 49.33 0.95 0.343
##
## Residual standard error: 1308 on 305 degrees of freedom
## Multiple R-squared: 0.002948, Adjusted R-squared: -0.0003213
## F-statistic: 0.9017 on 1 and 305 DF, p-value: 0.3431Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 9 10 11 12 13 14 15 16
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 17 18 19 20 21 22 23 24
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 25 26 27 28 29 30 31 32
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 33 34 35 36 37 38 39 40
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 41 42 43 44 45 46 47 48
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 49 50 51 52 53 54 55 56
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 57 58 59 60 61 74 75 76
## 1836.384 1836.384 1836.384 1836.384 1836.384 1649.005 1649.005 1649.005
## 77 78 79 80 81 82 83 84
## 1649.005 1649.005 1649.005 1649.005 1649.005 1836.384 1836.384 1836.384
## 85 86 87 88 89 90 91 92
## 1836.384 1836.384 1836.384 1836.384 1836.384 1930.073 1930.073 1930.073
## 93 94 95 96 97 98 138 144
## 1930.073 1930.073 1930.073 1930.073 1930.073 1649.005 1836.384 1836.384
## 147 148 149 151 152 153 154 155
## 1836.384 1836.384 1836.384 1836.384 1695.850 1695.850 1695.850 1695.850
## 156 157 158 159 160 161 162 163
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 164 165 166 167 168 169 170 171
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 172 173 174 175 176 177 178 179
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 180 181 182 183 184 240 241 242
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 243 244 245 246 247 248 249 250
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 251 252 253 254 255 256 257 258
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 259 260 261 262 263 264 265 266
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 267 268 269 270 271 272 273 274
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 275 276 277 278 279 280 281 282
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1649.005 1649.005
## 283 284 285 286 287 288 289 290
## 1649.005 1649.005 1649.005 1649.005 1649.005 1649.005 1649.005 1649.005
## 291 292 293 294 295 299 300 301
## 1649.005 1649.005 1649.005 1649.005 1695.850 1695.850 1649.005 1695.850
## 304 305 307 315 316 317 318 319
## 1649.005 1695.850 1649.005 1836.384 1836.384 1836.384 1836.384 1836.384
## 320 321 322 323 324 325 326 327
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 328 329 330 331 332 333 334 335
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 336 337 338 339 340 341 342 343
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 344 345 346 347 348 349 350 351
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 352 353 354 355 356 357 358 359
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 360 361 362 363 364 365 366 367
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 368 369 370 371 372 373 374 375
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 376 377 378 379 380 381 382 383
## 1836.384 1836.384 1836.384 1930.073 1930.073 1930.073 1930.073 1930.073
## 384 385 386 387 388 389 390 391
## 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073
## 392 393 394 395 396 397 398 399
## 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073
## 400 401 402 403 404 405 406 407
## 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1836.384 1836.384
## 408 409 430 431 432 436 437 438
## 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384 1836.384
## 440 441 442 443 444 445 446 447
## 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073
## 448 449 450 451 452 453 454 455
## 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073 1930.073
## 456 457 458
## 1930.073 1930.073 1930.073cor(Boeing$PricePremium,Boeing$PitchPremium)## [1] 0.05429251fit<-lm(PriceEconomy~PitchPremium,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchPremium, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1241.51 -861.23 -82.51 505.49 2286.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 999.800 1402.156 0.713 0.476
## PitchPremium 8.071 36.933 0.219 0.827
##
## Residual standard error: 979 on 305 degrees of freedom
## Multiple R-squared: 0.0001566, Adjusted R-squared: -0.003122
## F-statistic: 0.04776 on 1 and 305 DF, p-value: 0.8272Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 9 10 11 12 13 14 15 16
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 17 18 19 20 21 22 23 24
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 25 26 27 28 29 30 31 32
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 33 34 35 36 37 38 39 40
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 41 42 43 44 45 46 47 48
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 49 50 51 52 53 54 55 56
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 57 58 59 60 61 74 75 76
## 1306.513 1306.513 1306.513 1306.513 1306.513 1274.227 1274.227 1274.227
## 77 78 79 80 81 82 83 84
## 1274.227 1274.227 1274.227 1274.227 1274.227 1306.513 1306.513 1306.513
## 85 86 87 88 89 90 91 92
## 1306.513 1306.513 1306.513 1306.513 1306.513 1322.656 1322.656 1322.656
## 93 94 95 96 97 98 138 144
## 1322.656 1322.656 1322.656 1322.656 1322.656 1274.227 1306.513 1306.513
## 147 148 149 151 152 153 154 155
## 1306.513 1306.513 1306.513 1306.513 1282.299 1282.299 1282.299 1282.299
## 156 157 158 159 160 161 162 163
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 164 165 166 167 168 169 170 171
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 172 173 174 175 176 177 178 179
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 180 181 182 183 184 240 241 242
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 243 244 245 246 247 248 249 250
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 251 252 253 254 255 256 257 258
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 259 260 261 262 263 264 265 266
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 267 268 269 270 271 272 273 274
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 275 276 277 278 279 280 281 282
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1274.227 1274.227
## 283 284 285 286 287 288 289 290
## 1274.227 1274.227 1274.227 1274.227 1274.227 1274.227 1274.227 1274.227
## 291 292 293 294 295 299 300 301
## 1274.227 1274.227 1274.227 1274.227 1282.299 1282.299 1274.227 1282.299
## 304 305 307 315 316 317 318 319
## 1274.227 1282.299 1274.227 1306.513 1306.513 1306.513 1306.513 1306.513
## 320 321 322 323 324 325 326 327
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 328 329 330 331 332 333 334 335
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 336 337 338 339 340 341 342 343
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 344 345 346 347 348 349 350 351
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 352 353 354 355 356 357 358 359
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 360 361 362 363 364 365 366 367
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 368 369 370 371 372 373 374 375
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 376 377 378 379 380 381 382 383
## 1306.513 1306.513 1306.513 1322.656 1322.656 1322.656 1322.656 1322.656
## 384 385 386 387 388 389 390 391
## 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656
## 392 393 394 395 396 397 398 399
## 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656
## 400 401 402 403 404 405 406 407
## 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1306.513 1306.513
## 408 409 430 431 432 436 437 438
## 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513 1306.513
## 440 441 442 443 444 445 446 447
## 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656
## 448 449 450 451 452 453 454 455
## 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656 1322.656
## 456 457 458
## 1322.656 1322.656 1322.656cor(Boeing$PriceEconomy,Boeing$PitchPremium)## [1] 0.01251271fit<-lm(PriceEconomy~WidthPremium,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthPremium, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1475.13 -851.42 -43.03 489.08 2226.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3979.55 943.75 4.217 3.27e-05 ***
## WidthPremium -137.55 48.47 -2.838 0.00485 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 966.4 on 305 degrees of freedom
## Multiple R-squared: 0.02572, Adjusted R-squared: 0.02253
## F-statistic: 8.053 on 1 and 305 DF, p-value: 0.004848Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 9 10 11 12 13 14 15 16
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 17 18 19 20 21 22 23 24
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 25 26 27 28 29 30 31 32
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 33 34 35 36 37 38 39 40
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 41 42 43 44 45 46 47 48
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 49 50 51 52 53 54 55 56
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 57 58 59 60 61 74 75 76
## 1366.027 1366.027 1366.027 1366.027 1366.027 1503.581 1503.581 1503.581
## 77 78 79 80 81 82 83 84
## 1503.581 1503.581 1503.581 1503.581 1503.581 1366.027 1366.027 1366.027
## 85 86 87 88 89 90 91 92
## 1366.027 1366.027 1366.027 1366.027 1366.027 1090.919 1090.919 1090.919
## 93 94 95 96 97 98 138 144
## 1090.919 1090.919 1090.919 1090.919 1090.919 1641.135 1366.027 1366.027
## 147 148 149 151 152 153 154 155
## 1366.027 1366.027 1366.027 1366.027 1503.581 1503.581 1503.581 1503.581
## 156 157 158 159 160 161 162 163
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919
## 164 165 166 167 168 169 170 171
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919
## 172 173 174 175 176 177 178 179
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919
## 180 181 182 183 184 240 241 242
## 1090.919 1090.919 1090.919 1090.919 1090.919 1366.027 1366.027 1366.027
## 243 244 245 246 247 248 249 250
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 251 252 253 254 255 256 257 258
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 259 260 261 262 263 264 265 266
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 267 268 269 270 271 272 273 274
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 275 276 277 278 279 280 281 282
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1641.135 1641.135
## 283 284 285 286 287 288 289 290
## 1641.135 1641.135 1641.135 1641.135 1641.135 1641.135 1641.135 1641.135
## 291 292 293 294 295 299 300 301
## 1641.135 1641.135 1641.135 1641.135 1641.135 1641.135 1641.135 1641.135
## 304 305 307 315 316 317 318 319
## 1641.135 1641.135 1641.135 1228.473 1228.473 1228.473 1228.473 1228.473
## 320 321 322 323 324 325 326 327
## 1228.473 1228.473 1228.473 1228.473 1228.473 1228.473 1228.473 1228.473
## 328 329 330 331 332 333 334 335
## 1228.473 1228.473 1228.473 1228.473 1228.473 1228.473 1228.473 1228.473
## 336 337 338 339 340 341 342 343
## 1228.473 1228.473 1228.473 1366.027 1366.027 1366.027 1366.027 1366.027
## 344 345 346 347 348 349 350 351
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 352 353 354 355 356 357 358 359
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 360 361 362 363 364 365 366 367
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 368 369 370 371 372 373 374 375
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 376 377 378 379 380 381 382 383
## 1366.027 1366.027 1366.027 1090.919 1090.919 1090.919 1090.919 1090.919
## 384 385 386 387 388 389 390 391
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919
## 392 393 394 395 396 397 398 399
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919
## 400 401 402 403 404 405 406 407
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1366.027 1366.027
## 408 409 430 431 432 436 437 438
## 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027 1366.027
## 440 441 442 443 444 445 446 447
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919
## 448 449 450 451 452 453 454 455
## 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919 1090.919
## 456 457 458
## 1090.919 1090.919 1090.919cor(Boeing$PriceEconomy,Boeing$WidthPremium)## [1] -0.1603862fit<-lm(PricePremium~WidthPremium,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthPremium, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1812 -1207 -112 1137 5552
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3105.47 1276.83 2.432 0.0156 *
## WidthPremium -65.45 65.58 -0.998 0.3191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1308 on 305 degrees of freedom
## Multiple R-squared: 0.003255, Adjusted R-squared: -1.285e-05
## F-statistic: 0.9961 on 1 and 305 DF, p-value: 0.3191Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 9 10 11 12 13 14 15 16
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 17 18 19 20 21 22 23 24
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 25 26 27 28 29 30 31 32
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 33 34 35 36 37 38 39 40
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 41 42 43 44 45 46 47 48
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 49 50 51 52 53 54 55 56
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 57 58 59 60 61 74 75 76
## 1861.900 1861.900 1861.900 1861.900 1861.900 1927.351 1927.351 1927.351
## 77 78 79 80 81 82 83 84
## 1927.351 1927.351 1927.351 1927.351 1927.351 1861.900 1861.900 1861.900
## 85 86 87 88 89 90 91 92
## 1861.900 1861.900 1861.900 1861.900 1861.900 1730.998 1730.998 1730.998
## 93 94 95 96 97 98 138 144
## 1730.998 1730.998 1730.998 1730.998 1730.998 1992.802 1861.900 1861.900
## 147 148 149 151 152 153 154 155
## 1861.900 1861.900 1861.900 1861.900 1927.351 1927.351 1927.351 1927.351
## 156 157 158 159 160 161 162 163
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998
## 164 165 166 167 168 169 170 171
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998
## 172 173 174 175 176 177 178 179
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998
## 180 181 182 183 184 240 241 242
## 1730.998 1730.998 1730.998 1730.998 1730.998 1861.900 1861.900 1861.900
## 243 244 245 246 247 248 249 250
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 251 252 253 254 255 256 257 258
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 259 260 261 262 263 264 265 266
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 267 268 269 270 271 272 273 274
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 275 276 277 278 279 280 281 282
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1992.802 1992.802
## 283 284 285 286 287 288 289 290
## 1992.802 1992.802 1992.802 1992.802 1992.802 1992.802 1992.802 1992.802
## 291 292 293 294 295 299 300 301
## 1992.802 1992.802 1992.802 1992.802 1992.802 1992.802 1992.802 1992.802
## 304 305 307 315 316 317 318 319
## 1992.802 1992.802 1992.802 1796.449 1796.449 1796.449 1796.449 1796.449
## 320 321 322 323 324 325 326 327
## 1796.449 1796.449 1796.449 1796.449 1796.449 1796.449 1796.449 1796.449
## 328 329 330 331 332 333 334 335
## 1796.449 1796.449 1796.449 1796.449 1796.449 1796.449 1796.449 1796.449
## 336 337 338 339 340 341 342 343
## 1796.449 1796.449 1796.449 1861.900 1861.900 1861.900 1861.900 1861.900
## 344 345 346 347 348 349 350 351
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 352 353 354 355 356 357 358 359
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 360 361 362 363 364 365 366 367
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 368 369 370 371 372 373 374 375
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 376 377 378 379 380 381 382 383
## 1861.900 1861.900 1861.900 1730.998 1730.998 1730.998 1730.998 1730.998
## 384 385 386 387 388 389 390 391
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998
## 392 393 394 395 396 397 398 399
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998
## 400 401 402 403 404 405 406 407
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1861.900 1861.900
## 408 409 430 431 432 436 437 438
## 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900 1861.900
## 440 441 442 443 444 445 446 447
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998
## 448 449 450 451 452 453 454 455
## 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998 1730.998
## 456 457 458
## 1730.998 1730.998 1730.998cor(Boeing$PricePremium,Boeing$WidthPremium)## [1] -0.05705404fit<-lm(PriceEconomy~WidthEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1293.2 -821.8 -218.2 453.8 2425.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2075.98 1651.90 -1.257 0.2098
## WidthEconomy 190.79 93.14 2.048 0.0414 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 972.4 on 305 degrees of freedom
## Multiple R-squared: 0.01357, Adjusted R-squared: 0.01034
## F-statistic: 4.196 on 1 and 305 DF, p-value: 0.04137Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 9 10 11 12 13 14 15 16
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 17 18 19 20 21 22 23 24
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 25 26 27 28 29 30 31 32
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 33 34 35 36 37 38 39 40
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 41 42 43 44 45 46 47 48
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 49 50 51 52 53 54 55 56
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 57 58 59 60 61 74 75 76
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 77 78 79 80 81 82 83 84
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 85 86 87 88 89 90 91 92
## 1358.189 1358.189 1358.189 1358.189 1358.189 1167.402 1167.402 1167.402
## 93 94 95 96 97 98 138 144
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1358.189 1358.189
## 147 148 149 151 152 153 154 155
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 156 157 158 159 160 161 162 163
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 164 165 166 167 168 169 170 171
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 172 173 174 175 176 177 178 179
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 180 181 182 183 184 240 241 242
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 243 244 245 246 247 248 249 250
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 251 252 253 254 255 256 257 258
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 259 260 261 262 263 264 265 266
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 267 268 269 270 271 272 273 274
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 275 276 277 278 279 280 281 282
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1167.402 1167.402
## 283 284 285 286 287 288 289 290
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 291 292 293 294 295 299 300 301
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 304 305 307 315 316 317 318 319
## 1167.402 1167.402 1167.402 1548.976 1548.976 1548.976 1548.976 1548.976
## 320 321 322 323 324 325 326 327
## 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976
## 328 329 330 331 332 333 334 335
## 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976
## 336 337 338 339 340 341 342 343
## 1548.976 1548.976 1548.976 1167.402 1167.402 1167.402 1167.402 1167.402
## 344 345 346 347 348 349 350 351
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 352 353 354 355 356 357 358 359
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 360 361 362 363 364 365 366 367
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1358.189
## 368 369 370 371 372 373 374 375
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 376 377 378 379 380 381 382 383
## 1358.189 1358.189 1358.189 1167.402 1167.402 1167.402 1167.402 1167.402
## 384 385 386 387 388 389 390 391
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 392 393 394 395 396 397 398 399
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 400 401 402 403 404 405 406 407
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 408 409 430 431 432 436 437 438
## 1167.402 1167.402 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 440 441 442 443 444 445 446 447
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 448 449 450 451 452 453 454 455
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 456 457 458
## 1167.402 1167.402 1167.402cor(Boeing$PriceEconomy,Boeing$WidthEconomy)## [1] 0.1164968fit<-lm(PricePremium~WidthEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1877.3 -1031.4 -344.3 1033.7 5450.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6588.2 2171.8 -3.034 0.002625 **
## WidthEconomy 475.1 122.4 3.880 0.000128 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1278 on 305 degrees of freedom
## Multiple R-squared: 0.04704, Adjusted R-squared: 0.04391
## F-statistic: 15.05 on 1 and 305 DF, p-value: 0.0001281Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 9 10 11 12 13 14 15 16
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 17 18 19 20 21 22 23 24
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 25 26 27 28 29 30 31 32
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 33 34 35 36 37 38 39 40
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 41 42 43 44 45 46 47 48
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 49 50 51 52 53 54 55 56
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 57 58 59 60 61 74 75 76
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 77 78 79 80 81 82 83 84
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 85 86 87 88 89 90 91 92
## 1963.323 1963.323 1963.323 1963.323 1963.323 1488.239 1488.239 1488.239
## 93 94 95 96 97 98 138 144
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1963.323 1963.323
## 147 148 149 151 152 153 154 155
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 156 157 158 159 160 161 162 163
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 164 165 166 167 168 169 170 171
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 172 173 174 175 176 177 178 179
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 180 181 182 183 184 240 241 242
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 243 244 245 246 247 248 249 250
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 251 252 253 254 255 256 257 258
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 259 260 261 262 263 264 265 266
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 267 268 269 270 271 272 273 274
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 275 276 277 278 279 280 281 282
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1488.239 1488.239
## 283 284 285 286 287 288 289 290
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 291 292 293 294 295 299 300 301
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 304 305 307 315 316 317 318 319
## 1488.239 1488.239 1488.239 2438.407 2438.407 2438.407 2438.407 2438.407
## 320 321 322 323 324 325 326 327
## 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407
## 328 329 330 331 332 333 334 335
## 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407
## 336 337 338 339 340 341 342 343
## 2438.407 2438.407 2438.407 1488.239 1488.239 1488.239 1488.239 1488.239
## 344 345 346 347 348 349 350 351
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 352 353 354 355 356 357 358 359
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 360 361 362 363 364 365 366 367
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1963.323
## 368 369 370 371 372 373 374 375
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 376 377 378 379 380 381 382 383
## 1963.323 1963.323 1963.323 1488.239 1488.239 1488.239 1488.239 1488.239
## 384 385 386 387 388 389 390 391
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 392 393 394 395 396 397 398 399
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 400 401 402 403 404 405 406 407
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 408 409 430 431 432 436 437 438
## 1488.239 1488.239 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 440 441 442 443 444 445 446 447
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 448 449 450 451 452 453 454 455
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 456 457 458
## 1488.239 1488.239 1488.239cor(Boeing$PricePremium,Boeing$WidthEconomy)## [1] 0.216876fit<-lm(PriceEconomy~WidthEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1293.2 -821.8 -218.2 453.8 2425.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2075.98 1651.90 -1.257 0.2098
## WidthEconomy 190.79 93.14 2.048 0.0414 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 972.4 on 305 degrees of freedom
## Multiple R-squared: 0.01357, Adjusted R-squared: 0.01034
## F-statistic: 4.196 on 1 and 305 DF, p-value: 0.04137Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 9 10 11 12 13 14 15 16
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 17 18 19 20 21 22 23 24
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 25 26 27 28 29 30 31 32
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 33 34 35 36 37 38 39 40
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 41 42 43 44 45 46 47 48
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 49 50 51 52 53 54 55 56
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 57 58 59 60 61 74 75 76
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 77 78 79 80 81 82 83 84
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 85 86 87 88 89 90 91 92
## 1358.189 1358.189 1358.189 1358.189 1358.189 1167.402 1167.402 1167.402
## 93 94 95 96 97 98 138 144
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1358.189 1358.189
## 147 148 149 151 152 153 154 155
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 156 157 158 159 160 161 162 163
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 164 165 166 167 168 169 170 171
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 172 173 174 175 176 177 178 179
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 180 181 182 183 184 240 241 242
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 243 244 245 246 247 248 249 250
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 251 252 253 254 255 256 257 258
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 259 260 261 262 263 264 265 266
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 267 268 269 270 271 272 273 274
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 275 276 277 278 279 280 281 282
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1167.402 1167.402
## 283 284 285 286 287 288 289 290
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 291 292 293 294 295 299 300 301
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 304 305 307 315 316 317 318 319
## 1167.402 1167.402 1167.402 1548.976 1548.976 1548.976 1548.976 1548.976
## 320 321 322 323 324 325 326 327
## 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976
## 328 329 330 331 332 333 334 335
## 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976 1548.976
## 336 337 338 339 340 341 342 343
## 1548.976 1548.976 1548.976 1167.402 1167.402 1167.402 1167.402 1167.402
## 344 345 346 347 348 349 350 351
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 352 353 354 355 356 357 358 359
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 360 361 362 363 364 365 366 367
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1358.189
## 368 369 370 371 372 373 374 375
## 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 376 377 378 379 380 381 382 383
## 1358.189 1358.189 1358.189 1167.402 1167.402 1167.402 1167.402 1167.402
## 384 385 386 387 388 389 390 391
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 392 393 394 395 396 397 398 399
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 400 401 402 403 404 405 406 407
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 408 409 430 431 432 436 437 438
## 1167.402 1167.402 1358.189 1358.189 1358.189 1358.189 1358.189 1358.189
## 440 441 442 443 444 445 446 447
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 448 449 450 451 452 453 454 455
## 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402 1167.402
## 456 457 458
## 1167.402 1167.402 1167.402cor(Boeing$PriceEconomy,Boeing$WidthEconomy)## [1] 0.1164968fit<-lm(PricePremium~WidthEconomy,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthEconomy, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1877.3 -1031.4 -344.3 1033.7 5450.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6588.2 2171.8 -3.034 0.002625 **
## WidthEconomy 475.1 122.4 3.880 0.000128 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1278 on 305 degrees of freedom
## Multiple R-squared: 0.04704, Adjusted R-squared: 0.04391
## F-statistic: 15.05 on 1 and 305 DF, p-value: 0.0001281Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 9 10 11 12 13 14 15 16
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 17 18 19 20 21 22 23 24
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 25 26 27 28 29 30 31 32
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 33 34 35 36 37 38 39 40
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 41 42 43 44 45 46 47 48
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 49 50 51 52 53 54 55 56
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 57 58 59 60 61 74 75 76
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 77 78 79 80 81 82 83 84
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 85 86 87 88 89 90 91 92
## 1963.323 1963.323 1963.323 1963.323 1963.323 1488.239 1488.239 1488.239
## 93 94 95 96 97 98 138 144
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1963.323 1963.323
## 147 148 149 151 152 153 154 155
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 156 157 158 159 160 161 162 163
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 164 165 166 167 168 169 170 171
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 172 173 174 175 176 177 178 179
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 180 181 182 183 184 240 241 242
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 243 244 245 246 247 248 249 250
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 251 252 253 254 255 256 257 258
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 259 260 261 262 263 264 265 266
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 267 268 269 270 271 272 273 274
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 275 276 277 278 279 280 281 282
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1488.239 1488.239
## 283 284 285 286 287 288 289 290
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 291 292 293 294 295 299 300 301
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 304 305 307 315 316 317 318 319
## 1488.239 1488.239 1488.239 2438.407 2438.407 2438.407 2438.407 2438.407
## 320 321 322 323 324 325 326 327
## 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407
## 328 329 330 331 332 333 334 335
## 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407 2438.407
## 336 337 338 339 340 341 342 343
## 2438.407 2438.407 2438.407 1488.239 1488.239 1488.239 1488.239 1488.239
## 344 345 346 347 348 349 350 351
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 352 353 354 355 356 357 358 359
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 360 361 362 363 364 365 366 367
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1963.323
## 368 369 370 371 372 373 374 375
## 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 376 377 378 379 380 381 382 383
## 1963.323 1963.323 1963.323 1488.239 1488.239 1488.239 1488.239 1488.239
## 384 385 386 387 388 389 390 391
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 392 393 394 395 396 397 398 399
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 400 401 402 403 404 405 406 407
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 408 409 430 431 432 436 437 438
## 1488.239 1488.239 1963.323 1963.323 1963.323 1963.323 1963.323 1963.323
## 440 441 442 443 444 445 446 447
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 448 449 450 451 452 453 454 455
## 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239 1488.239
## 456 457 458
## 1488.239 1488.239 1488.239cor(Boeing$PricePremium,Boeing$WidthEconomy)## [1] 0.216876fit<-lm(PriceEconomy~SeatsTotal,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsTotal, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2096.9 -682.5 -136.9 542.0 2368.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 63.4722 155.0306 0.409 0.683
## SeatsTotal 5.8616 0.6918 8.473 1.04e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 880.9 on 305 degrees of freedom
## Multiple R-squared: 0.1905, Adjusted R-squared: 0.1879
## F-statistic: 71.78 on 1 and 305 DF, p-value: 1.036e-15Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7
## 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577
## 8 9 10 11 12 13 14
## 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577
## 15 16 17 18 19 20 21
## 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577
## 22 23 24 25 26 27 28
## 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577
## 29 30 31 32 33 34 35
## 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577
## 36 37 38 39 40 41 42
## 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577
## 43 44 45 46 47 48 49
## 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577 1013.0577
## 50 51 52 53 54 55 56
## 1013.0577 1013.0577 1036.5043 1036.5043 1036.5043 1036.5043 1036.5043
## 57 58 59 60 61 74 75
## 1036.5043 1036.5043 1036.5043 1036.5043 1036.5043 637.9128 637.9128
## 76 77 78 79 80 81 82
## 637.9128 637.9128 637.9128 637.9128 637.9128 637.9128 1816.1023
## 83 84 85 86 87 88 89
## 1816.1023 1816.1023 1816.1023 1816.1023 1816.1023 1816.1023 1816.1023
## 90 91 92 93 94 95 96
## 1036.5043 1036.5043 1036.5043 1036.5043 1036.5043 1036.5043 1036.5043
## 97 98 138 144 147 148 149
## 1036.5043 989.6112 2161.9391 2161.9391 2161.9391 2161.9391 2161.9391
## 151 152 153 154 155 156 157
## 2161.9391 1235.8000 1235.8000 1235.8000 1235.8000 1429.2341 1429.2341
## 158 159 160 161 162 163 164
## 1429.2341 1429.2341 1429.2341 1429.2341 1429.2341 1429.2341 2648.4551
## 165 166 167 168 169 170 171
## 2648.4551 2648.4551 2648.4551 1429.2341 1429.2341 1429.2341 1429.2341
## 172 173 174 175 176 177 178
## 1429.2341 1429.2341 2648.4551 2648.4551 2648.4551 2648.4551 1429.2341
## 179 180 181 182 183 184 240
## 1429.2341 1429.2341 1429.2341 1429.2341 1429.2341 1429.2341 1698.8696
## 241 242 243 244 245 246 247
## 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696
## 248 249 250 251 252 253 254
## 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696
## 255 256 257 258 259 260 261
## 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696
## 262 263 264 265 266 267 268
## 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696
## 269 270 271 272 273 274 275
## 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696
## 276 277 278 279 280 281 282
## 1698.8696 1698.8696 1698.8696 1698.8696 1698.8696 907.5482 907.5482
## 283 284 285 286 287 288 289
## 907.5482 907.5482 907.5482 1001.3345 1001.3345 1001.3345 907.5482
## 290 291 292 293 294 295 299
## 907.5482 907.5482 1001.3345 907.5482 907.5482 977.8879 977.8879
## 300 301 304 305 307 315 316
## 907.5482 977.8879 907.5482 977.8879 907.5482 1306.1397 1306.1397
## 317 318 319 320 321 322 323
## 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397
## 324 325 326 327 328 329 330
## 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397
## 331 332 333 334 335 336 337
## 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397 1306.1397
## 338 339 340 341 342 343 344
## 1306.1397 1399.9259 1399.9259 1399.9259 1399.9259 1399.9259 1399.9259
## 345 346 347 348 349 350 351
## 1399.9259 1399.9259 1399.9259 1399.9259 1399.9259 1399.9259 1399.9259
## 352 353 354 355 356 357 358
## 1399.9259 1399.9259 1224.0768 1224.0768 1224.0768 1224.0768 1399.9259
## 359 360 361 362 363 364 365
## 1399.9259 1399.9259 1224.0768 1224.0768 1399.9259 1399.9259 1399.9259
## 366 367 368 369 370 371 372
## 1399.9259 1394.0643 1394.0643 1394.0643 1394.0643 1394.0643 1394.0643
## 373 374 375 376 377 378 379
## 1394.0643 1394.0643 1394.0643 1394.0643 1394.0643 1394.0643 884.1017
## 380 381 382 383 384 385 386
## 884.1017 884.1017 884.1017 884.1017 884.1017 884.1017 884.1017
## 387 388 389 390 391 392 393
## 884.1017 884.1017 884.1017 884.1017 884.1017 884.1017 884.1017
## 394 395 396 397 398 399 400
## 884.1017 884.1017 884.1017 884.1017 884.1017 884.1017 884.1017
## 401 402 403 404 405 406 407
## 884.1017 884.1017 884.1017 884.1017 884.1017 1470.2656 1470.2656
## 408 409 430 431 432 436 437
## 1470.2656 1470.2656 2566.3922 2566.3922 2566.3922 2566.3922 2566.3922
## 438 440 441 442 443 444 445
## 2566.3922 1059.9509 1059.9509 1059.9509 1059.9509 1059.9509 1059.9509
## 446 447 448 449 450 451 452
## 1059.9509 1059.9509 1059.9509 1059.9509 1059.9509 1059.9509 1059.9509
## 453 454 455 456 457 458
## 1059.9509 1059.9509 1059.9509 1059.9509 1059.9509 1059.9509cor(Boeing$PriceEconomy,Boeing$SeatsTotal)## [1] 0.4364836fit<-lm(PricePremium~SeatsTotal,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsTotal, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2982.3 -865.4 -261.3 782.3 5453.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.5680 203.3369 0.20 0.842
## SeatsTotal 8.4575 0.9074 9.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1155 on 305 degrees of freedom
## Multiple R-squared: 0.2217, Adjusted R-squared: 0.2191
## F-statistic: 86.87 on 1 and 305 DF, p-value: < 2.2e-16Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7
## 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789
## 8 9 10 11 12 13 14
## 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789
## 15 16 17 18 19 20 21
## 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789
## 22 23 24 25 26 27 28
## 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789
## 29 30 31 32 33 34 35
## 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789
## 36 37 38 39 40 41 42
## 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789
## 43 44 45 46 47 48 49
## 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789 1410.6789
## 50 51 52 53 54 55 56
## 1410.6789 1410.6789 1444.5088 1444.5088 1444.5088 1444.5088 1444.5088
## 57 58 59 60 61 74 75
## 1444.5088 1444.5088 1444.5088 1444.5088 1444.5088 869.4005 869.4005
## 76 77 78 79 80 81 82
## 869.4005 869.4005 869.4005 869.4005 869.4005 869.4005 2569.3529
## 83 84 85 86 87 88 89
## 2569.3529 2569.3529 2569.3529 2569.3529 2569.3529 2569.3529 2569.3529
## 90 91 92 93 94 95 96
## 1444.5088 1444.5088 1444.5088 1444.5088 1444.5088 1444.5088 1444.5088
## 97 98 138 144 147 148 149
## 1444.5088 1376.8490 3068.3439 3068.3439 3068.3439 3068.3439 3068.3439
## 151 152 153 154 155 156 157
## 3068.3439 1732.0629 1732.0629 1732.0629 1732.0629 2011.1596 2011.1596
## 158 159 160 161 162 163 164
## 2011.1596 2011.1596 2011.1596 2011.1596 2011.1596 2011.1596 3770.3143
## 165 166 167 168 169 170 171
## 3770.3143 3770.3143 3770.3143 2011.1596 2011.1596 2011.1596 2011.1596
## 172 173 174 175 176 177 178
## 2011.1596 2011.1596 3770.3143 3770.3143 3770.3143 3770.3143 2011.1596
## 179 180 181 182 183 184 240
## 2011.1596 2011.1596 2011.1596 2011.1596 2011.1596 2011.1596 2400.2034
## 241 242 243 244 245 246 247
## 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034
## 248 249 250 251 252 253 254
## 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034
## 255 256 257 258 259 260 261
## 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034
## 262 263 264 265 266 267 268
## 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034
## 269 270 271 272 273 274 275
## 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034
## 276 277 278 279 280 281 282
## 2400.2034 2400.2034 2400.2034 2400.2034 2400.2034 1258.4444 1258.4444
## 283 284 285 286 287 288 289
## 1258.4444 1258.4444 1258.4444 1393.7640 1393.7640 1393.7640 1258.4444
## 290 291 292 293 294 295 299
## 1258.4444 1258.4444 1393.7640 1258.4444 1258.4444 1359.9341 1359.9341
## 300 301 304 305 307 315 316
## 1258.4444 1359.9341 1258.4444 1359.9341 1258.4444 1833.5526 1833.5526
## 317 318 319 320 321 322 323
## 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526
## 324 325 326 327 328 329 330
## 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526
## 331 332 333 334 335 336 337
## 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526 1833.5526
## 338 339 340 341 342 343 344
## 1833.5526 1968.8722 1968.8722 1968.8722 1968.8722 1968.8722 1968.8722
## 345 346 347 348 349 350 351
## 1968.8722 1968.8722 1968.8722 1968.8722 1968.8722 1968.8722 1968.8722
## 352 353 354 355 356 357 358
## 1968.8722 1968.8722 1715.1480 1715.1480 1715.1480 1715.1480 1968.8722
## 359 360 361 362 363 364 365
## 1968.8722 1968.8722 1715.1480 1715.1480 1968.8722 1968.8722 1968.8722
## 366 367 368 369 370 371 372
## 1968.8722 1960.4148 1960.4148 1960.4148 1960.4148 1960.4148 1960.4148
## 373 374 375 376 377 378 379
## 1960.4148 1960.4148 1960.4148 1960.4148 1960.4148 1960.4148 1224.6145
## 380 381 382 383 384 385 386
## 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145
## 387 388 389 390 391 392 393
## 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145
## 394 395 396 397 398 399 400
## 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145
## 401 402 403 404 405 406 407
## 1224.6145 1224.6145 1224.6145 1224.6145 1224.6145 2070.3619 2070.3619
## 408 409 430 431 432 436 437
## 2070.3619 2070.3619 3651.9097 3651.9097 3651.9097 3651.9097 3651.9097
## 438 440 441 442 443 444 445
## 3651.9097 1478.3387 1478.3387 1478.3387 1478.3387 1478.3387 1478.3387
## 446 447 448 449 450 451 452
## 1478.3387 1478.3387 1478.3387 1478.3387 1478.3387 1478.3387 1478.3387
## 453 454 455 456 457 458
## 1478.3387 1478.3387 1478.3387 1478.3387 1478.3387 1478.3387cor(Boeing$PricePremium,Boeing$SeatsTotal)## [1] 0.4708315fit<-lm(PriceEconomy~PitchDifference,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchDifference, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1461.7 -800.2 -114.0 517.7 2232.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1761.11 195.59 9.004 <2e-16 ***
## PitchDifference -66.69 27.49 -2.426 0.0158 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 969.8 on 305 degrees of freedom
## Multiple R-squared: 0.01893, Adjusted R-squared: 0.01572
## F-statistic: 5.886 on 1 and 305 DF, p-value: 0.01584Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 9 10 11 12 13 14 15 16
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 17 18 19 20 21 22 23 24
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 25 26 27 28 29 30 31 32
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 33 34 35 36 37 38 39 40
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 41 42 43 44 45 46 47 48
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 49 50 51 52 53 54 55 56
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 57 58 59 60 61 74 75 76
## 1294.256 1294.256 1294.256 1294.256 1294.256 1561.028 1561.028 1561.028
## 77 78 79 80 81 82 83 84
## 1561.028 1561.028 1561.028 1561.028 1561.028 1294.256 1294.256 1294.256
## 85 86 87 88 89 90 91 92
## 1294.256 1294.256 1294.256 1294.256 1294.256 1094.177 1094.177 1094.177
## 93 94 95 96 97 98 138 144
## 1094.177 1094.177 1094.177 1094.177 1094.177 1627.722 1294.256 1294.256
## 147 148 149 151 152 153 154 155
## 1294.256 1294.256 1294.256 1294.256 1561.028 1561.028 1561.028 1561.028
## 156 157 158 159 160 161 162 163
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 164 165 166 167 168 169 170 171
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 172 173 174 175 176 177 178 179
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 180 181 182 183 184 240 241 242
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 243 244 245 246 247 248 249 250
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 251 252 253 254 255 256 257 258
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 259 260 261 262 263 264 265 266
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 267 268 269 270 271 272 273 274
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 275 276 277 278 279 280 281 282
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1627.722 1627.722
## 283 284 285 286 287 288 289 290
## 1627.722 1627.722 1627.722 1561.028 1561.028 1561.028 1627.722 1627.722
## 291 292 293 294 295 299 300 301
## 1627.722 1561.028 1627.722 1627.722 1627.722 1627.722 1627.722 1627.722
## 304 305 307 315 316 317 318 319
## 1627.722 1627.722 1627.722 1360.949 1360.949 1360.949 1360.949 1360.949
## 320 321 322 323 324 325 326 327
## 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949
## 328 329 330 331 332 333 334 335
## 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949
## 336 337 338 339 340 341 342 343
## 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949
## 344 345 346 347 348 349 350 351
## 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949
## 352 353 354 355 356 357 358 359
## 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949
## 360 361 362 363 364 365 366 367
## 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1294.256
## 368 369 370 371 372 373 374 375
## 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256 1294.256
## 376 377 378 379 380 381 382 383
## 1294.256 1294.256 1294.256 1094.177 1094.177 1094.177 1094.177 1094.177
## 384 385 386 387 388 389 390 391
## 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177
## 392 393 394 395 396 397 398 399
## 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177
## 400 401 402 403 404 405 406 407
## 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1360.949 1360.949
## 408 409 430 431 432 436 437 438
## 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949 1360.949
## 440 441 442 443 444 445 446 447
## 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177
## 448 449 450 451 452 453 454 455
## 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177 1094.177
## 456 457 458
## 1094.177 1094.177 1094.177cor(Boeing$PriceEconomy,Boeing$PitchDifference)## [1] -0.1375936fit<-lm(PricePremium~PitchDifference,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchDifference, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1834.9 -1230.2 -154.5 1166.3 5587.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2091.54 263.69 7.932 4.13e-14 ***
## PitchDifference -37.84 37.06 -1.021 0.308
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1307 on 305 degrees of freedom
## Multiple R-squared: 0.003406, Adjusted R-squared: 0.0001383
## F-statistic: 1.042 on 1 and 305 DF, p-value: 0.3081Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 9 10 11 12 13 14 15 16
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 17 18 19 20 21 22 23 24
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 25 26 27 28 29 30 31 32
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 33 34 35 36 37 38 39 40
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 41 42 43 44 45 46 47 48
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 49 50 51 52 53 54 55 56
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 57 58 59 60 61 74 75 76
## 1826.677 1826.677 1826.677 1826.677 1826.677 1978.024 1978.024 1978.024
## 77 78 79 80 81 82 83 84
## 1978.024 1978.024 1978.024 1978.024 1978.024 1826.677 1826.677 1826.677
## 85 86 87 88 89 90 91 92
## 1826.677 1826.677 1826.677 1826.677 1826.677 1713.166 1713.166 1713.166
## 93 94 95 96 97 98 138 144
## 1713.166 1713.166 1713.166 1713.166 1713.166 2015.861 1826.677 1826.677
## 147 148 149 151 152 153 154 155
## 1826.677 1826.677 1826.677 1826.677 1978.024 1978.024 1978.024 1978.024
## 156 157 158 159 160 161 162 163
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 164 165 166 167 168 169 170 171
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 172 173 174 175 176 177 178 179
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 180 181 182 183 184 240 241 242
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 243 244 245 246 247 248 249 250
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 251 252 253 254 255 256 257 258
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 259 260 261 262 263 264 265 266
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 267 268 269 270 271 272 273 274
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 275 276 277 278 279 280 281 282
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 2015.861 2015.861
## 283 284 285 286 287 288 289 290
## 2015.861 2015.861 2015.861 1978.024 1978.024 1978.024 2015.861 2015.861
## 291 292 293 294 295 299 300 301
## 2015.861 1978.024 2015.861 2015.861 2015.861 2015.861 2015.861 2015.861
## 304 305 307 315 316 317 318 319
## 2015.861 2015.861 2015.861 1864.514 1864.514 1864.514 1864.514 1864.514
## 320 321 322 323 324 325 326 327
## 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514
## 328 329 330 331 332 333 334 335
## 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514
## 336 337 338 339 340 341 342 343
## 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514
## 344 345 346 347 348 349 350 351
## 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514
## 352 353 354 355 356 357 358 359
## 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514
## 360 361 362 363 364 365 366 367
## 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1826.677
## 368 369 370 371 372 373 374 375
## 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677 1826.677
## 376 377 378 379 380 381 382 383
## 1826.677 1826.677 1826.677 1713.166 1713.166 1713.166 1713.166 1713.166
## 384 385 386 387 388 389 390 391
## 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166
## 392 393 394 395 396 397 398 399
## 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166
## 400 401 402 403 404 405 406 407
## 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1864.514 1864.514
## 408 409 430 431 432 436 437 438
## 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514 1864.514
## 440 441 442 443 444 445 446 447
## 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166
## 448 449 450 451 452 453 454 455
## 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166 1713.166
## 456 457 458
## 1713.166 1713.166 1713.166cor(Boeing$PricePremium,Boeing$PitchDifference)## [1] -0.05835906fit<-lm(PriceEconomy~WidthDifference,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthDifference, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1399.5 -799.3 -168.4 463.7 2329.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1557.50 90.88 17.138 < 2e-16 ***
## WidthDifference -147.07 42.39 -3.469 0.000597 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 960.3 on 305 degrees of freedom
## Multiple R-squared: 0.03797, Adjusted R-squared: 0.03481
## F-statistic: 12.04 on 1 and 305 DF, p-value: 0.0005968Boeing$PriceEconomy## [1] 2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 1705 1911 2378 1750
## [15] 1750 1750 1813 1813 1813 1634 1634 1634 1651 1651 1651 1542 1566 1566
## [29] 1356 1356 1356 1242 1242 1242 1242 940 940 940 1224 1224 1224 1224
## [43] 1127 1127 1127 1123 1123 1123 509 509 509 1476 2156 2156 2156 1634
## [57] 1634 1634 1038 1038 509 158 189 228 222 216 391 349 581 1444
## [71] 1444 1444 1444 1824 1824 1824 1823 354 354 354 354 464 464 464
## [85] 489 458 137 109 77 77 69 65 298 423 483 713 574 574
## [99] 574 574 1086 1086 1086 1247 1781 1781 1781 1781 1580 1580 1580 1580
## [113] 1903 1096 2445 2445 2445 2445 975 2369 1811 1811 1811 1811 1356 1651
## [127] 1651 2775 2230 2230 2230 2356 2356 2356 2356 1562 1562 1562 2281 2281
## [141] 2281 2281 1813 1813 1813 1140 1609 1609 1609 1632 1632 1632 1140 1736
## [155] 1736 1736 846 846 937 1485 891 1323 1023 1023 757 533 288 288
## [169] 363 363 363 413 413 413 413 413 340 423 328 328 166 243
## [183] 626 354 293 636 349 794 794 794 794 1215 1215 1215 876 609
## [197] 609 1406 1406 1406 1247 1247 1247 563 563 563 563 1431 1431 1431
## [211] 1431 2918 2918 2918 2581 2860 3026 3026 3026 3057 3057 3057 3414 3414
## [225] 3414 3414 3215 3215 3215 3215 3480 3480 3480 3593 3593 3159 3159 3159
## [239] 3159 3102 3102 3102 2166 2166 2166 649 575 575 797 524 582 167
## [253] 167 167 139 149 197 211 139 118 118 118 108 108 108 297
## [267] 234 156 156 324 147 127 154 154 154 154 322 594 648 648
## [281] 700 1094 2996 2996 2996 2979 3593 3593 201 148 148 187 187 187
## [295] 187 245 234 172 172 172 293 281 295 380 380 505 510fitted(fit)## 1 2 3 4 5 6 7 8
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 9 10 11 12 13 14 15 16
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 17 18 19 20 21 22 23 24
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 25 26 27 28 29 30 31 32
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 33 34 35 36 37 38 39 40
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 41 42 43 44 45 46 47 48
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 49 50 51 52 53 54 55 56
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 57 58 59 60 61 74 75 76
## 1410.424 1410.424 1410.424 1410.424 1410.424 1557.498 1557.498 1557.498
## 77 78 79 80 81 82 83 84
## 1557.498 1557.498 1557.498 1557.498 1557.498 1410.424 1410.424 1410.424
## 85 86 87 88 89 90 91 92
## 1410.424 1410.424 1410.424 1410.424 1410.424 969.202 969.202 969.202
## 93 94 95 96 97 98 138 144
## 969.202 969.202 969.202 969.202 969.202 1557.498 1410.424 1410.424
## 147 148 149 151 152 153 154 155
## 1410.424 1410.424 1410.424 1410.424 1557.498 1557.498 1557.498 1557.498
## 156 157 158 159 160 161 162 163
## 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276
## 164 165 166 167 168 169 170 171
## 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276
## 172 173 174 175 176 177 178 179
## 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276 1116.276
## 180 181 182 183 184 240 241 242
## 1116.276 1116.276 1116.276 1116.276 1116.276 1410.424 1410.424 1410.424
## 243 244 245 246 247 248 249 250
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 251 252 253 254 255 256 257 258
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 259 260 261 262 263 264 265 266
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 267 268 269 270 271 272 273 274
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 275 276 277 278 279 280 281 282
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1557.498 1557.498
## 283 284 285 286 287 288 289 290
## 1557.498 1557.498 1557.498 1557.498 1557.498 1557.498 1557.498 1557.498
## 291 292 293 294 295 299 300 301
## 1557.498 1557.498 1557.498 1557.498 1557.498 1557.498 1557.498 1557.498
## 304 305 307 315 316 317 318 319
## 1557.498 1557.498 1557.498 1410.424 1410.424 1410.424 1410.424 1410.424
## 320 321 322 323 324 325 326 327
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 328 329 330 331 332 333 334 335
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 336 337 338 339 340 341 342 343
## 1410.424 1410.424 1410.424 1263.350 1263.350 1263.350 1263.350 1263.350
## 344 345 346 347 348 349 350 351
## 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350
## 352 353 354 355 356 357 358 359
## 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350
## 360 361 362 363 364 365 366 367
## 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350 1263.350 1410.424
## 368 369 370 371 372 373 374 375
## 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 376 377 378 379 380 381 382 383
## 1410.424 1410.424 1410.424 969.202 969.202 969.202 969.202 969.202
## 384 385 386 387 388 389 390 391
## 969.202 969.202 969.202 969.202 969.202 969.202 969.202 969.202
## 392 393 394 395 396 397 398 399
## 969.202 969.202 969.202 969.202 969.202 969.202 969.202 969.202
## 400 401 402 403 404 405 406 407
## 969.202 969.202 969.202 969.202 969.202 969.202 1263.350 1263.350
## 408 409 430 431 432 436 437 438
## 1263.350 1263.350 1410.424 1410.424 1410.424 1410.424 1410.424 1410.424
## 440 441 442 443 444 445 446 447
## 969.202 969.202 969.202 969.202 969.202 969.202 969.202 969.202
## 448 449 450 451 452 453 454 455
## 969.202 969.202 969.202 969.202 969.202 969.202 969.202 969.202
## 456 457 458
## 969.202 969.202 969.202cor(Boeing$PriceEconomy,Boeing$WidthDifference)## [1] -0.1948488fit<-lm(PricePremium~WidthDifference,data = Boeing)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthDifference, data = Boeing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1920 -1056 -157 1058 5473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2092.61 122.53 17.078 <2e-16 ***
## WidthDifference -151.62 57.16 -2.653 0.0084 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1295 on 305 degrees of freedom
## Multiple R-squared: 0.02255, Adjusted R-squared: 0.01935
## F-statistic: 7.037 on 1 and 305 DF, p-value: 0.008403Boeing$PricePremium## [1] 3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 2989 2989 2989 2656
## [15] 2656 2656 2504 2504 2504 2195 2195 2195 2191 2191 2191 2084 2084 2084
## [29] 1820 1820 1820 1764 1764 1764 1764 1548 1548 1548 1512 1512 1512 1512
## [43] 1317 1317 1317 1213 1213 1213 773 773 773 2997 2933 2933 2933 2195
## [57] 2195 2195 1259 1259 818 173 204 243 237 231 406 364 596 2982
## [71] 2982 2982 2982 2549 2549 2549 2548 524 524 524 524 616 616 616
## [85] 616 497 172 141 99 99 97 86 337 467 527 757 1619 1619
## [99] 1619 1619 2964 2964 2964 2964 3509 3509 3509 3509 3019 3019 3019 3019
## [113] 3509 1710 3694 3694 3694 3694 1465 3540 2531 2531 2531 2531 1710 3509
## [127] 3509 3509 3227 3227 3227 3200 3200 3200 3200 3099 3099 3099 3025 3025
## [141] 3025 3025 2472 2472 2472 2423 2292 2292 2292 2278 2278 2278 2049 1866
## [155] 1866 1866 1784 1784 1784 1784 1603 1550 1199 1199 912 837 327 327
## [169] 407 407 407 457 457 457 457 457 379 467 362 362 181 262
## [183] 670 378 308 660 364 1671 1452 1452 1408 1947 1947 1947 1356 900
## [197] 900 1584 1584 1584 1407 1407 1407 619 619 619 619 1564 1564 1564
## [211] 1564 3972 3972 3972 2781 3063 3226 3226 3226 3167 3167 3167 3524 3524
## [225] 3524 3524 3325 3325 3325 3325 3589 3589 3589 3702 3702 3243 3243 3243
## [239] 3243 7414 7414 7414 2470 2470 2470 1152 853 853 826 797 797 483
## [253] 483 483 398 398 520 534 318 267 267 267 228 228 228 620
## [267] 483 318 318 620 267 228 267 267 267 267 483 696 1710 1710
## [281] 1710 1710 3196 3196 3196 3088 3702 3702 545 397 397 430 430 430
## [295] 430 545 483 304 304 304 483 451 464 550 550 696 569fitted(fit)## 1 2 3 4 5 6 7 8
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 9 10 11 12 13 14 15 16
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 17 18 19 20 21 22 23 24
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 25 26 27 28 29 30 31 32
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 33 34 35 36 37 38 39 40
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 41 42 43 44 45 46 47 48
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 49 50 51 52 53 54 55 56
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 57 58 59 60 61 74 75 76
## 1940.995 1940.995 1940.995 1940.995 1940.995 2092.611 2092.611 2092.611
## 77 78 79 80 81 82 83 84
## 2092.611 2092.611 2092.611 2092.611 2092.611 1940.995 1940.995 1940.995
## 85 86 87 88 89 90 91 92
## 1940.995 1940.995 1940.995 1940.995 1940.995 1486.145 1486.145 1486.145
## 93 94 95 96 97 98 138 144
## 1486.145 1486.145 1486.145 1486.145 1486.145 2092.611 1940.995 1940.995
## 147 148 149 151 152 153 154 155
## 1940.995 1940.995 1940.995 1940.995 2092.611 2092.611 2092.611 2092.611
## 156 157 158 159 160 161 162 163
## 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762
## 164 165 166 167 168 169 170 171
## 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762
## 172 173 174 175 176 177 178 179
## 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762 1637.762
## 180 181 182 183 184 240 241 242
## 1637.762 1637.762 1637.762 1637.762 1637.762 1940.995 1940.995 1940.995
## 243 244 245 246 247 248 249 250
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 251 252 253 254 255 256 257 258
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 259 260 261 262 263 264 265 266
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 267 268 269 270 271 272 273 274
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 275 276 277 278 279 280 281 282
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 2092.611 2092.611
## 283 284 285 286 287 288 289 290
## 2092.611 2092.611 2092.611 2092.611 2092.611 2092.611 2092.611 2092.611
## 291 292 293 294 295 299 300 301
## 2092.611 2092.611 2092.611 2092.611 2092.611 2092.611 2092.611 2092.611
## 304 305 307 315 316 317 318 319
## 2092.611 2092.611 2092.611 1940.995 1940.995 1940.995 1940.995 1940.995
## 320 321 322 323 324 325 326 327
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 328 329 330 331 332 333 334 335
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 336 337 338 339 340 341 342 343
## 1940.995 1940.995 1940.995 1789.378 1789.378 1789.378 1789.378 1789.378
## 344 345 346 347 348 349 350 351
## 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378
## 352 353 354 355 356 357 358 359
## 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378
## 360 361 362 363 364 365 366 367
## 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378 1789.378 1940.995
## 368 369 370 371 372 373 374 375
## 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 376 377 378 379 380 381 382 383
## 1940.995 1940.995 1940.995 1486.145 1486.145 1486.145 1486.145 1486.145
## 384 385 386 387 388 389 390 391
## 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145
## 392 393 394 395 396 397 398 399
## 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145
## 400 401 402 403 404 405 406 407
## 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1789.378 1789.378
## 408 409 430 431 432 436 437 438
## 1789.378 1789.378 1940.995 1940.995 1940.995 1940.995 1940.995 1940.995
## 440 441 442 443 444 445 446 447
## 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145
## 448 449 450 451 452 453 454 455
## 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145 1486.145
## 456 457 458
## 1486.145 1486.145 1486.145cor(Boeing$PricePremium,Boeing$WidthDifference)## [1] -0.1501705AirBus
Analyse all about AirBus Aircrafts:-
Airbus <- airline[ which(airline$Aircraft=='AirBus'),]
View(Airbus)
summary(Airbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance:36 AirBus:151 Min. : 1.250 Aug:39
## British :47 Boeing: 0 1st Qu.: 4.500 Jul:25
## Delta :12 Median : 8.000 Oct:41
## Jet : 7 Mean : 7.436 Sep:46
## Singapore:16 3rd Qu.: 9.500
## Virgin :33 Max. :13.330
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 6 Min. :120.0 Min. :18.00 Min. :31.00
## International:145 1st Qu.:147.0 1st Qu.:21.00 1st Qu.:31.00
## Median :233.0 Median :38.00 Median :31.00
## Mean :245.6 Mean :39.15 Mean :31.44
## 3rd Qu.:303.0 3rd Qu.:55.00 3rd Qu.:32.00
## Max. :389.0 Max. :55.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 74
## 1st Qu.:38.00 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 409
## Median :38.00 Median :18.00 Median :19.00 Median :1434
## Mean :37.85 Mean :18.07 Mean :19.54 Mean :1370
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:2052
## Max. :38.00 Max. :19.00 Max. :21.00 Max. :3220
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 97 Min. :0.0200 Min. :138.0 Min. :2.000
## 1st Qu.: 464 1st Qu.:0.0800 1st Qu.:168.0 1st Qu.:6.000
## Median :2409 Median :0.3000 Median :271.0 Median :7.000
## Mean :1870 Mean :0.4148 Mean :284.7 Mean :6.411
## 3rd Qu.:2982 3rd Qu.:0.6100 3rd Qu.:358.0 3rd Qu.:7.000
## Max. :3563 Max. :1.5600 Max. :427.0 Max. :7.000
## WidthDifference PercentPremiumSeats
## Min. :0.000 Min. : 8.90
## 1st Qu.:1.000 1st Qu.:12.50
## Median :1.000 Median :14.02
## Mean :1.477 Mean :13.84
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :3.000 Max. :20.60Check the all the means now all Airbus aircrafts
mean(Airbus$PriceEconomy)## [1] 1369.954mean(Airbus$PricePremium)## [1] 1869.503mean(Airbus$FlightDuration)## [1] 7.436026mean(Airbus$PitchEconomy)## [1] 31.43709mean(Airbus$PitchPremium)## [1] 37.84768mean(Airbus$WidthEconomy)## [1] 18.06623mean(Airbus$WidthPremium)## [1] 19.54305mean(Airbus$PriceRelative)## [1] 0.4147682mean(Airbus$PitchDifference)## [1] 6.410596mean(Airbus$WidthDifference)## [1] 1.476821View(Airbus)
summary(Airbus)## Airline Aircraft FlightDuration TravelMonth
## AirFrance:36 AirBus:151 Min. : 1.250 Aug:39
## British :47 Boeing: 0 1st Qu.: 4.500 Jul:25
## Delta :12 Median : 8.000 Oct:41
## Jet : 7 Mean : 7.436 Sep:46
## Singapore:16 3rd Qu.: 9.500
## Virgin :33 Max. :13.330
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 6 Min. :120.0 Min. :18.00 Min. :31.00
## International:145 1st Qu.:147.0 1st Qu.:21.00 1st Qu.:31.00
## Median :233.0 Median :38.00 Median :31.00
## Mean :245.6 Mean :39.15 Mean :31.44
## 3rd Qu.:303.0 3rd Qu.:55.00 3rd Qu.:32.00
## Max. :389.0 Max. :55.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 74
## 1st Qu.:38.00 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 409
## Median :38.00 Median :18.00 Median :19.00 Median :1434
## Mean :37.85 Mean :18.07 Mean :19.54 Mean :1370
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:2052
## Max. :38.00 Max. :19.00 Max. :21.00 Max. :3220
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 97 Min. :0.0200 Min. :138.0 Min. :2.000
## 1st Qu.: 464 1st Qu.:0.0800 1st Qu.:168.0 1st Qu.:6.000
## Median :2409 Median :0.3000 Median :271.0 Median :7.000
## Mean :1870 Mean :0.4148 Mean :284.7 Mean :6.411
## 3rd Qu.:2982 3rd Qu.:0.6100 3rd Qu.:358.0 3rd Qu.:7.000
## Max. :3563 Max. :1.5600 Max. :427.0 Max. :7.000
## WidthDifference PercentPremiumSeats
## Min. :0.000 Min. : 8.90
## 1st Qu.:1.000 1st Qu.:12.50
## Median :1.000 Median :14.02
## Mean :1.477 Mean :13.84
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :3.000 Max. :20.60mean(Airbus$PriceEconomy)## [1] 1369.954mean(Airbus$PricePremium)## [1] 1869.503library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(Airbus$PriceEconomy,Airbus$TravelMonth,mean))
y2<-c(by(Airbus$PricePremium,Airbus$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')mean(Airbus$PriceEconomy)## [1] 1369.954mean(Airbus$PricePremium)## [1] 1869.503library(plotly)
x<-c('British','Virgin','Delta','Jet','AirFrance','Singapore')
y1<-c(by(Airbus$PriceEconomy,Airbus$Airline,mean))
y2<-c(by(Airbus$PricePremium,Airbus$Airline,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Airlines", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')fit<-lm(PriceEconomy~FlightDuration,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ FlightDuration, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1815.3 -535.7 -167.5 457.3 1587.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 170.92 173.86 0.983 0.327
## FlightDuration 161.25 21.39 7.540 4.26e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 863.6 on 149 degrees of freedom
## Multiple R-squared: 0.2762, Adjusted R-squared: 0.2713
## F-statistic: 56.85 on 1 and 149 DF, p-value: 4.258e-12Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1460.8925 1460.8925 1460.8925 1460.8925 1594.7272 1594.7272 1594.7272
## 69 70 71 72 73 99 100
## 1594.7272 1312.5456 1312.5456 1312.5456 1420.5808 1970.4318 1970.4318
## 101 102 103 104 105 106 107
## 1970.4318 1970.4318 1864.0091 1864.0091 1864.0091 1864.0091 2280.0254
## 108 109 110 111 112 113 114
## 2280.0254 2280.0254 1970.4318 1970.4318 1970.4318 828.8057 748.1823
## 115 116 117 118 119 120 121
## 748.1823 559.5238 694.9709 694.9709 748.1823 599.8354 559.5238
## 122 123 124 125 126 127 128
## 559.5238 559.5238 694.9709 466.0007 748.1823 896.5292 559.5238
## 129 130 131 132 133 134 135
## 896.5292 466.0007 559.5238 748.1823 828.8057 627.2474 896.5292
## 136 137 139 140 141 142 143
## 372.4777 828.8057 559.5238 466.0007 466.0007 372.4777 372.4777
## 145 146 150 185 186 187 188
## 748.1823 627.2474 559.5238 1514.1039 1514.1039 1702.7624 1702.7624
## 189 190 191 192 193 194 195
## 1702.7624 1514.1039 1285.1336 1285.1336 1285.1336 1285.1336 1231.9222
## 196 197 198 199 200 201 202
## 1231.9222 1231.9222 1231.9222 1849.4969 1849.4969 1849.4969 1997.8438
## 203 204 205 206 207 208 209
## 1997.8438 1365.7570 1365.7570 1365.7570 1365.7570 1312.5456 1420.5808
## 210 211 212 213 214 215 216
## 1420.5808 1420.5808 1702.7624 1702.7624 1702.7624 1514.1039 1514.1039
## 217 218 219 220 221 222 223
## 1514.1039 1514.1039 1514.1039 1514.1039 1514.1039 1514.1039 1352.8572
## 224 225 226 227 228 229 230
## 1352.8572 1352.8572 1272.2339 1272.2339 1607.6269 1607.6269 1607.6269
## 231 232 233 234 235 236 237
## 1473.7922 1651.1635 1651.1635 1662.4508 1662.4508 1647.9386 1647.9386
## 238 239 296 297 298 302 303
## 1662.4508 1662.4508 485.3503 535.3368 582.0983 462.7758 466.0007
## 306 308 309 310 311 312 313
## 461.1633 1702.7624 1702.7624 1702.7624 1607.6269 1607.6269 1702.7624
## 314 410 411 412 413 414 415
## 1607.6269 2320.3370 2320.3370 2320.3370 2320.3370 1164.1987 1164.1987
## 416 417 418 419 420 421 422
## 1164.1987 1164.1987 2212.3018 2212.3018 2212.3018 2212.3018 1219.0225
## 423 424 425 426 427 428 429
## 1219.0225 1219.0225 1219.0225 2267.1257 2267.1257 1380.2692 1380.2692
## 433 434 435 439
## 1527.0036 1541.5158 1541.5158 2267.1257cor(Airbus$PriceEconomy,Airbus$FlightDuration)## [1] 0.5255296fit<-lm(PriceEconomy~SeatsEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1754.6 -882.6 50.9 780.9 2478.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2446.7103 256.2089 9.55 < 2e-16 ***
## SeatsEconomy -4.3846 0.9942 -4.41 1.97e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 954.7 on 149 degrees of freedom
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.1095
## F-statistic: 19.45 on 1 and 149 DF, p-value: 1.967e-05Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567
## 69 70 71 72 73 99 100
## 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567 1118.1723 1118.1723
## 101 102 103 104 105 106 107
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 108 109 110 111 112 113 114
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 115 116 117 118 119 120 121
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 122 123 124 125 126 127 128
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 129 130 131 132 133 134 135
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 136 137 139 140 141 142 143
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 145 146 150 185 186 187 188
## 1118.1723 1118.1723 1078.7107 1425.0952 1425.0952 1425.0952 1425.0952
## 189 190 191 192 193 194 195
## 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952
## 196 197 198 199 200 201 202
## 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952
## 203 204 205 206 207 208 209
## 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952
## 210 211 212 213 214 215 216
## 1425.0952 1425.0952 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 217 218 219 220 221 222 223
## 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 224 225 226 227 228 229 230
## 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 231 232 233 234 235 236 237
## 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 238 239 296 297 298 302 303
## 1802.1721 1802.1721 1920.5566 1920.5566 1850.4028 1920.5566 1920.5566
## 306 308 309 310 311 312 313
## 1920.5566 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 314 410 411 412 413 414 415
## 1802.1721 986.6338 986.6338 986.6338 986.6338 986.6338 986.6338
## 416 417 418 419 420 421 422
## 986.6338 986.6338 986.6338 986.6338 986.6338 986.6338 986.6338
## 423 424 425 426 427 428 429
## 986.6338 986.6338 986.6338 741.0955 741.0955 741.0955 741.0955
## 433 434 435 439
## 741.0955 741.0955 741.0955 741.0955cor(Airbus$PriceEconomy,Airbus$SeatsEconomy)## [1] -0.3398079fit<-lm(PriceEconomy~PriceRelative,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1358.6 -1099.9 380.9 735.6 1662.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1567.5 120.2 13.043 <2e-16 ***
## PriceRelative -476.3 213.5 -2.231 0.0272 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 998.6 on 149 degrees of freedom
## Multiple R-squared: 0.03233, Adjusted R-squared: 0.02584
## F-statistic: 4.978 on 1 and 149 DF, p-value: 0.02716Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1219.8235 1219.8235 1219.8235 1219.8235 1381.7496 1381.7496 1381.7496
## 69 70 71 72 73 99 100
## 1381.7496 1443.6625 1443.6625 1443.6625 1519.8630 1334.1243 1334.1243
## 101 102 103 104 105 106 107
## 1334.1243 1334.1243 1134.0979 1134.0979 1134.0979 1134.0979 1343.6493
## 108 109 110 111 112 113 114
## 1343.6493 1343.6493 962.6468 962.6468 1396.0372 1538.9131 1519.8630
## 115 116 117 118 119 120 121
## 1519.8630 1548.4382 1515.1005 1515.1005 1529.3881 1524.6255 1543.6757
## 122 123 124 125 126 127 128
## 1543.6757 1515.1005 1500.8129 1486.5253 1491.2878 1496.0504 1534.1506
## 129 130 131 132 133 134 135
## 1486.5253 1481.7628 1500.8129 1505.5754 1491.2878 1481.7628 1481.7628
## 136 137 139 140 141 142 143
## 1448.4250 1472.2377 1477.0002 1457.9501 1457.9501 1424.6124 1424.6124
## 145 146 150 185 186 187 188
## 1448.4250 1429.3749 1419.8498 1348.4119 1348.4119 1386.5121 1386.5121
## 189 190 191 192 193 194 195
## 1386.5121 1424.6124 1053.1349 1053.1349 1053.1349 1053.1349 1076.9475
## 196 197 198 199 200 201 202
## 1076.9475 1076.9475 1076.9475 1167.4356 1167.4356 1167.4356 1334.1243
## 203 204 205 206 207 208 209
## 1334.1243 1372.2245 1372.2245 1372.2245 1372.2245 1443.6625 1519.8630
## 210 211 212 213 214 215 216
## 1519.8630 1519.8630 824.5333 1010.2721 1267.4488 1529.3881 1529.3881
## 217 218 219 220 221 222 223
## 1529.3881 1529.3881 1529.3881 1529.3881 1529.3881 1529.3881 1529.3881
## 224 225 226 227 228 229 230
## 1529.3881 1529.3881 1534.1506 1534.1506 1534.1506 1534.1506 1534.1506
## 231 232 233 234 235 236 237
## 1548.4382 1553.2007 1553.2007 1553.2007 1553.2007 1553.2007 1553.2007
## 238 239 296 297 298 302 303
## 1553.2007 1553.2007 1524.6255 1529.3881 1529.3881 1543.6757 1543.6757
## 306 308 309 310 311 312 313
## 1548.4382 853.1085 1110.2853 1176.9607 1367.4620 1367.4620 1376.9871
## 314 410 411 412 413 414 415
## 1386.5121 1095.9977 1095.9977 1095.9977 1095.9977 1095.9977 1095.9977
## 416 417 418 419 420 421 422
## 1095.9977 1095.9977 1276.9739 1276.9739 1276.9739 1276.9739 1276.9739
## 423 424 425 426 427 428 429
## 1276.9739 1276.9739 1276.9739 1015.0346 1015.0346 1529.3881 1529.3881
## 433 434 435 439
## 1548.4382 1548.4382 1548.4382 1557.9633cor(Airbus$PriceEconomy,Airbus$PriceRelative)## [1] -0.1798069fit<-lm(PriceEconomy~PercentPremiumSeats,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1257.06 -1003.28 70.07 911.04 1749.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1839.00 414.53 4.436 1.77e-05 ***
## PercentPremiumSeats -33.89 29.35 -1.154 0.25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1011 on 149 degrees of freedom
## Multiple R-squared: 0.008865, Adjusted R-squared: 0.002214
## F-statistic: 1.333 on 1 and 149 DF, p-value: 0.2502Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68 69
## 1140.963 1140.963 1140.963 1140.963 1140.963 1140.963 1140.963 1140.963
## 70 71 72 73 99 100 101 102
## 1140.963 1140.963 1140.963 1140.963 1318.522 1318.522 1318.522 1318.522
## 103 104 105 106 107 108 109 110
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 111 112 113 114 115 116 117 118
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 119 120 121 122 123 124 125 126
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 127 128 129 130 131 132 133 134
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 135 136 137 139 140 141 142 143
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 145 146 150 185 186 187 188 189
## 1318.522 1318.522 1331.060 1363.928 1363.928 1363.928 1363.928 1363.928
## 190 191 192 193 194 195 196 197
## 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928
## 198 199 200 201 202 203 204 205
## 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928
## 206 207 208 209 210 211 212 213
## 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1415.434 1415.434
## 214 215 216 217 218 219 220 221
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434
## 222 223 224 225 226 227 228 229
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434
## 230 231 232 233 234 235 236 237
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434
## 238 239 296 297 298 302 303 306
## 1415.434 1415.434 1397.136 1397.136 1404.591 1397.136 1397.136 1397.136
## 308 309 310 311 312 313 314 410
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1508.280
## 411 412 413 414 415 416 417 418
## 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280
## 419 420 421 422 423 424 425 426
## 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1537.421
## 427 428 429 433 434 435 439
## 1537.421 1537.421 1537.421 1537.421 1537.421 1537.421 1537.421cor(Airbus$PriceEconomy,Airbus$PercentPremiumSeats)## [1] -0.09415658fit<-lm(PricePremium~FlightDuration,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ FlightDuration, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2300.13 -574.78 65.19 841.40 1341.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.53 194.02 0.307 0.759
## FlightDuration 243.41 23.87 10.199 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 963.8 on 149 degrees of freedom
## Multiple R-squared: 0.4111, Adjusted R-squared: 0.4072
## F-statistic: 104 on 1 and 149 DF, p-value: < 2.2e-16Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68
## 2006.7778 2006.7778 2006.7778 2006.7778 2208.8047 2208.8047 2208.8047
## 69 70 71 72 73 99 100
## 2208.8047 1782.8443 1782.8443 1782.8443 1945.9263 2775.9406 2775.9406
## 101 102 103 104 105 106 107
## 2775.9406 2775.9406 2615.2927 2615.2927 2615.2927 2615.2927 3243.2800
## 108 109 110 111 112 113 114
## 3243.2800 3243.2800 2775.9406 2775.9406 2775.9406 1052.6265 930.9235
## 115 116 117 118 119 120 121
## 930.9235 646.1386 850.5996 850.5996 930.9235 706.9901 646.1386
## 122 123 124 125 126 127 128
## 646.1386 646.1386 850.5996 504.9631 930.9235 1154.8570 646.1386
## 129 130 131 132 133 134 135
## 1154.8570 504.9631 646.1386 930.9235 1052.6265 748.3691 1154.8570
## 136 137 139 140 141 142 143
## 363.7877 1052.6265 646.1386 504.9631 504.9631 363.7877 363.7877
## 145 146 150 185 186 187 188
## 930.9235 748.3691 646.1386 2087.1018 2087.1018 2371.8867 2371.8867
## 189 190 191 192 193 194 195
## 2371.8867 2087.1018 1741.4653 1741.4653 1741.4653 1741.4653 1661.1414
## 196 197 198 199 200 201 202
## 1661.1414 1661.1414 1661.1414 2593.3861 2593.3861 2593.3861 2817.3196
## 203 204 205 206 207 208 209
## 2817.3196 1863.1683 1863.1683 1863.1683 1863.1683 1782.8443 1945.9263
## 210 211 212 213 214 215 216
## 1945.9263 1945.9263 2371.8867 2371.8867 2371.8867 2087.1018 2087.1018
## 217 218 219 220 221 222 223
## 2087.1018 2087.1018 2087.1018 2087.1018 2087.1018 2087.1018 1843.6958
## 224 225 226 227 228 229 230
## 1843.6958 1843.6958 1721.9929 1721.9929 2228.2772 2228.2772 2228.2772
## 231 232 233 234 235 236 237
## 2026.2503 2293.9968 2293.9968 2311.0352 2311.0352 2289.1287 2289.1287
## 238 239 296 297 298 302 303
## 2311.0352 2311.0352 534.1719 609.6277 680.2154 500.0950 504.9631
## 306 308 309 310 311 312 313
## 497.6610 2371.8867 2371.8867 2371.8867 2228.2772 2228.2772 2371.8867
## 314 410 411 412 413 414 415
## 2228.2772 3304.1315 3304.1315 3304.1315 3304.1315 1558.9109 1558.9109
## 416 417 418 419 420 421 422
## 1558.9109 1558.9109 3141.0495 3141.0495 3141.0495 3141.0495 1641.6689
## 423 424 425 426 427 428 429
## 1641.6689 1641.6689 1641.6689 3223.8075 3223.8075 1885.0748 1885.0748
## 433 434 435 439
## 2106.5743 2128.4808 2128.4808 3223.8075cor(Airbus$PricePremium,Airbus$FlightDuration)## [1] 0.6411862fit<-lm(PriceEconomy~SeatsEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1754.6 -882.6 50.9 780.9 2478.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2446.7103 256.2089 9.55 < 2e-16 ***
## SeatsEconomy -4.3846 0.9942 -4.41 1.97e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 954.7 on 149 degrees of freedom
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.1095
## F-statistic: 19.45 on 1 and 149 DF, p-value: 1.967e-05Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68
## 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567
## 69 70 71 72 73 99 100
## 1635.5567 1635.5567 1635.5567 1635.5567 1635.5567 1118.1723 1118.1723
## 101 102 103 104 105 106 107
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 108 109 110 111 112 113 114
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 115 116 117 118 119 120 121
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 122 123 124 125 126 127 128
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 129 130 131 132 133 134 135
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 136 137 139 140 141 142 143
## 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723 1118.1723
## 145 146 150 185 186 187 188
## 1118.1723 1118.1723 1078.7107 1425.0952 1425.0952 1425.0952 1425.0952
## 189 190 191 192 193 194 195
## 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952
## 196 197 198 199 200 201 202
## 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952
## 203 204 205 206 207 208 209
## 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952 1425.0952
## 210 211 212 213 214 215 216
## 1425.0952 1425.0952 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 217 218 219 220 221 222 223
## 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 224 225 226 227 228 229 230
## 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 231 232 233 234 235 236 237
## 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 238 239 296 297 298 302 303
## 1802.1721 1802.1721 1920.5566 1920.5566 1850.4028 1920.5566 1920.5566
## 306 308 309 310 311 312 313
## 1920.5566 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721 1802.1721
## 314 410 411 412 413 414 415
## 1802.1721 986.6338 986.6338 986.6338 986.6338 986.6338 986.6338
## 416 417 418 419 420 421 422
## 986.6338 986.6338 986.6338 986.6338 986.6338 986.6338 986.6338
## 423 424 425 426 427 428 429
## 986.6338 986.6338 986.6338 741.0955 741.0955 741.0955 741.0955
## 433 434 435 439
## 741.0955 741.0955 741.0955 741.0955cor(Airbus$PricePremium,Airbus$SeatsEconomy)## [1] -0.2465987fit<-lm(PriceEconomy~SeatsPremium,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsPremium, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1851.61 -776.10 70.76 733.24 1814.76
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2568.750 233.175 11.016 < 2e-16 ***
## SeatsPremium -30.619 5.635 -5.434 2.2e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 927.4 on 149 degrees of freedom
## Multiple R-squared: 0.1654, Adjusted R-squared: 0.1598
## F-statistic: 29.52 on 1 and 149 DF, p-value: 2.202e-07Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68
## 1099.0484 1099.0484 1099.0484 1099.0484 1099.0484 1099.0484 1099.0484
## 69 70 71 72 73 99 100
## 1099.0484 1099.0484 1099.0484 1099.0484 1099.0484 884.7169 884.7169
## 101 102 103 104 105 106 107
## 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169
## 108 109 110 111 112 113 114
## 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169
## 115 116 117 118 119 120 121
## 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169
## 122 123 124 125 126 127 128
## 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169
## 129 130 131 132 133 134 135
## 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169
## 136 137 139 140 141 142 143
## 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169 884.7169
## 145 146 150 185 186 187 188
## 884.7169 884.7169 884.7169 1405.2362 1405.2362 1405.2362 1405.2362
## 189 190 191 192 193 194 195
## 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362
## 196 197 198 199 200 201 202
## 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362
## 203 204 205 206 207 208 209
## 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362 1405.2362
## 210 211 212 213 214 215 216
## 1405.2362 1405.2362 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555
## 217 218 219 220 221 222 223
## 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555
## 224 225 226 227 228 229 230
## 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555
## 231 232 233 234 235 236 237
## 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555
## 238 239 296 297 298 302 303
## 1925.7555 1925.7555 2017.6119 2017.6119 1956.3743 2017.6119 2017.6119
## 306 308 309 310 311 312 313
## 2017.6119 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555 1925.7555
## 314 410 411 412 413 414 415
## 1925.7555 1466.4738 1466.4738 1466.4738 1466.4738 1466.4738 1466.4738
## 416 417 418 419 420 421 422
## 1466.4738 1466.4738 1466.4738 1466.4738 1466.4738 1466.4738 1466.4738
## 423 424 425 426 427 428 429
## 1466.4738 1466.4738 1466.4738 1405.2362 1405.2362 1405.2362 1405.2362
## 433 434 435 439
## 1405.2362 1405.2362 1405.2362 1405.2362cor(Airbus$PricePremium,Airbus$SeatsPremium)## [1] -0.2739066fit<-lm(PriceEconomy~PriceRelative,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PriceRelative, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1358.6 -1099.9 380.9 735.6 1662.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1567.5 120.2 13.043 <2e-16 ***
## PriceRelative -476.3 213.5 -2.231 0.0272 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 998.6 on 149 degrees of freedom
## Multiple R-squared: 0.03233, Adjusted R-squared: 0.02584
## F-statistic: 4.978 on 1 and 149 DF, p-value: 0.02716Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68
## 1219.8235 1219.8235 1219.8235 1219.8235 1381.7496 1381.7496 1381.7496
## 69 70 71 72 73 99 100
## 1381.7496 1443.6625 1443.6625 1443.6625 1519.8630 1334.1243 1334.1243
## 101 102 103 104 105 106 107
## 1334.1243 1334.1243 1134.0979 1134.0979 1134.0979 1134.0979 1343.6493
## 108 109 110 111 112 113 114
## 1343.6493 1343.6493 962.6468 962.6468 1396.0372 1538.9131 1519.8630
## 115 116 117 118 119 120 121
## 1519.8630 1548.4382 1515.1005 1515.1005 1529.3881 1524.6255 1543.6757
## 122 123 124 125 126 127 128
## 1543.6757 1515.1005 1500.8129 1486.5253 1491.2878 1496.0504 1534.1506
## 129 130 131 132 133 134 135
## 1486.5253 1481.7628 1500.8129 1505.5754 1491.2878 1481.7628 1481.7628
## 136 137 139 140 141 142 143
## 1448.4250 1472.2377 1477.0002 1457.9501 1457.9501 1424.6124 1424.6124
## 145 146 150 185 186 187 188
## 1448.4250 1429.3749 1419.8498 1348.4119 1348.4119 1386.5121 1386.5121
## 189 190 191 192 193 194 195
## 1386.5121 1424.6124 1053.1349 1053.1349 1053.1349 1053.1349 1076.9475
## 196 197 198 199 200 201 202
## 1076.9475 1076.9475 1076.9475 1167.4356 1167.4356 1167.4356 1334.1243
## 203 204 205 206 207 208 209
## 1334.1243 1372.2245 1372.2245 1372.2245 1372.2245 1443.6625 1519.8630
## 210 211 212 213 214 215 216
## 1519.8630 1519.8630 824.5333 1010.2721 1267.4488 1529.3881 1529.3881
## 217 218 219 220 221 222 223
## 1529.3881 1529.3881 1529.3881 1529.3881 1529.3881 1529.3881 1529.3881
## 224 225 226 227 228 229 230
## 1529.3881 1529.3881 1534.1506 1534.1506 1534.1506 1534.1506 1534.1506
## 231 232 233 234 235 236 237
## 1548.4382 1553.2007 1553.2007 1553.2007 1553.2007 1553.2007 1553.2007
## 238 239 296 297 298 302 303
## 1553.2007 1553.2007 1524.6255 1529.3881 1529.3881 1543.6757 1543.6757
## 306 308 309 310 311 312 313
## 1548.4382 853.1085 1110.2853 1176.9607 1367.4620 1367.4620 1376.9871
## 314 410 411 412 413 414 415
## 1386.5121 1095.9977 1095.9977 1095.9977 1095.9977 1095.9977 1095.9977
## 416 417 418 419 420 421 422
## 1095.9977 1095.9977 1276.9739 1276.9739 1276.9739 1276.9739 1276.9739
## 423 424 425 426 427 428 429
## 1276.9739 1276.9739 1276.9739 1015.0346 1015.0346 1529.3881 1529.3881
## 433 434 435 439
## 1548.4382 1548.4382 1548.4382 1557.9633cor(Airbus$PricePremium,Airbus$PriceRelative)## [1] 0.1751965fit<-lm(PriceEconomy~PercentPremiumSeats,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PercentPremiumSeats, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1257.06 -1003.28 70.07 911.04 1749.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1839.00 414.53 4.436 1.77e-05 ***
## PercentPremiumSeats -33.89 29.35 -1.154 0.25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1011 on 149 degrees of freedom
## Multiple R-squared: 0.008865, Adjusted R-squared: 0.002214
## F-statistic: 1.333 on 1 and 149 DF, p-value: 0.2502Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 1140.963 1140.963 1140.963 1140.963 1140.963 1140.963 1140.963 1140.963
## 70 71 72 73 99 100 101 102
## 1140.963 1140.963 1140.963 1140.963 1318.522 1318.522 1318.522 1318.522
## 103 104 105 106 107 108 109 110
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 111 112 113 114 115 116 117 118
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 119 120 121 122 123 124 125 126
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 127 128 129 130 131 132 133 134
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 135 136 137 139 140 141 142 143
## 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522 1318.522
## 145 146 150 185 186 187 188 189
## 1318.522 1318.522 1331.060 1363.928 1363.928 1363.928 1363.928 1363.928
## 190 191 192 193 194 195 196 197
## 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928
## 198 199 200 201 202 203 204 205
## 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928
## 206 207 208 209 210 211 212 213
## 1363.928 1363.928 1363.928 1363.928 1363.928 1363.928 1415.434 1415.434
## 214 215 216 217 218 219 220 221
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434
## 222 223 224 225 226 227 228 229
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434
## 230 231 232 233 234 235 236 237
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434
## 238 239 296 297 298 302 303 306
## 1415.434 1415.434 1397.136 1397.136 1404.591 1397.136 1397.136 1397.136
## 308 309 310 311 312 313 314 410
## 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1415.434 1508.280
## 411 412 413 414 415 416 417 418
## 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280
## 419 420 421 422 423 424 425 426
## 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1508.280 1537.421
## 427 428 429 433 434 435 439
## 1537.421 1537.421 1537.421 1537.421 1537.421 1537.421 1537.421cor(Airbus$PricePremium,Airbus$PercentPremiumSeats)## [1] -0.009426023fit<-lm(PricePremium~PitchEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1847.8 -1338.3 606.7 1132.0 1760.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2963.2 6298.5 -0.470 0.639
## PitchEconomy 153.7 200.3 0.767 0.444
##
## Residual standard error: 1253 on 149 degrees of freedom
## Multiple R-squared: 0.003937, Adjusted R-squared: -0.002748
## F-statistic: 0.5889 on 1 and 149 DF, p-value: 0.4441Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 70 71 72 73 99 100 101 102
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 103 104 105 106 107 108 109 110
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 111 112 113 114 115 116 117 118
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 119 120 121 122 123 124 125 126
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 127 128 129 130 131 132 133 134
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 135 136 137 139 140 141 142 143
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 145 146 150 185 186 187 188 189
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 190 191 192 193 194 195 196 197
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 198 199 200 201 202 203 204 205
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311
## 206 207 208 209 210 211 212 213
## 1802.311 1802.311 1802.311 1802.311 1802.311 1802.311 1956.039 1956.039
## 214 215 216 217 218 219 220 221
## 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039
## 222 223 224 225 226 227 228 229
## 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039
## 230 231 232 233 234 235 236 237
## 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039
## 238 239 296 297 298 302 303 306
## 1956.039 1956.039 1956.039 1956.039 2109.766 1956.039 1956.039 1956.039
## 308 309 310 311 312 313 314 410
## 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039
## 411 412 413 414 415 416 417 418
## 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039
## 419 420 421 422 423 424 425 426
## 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039
## 427 428 429 433 434 435 439
## 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039 1956.039cor(Airbus$PricePremium,Airbus$PitchEconomy)## [1] 0.06274279fit<-lm(PriceEconomy~PitchEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1857.6 -957.9 268.4 886.4 1586.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13326.4 4956.7 -2.689 0.00799 **
## PitchEconomy 467.5 157.6 2.965 0.00352 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 986.4 on 149 degrees of freedom
## Multiple R-squared: 0.05573, Adjusted R-squared: 0.04939
## F-statistic: 8.793 on 1 and 149 DF, p-value: 0.003522Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68 69
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 70 71 72 73 99 100 101 102
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 103 104 105 106 107 108 109 110
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 111 112 113 114 115 116 117 118
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 119 120 121 122 123 124 125 126
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 127 128 129 130 131 132 133 134
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 135 136 137 139 140 141 142 143
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 145 146 150 185 186 187 188 189
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 190 191 192 193 194 195 196 197
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 198 199 200 201 202 203 204 205
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623
## 206 207 208 209 210 211 212 213
## 1165.623 1165.623 1165.623 1165.623 1165.623 1165.623 1633.107 1633.107
## 214 215 216 217 218 219 220 221
## 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107
## 222 223 224 225 226 227 228 229
## 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107
## 230 231 232 233 234 235 236 237
## 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107
## 238 239 296 297 298 302 303 306
## 1633.107 1633.107 1633.107 1633.107 2100.590 1633.107 1633.107 1633.107
## 308 309 310 311 312 313 314 410
## 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107
## 411 412 413 414 415 416 417 418
## 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107
## 419 420 421 422 423 424 425 426
## 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107
## 427 428 429 433 434 435 439
## 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107 1633.107cor(Airbus$PriceEconomy,Airbus$PitchEconomy)## [1] 0.2360655fit<-lm(PricePremium~PitchPremium,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchPremium, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1836.4 -1144.4 475.6 1048.6 1629.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -14015.9 4974.4 -2.818 0.00550 **
## PitchPremium 419.7 131.4 3.194 0.00171 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1215 on 149 degrees of freedom
## Multiple R-squared: 0.06408, Adjusted R-squared: 0.0578
## F-statistic: 10.2 on 1 and 149 DF, p-value: 0.001712Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 69 70 71 72 73 99 100
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 101 102 103 104 105 106 107
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 108 109 110 111 112 113 114
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 115 116 117 118 119 120 121
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 122 123 124 125 126 127 128
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 129 130 131 132 133 134 135
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 136 137 139 140 141 142 143
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 145 146 150 185 186 187 188
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 189 190 191 192 193 194 195
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 196 197 198 199 200 201 202
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 203 204 205 206 207 208 209
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 210 211 212 213 214 215 216
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 217 218 219 220 221 222 223
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 224 225 226 227 228 229 230
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 231 232 233 234 235 236 237
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 238 239 296 297 298 302 303
## 1933.4340 1933.4340 254.5584 254.5584 674.2773 254.5584 254.5584
## 306 308 309 310 311 312 313
## 254.5584 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 314 410 411 412 413 414 415
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 416 417 418 419 420 421 422
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 423 424 425 426 427 428 429
## 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340 1933.4340
## 433 434 435 439
## 1933.4340 1933.4340 1933.4340 1933.4340cor(Airbus$PricePremium,Airbus$PitchPremium)## [1] 0.2531437fit<-lm(PriceEconomy~PitchPremium,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchPremium, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1340.12 -909.12 61.88 637.88 1805.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9604.9 4057.5 -2.367 0.01921 *
## PitchPremium 290.0 107.2 2.705 0.00762 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 991.1 on 149 degrees of freedom
## Multiple R-squared: 0.04682, Adjusted R-squared: 0.04042
## F-statistic: 7.319 on 1 and 149 DF, p-value: 0.007618Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 69 70 71 72 73 99 100
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 101 102 103 104 105 106 107
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 108 109 110 111 112 113 114
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 115 116 117 118 119 120 121
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 122 123 124 125 126 127 128
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 129 130 131 132 133 134 135
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 136 137 139 140 141 142 143
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 145 146 150 185 186 187 188
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 189 190 191 192 193 194 195
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 196 197 198 199 200 201 202
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 203 204 205 206 207 208 209
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 210 211 212 213 214 215 216
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 217 218 219 220 221 222 223
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 224 225 226 227 228 229 230
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 231 232 233 234 235 236 237
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 238 239 296 297 298 302 303
## 1414.1221 1414.1221 254.2206 254.2206 544.1960 254.2206 254.2206
## 306 308 309 310 311 312 313
## 254.2206 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 314 410 411 412 413 414 415
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 416 417 418 419 420 421 422
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 423 424 425 426 427 428 429
## 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221 1414.1221
## 433 434 435 439
## 1414.1221 1414.1221 1414.1221 1414.1221cor(Airbus$PriceEconomy,Airbus$PitchPremium)## [1] 0.2163831fit<-lm(PriceEconomy~WidthPremium,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthPremium, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1203.7 -942.5 -141.4 570.3 1942.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1948.7 1590.9 -1.225 0.2225
## WidthPremium 169.8 81.3 2.089 0.0384 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1001 on 149 degrees of freedom
## Multiple R-squared: 0.02845, Adjusted R-squared: 0.02193
## F-statistic: 4.363 on 1 and 149 DF, p-value: 0.03843Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652
## 69 70 71 72 73 99 100
## 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1277.7366 1277.7366
## 101 102 103 104 105 106 107
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 108 109 110 111 112 113 114
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 115 116 117 118 119 120 121
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 122 123 124 125 126 127 128
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 129 130 131 132 133 134 135
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 136 137 139 140 141 142 143
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 145 146 150 185 186 187 188
## 1277.7366 1277.7366 1277.7366 1617.3652 1617.3652 1617.3652 1617.3652
## 189 190 191 192 193 194 195
## 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652
## 196 197 198 199 200 201 202
## 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652
## 203 204 205 206 207 208 209
## 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652 1617.3652
## 210 211 212 213 214 215 216
## 1617.3652 1617.3652 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 217 218 219 220 221 222 223
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 224 225 226 227 228 229 230
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 231 232 233 234 235 236 237
## 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 238 239 296 297 298 302 303
## 1277.7366 1277.7366 938.1081 938.1081 938.1081 938.1081 938.1081
## 306 308 309 310 311 312 313
## 938.1081 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366 1277.7366
## 314 410 411 412 413 414 415
## 1277.7366 1447.5509 1447.5509 1447.5509 1447.5509 1447.5509 1447.5509
## 416 417 418 419 420 421 422
## 1447.5509 1447.5509 1447.5509 1447.5509 1447.5509 1447.5509 1447.5509
## 423 424 425 426 427 428 429
## 1447.5509 1447.5509 1447.5509 1277.7366 1277.7366 1277.7366 1277.7366
## 433 434 435 439
## 1277.7366 1277.7366 1277.7366 1277.7366cor(Airbus$PriceEconomy,Airbus$WidthPremium)## [1] 0.1686651fit<-lm(PricePremium~WidthPremium,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthPremium, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1919.74 -1067.56 -14.74 1157.62 1933.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6772.0 1866.9 -3.627 0.000393 ***
## WidthPremium 442.2 95.4 4.635 7.74e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1174 on 149 degrees of freedom
## Multiple R-squared: 0.126, Adjusted R-squared: 0.1201
## F-statistic: 21.48 on 1 and 149 DF, p-value: 7.74e-06Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68
## 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356
## 69 70 71 72 73 99 100
## 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 1629.3804 1629.3804
## 101 102 103 104 105 106 107
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 108 109 110 111 112 113 114
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 115 116 117 118 119 120 121
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 122 123 124 125 126 127 128
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 129 130 131 132 133 134 135
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 136 137 139 140 141 142 143
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 145 146 150 185 186 187 188
## 1629.3804 1629.3804 1629.3804 2513.7356 2513.7356 2513.7356 2513.7356
## 189 190 191 192 193 194 195
## 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356
## 196 197 198 199 200 201 202
## 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356
## 203 204 205 206 207 208 209
## 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356 2513.7356
## 210 211 212 213 214 215 216
## 2513.7356 2513.7356 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 217 218 219 220 221 222 223
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 224 225 226 227 228 229 230
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 231 232 233 234 235 236 237
## 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 238 239 296 297 298 302 303
## 1629.3804 1629.3804 745.0252 745.0252 745.0252 745.0252 745.0252
## 306 308 309 310 311 312 313
## 745.0252 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804 1629.3804
## 314 410 411 412 413 414 415
## 1629.3804 2071.5580 2071.5580 2071.5580 2071.5580 2071.5580 2071.5580
## 416 417 418 419 420 421 422
## 2071.5580 2071.5580 2071.5580 2071.5580 2071.5580 2071.5580 2071.5580
## 423 424 425 426 427 428 429
## 2071.5580 2071.5580 2071.5580 1629.3804 1629.3804 1629.3804 1629.3804
## 433 434 435 439
## 1629.3804 1629.3804 1629.3804 1629.3804cor(Airbus$PricePremium,Airbus$WidthPremium)## [1] 0.3549722fit<-lm(PriceEconomy~WidthEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1497.8 -986.2 45.8 663.8 1831.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6348.0 3950.1 1.607 0.110
## WidthEconomy -275.5 218.6 -1.261 0.209
##
## Residual standard error: 1010 on 149 degrees of freedom
## Multiple R-squared: 0.01055, Adjusted R-squared: 0.003911
## F-statistic: 1.589 on 1 and 149 DF, p-value: 0.2094Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68 69
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 70 71 72 73 99 100 101 102
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 103 104 105 106 107 108 109 110
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 111 112 113 114 115 116 117 118
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 119 120 121 122 123 124 125 126
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 127 128 129 130 131 132 133 134
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 135 136 137 139 140 141 142 143
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 145 146 150 185 186 187 188 189
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 190 191 192 193 194 195 196 197
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 198 199 200 201 202 203 204 205
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 206 207 208 209 210 211 212 213
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 214 215 216 217 218 219 220 221
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 222 223 224 225 226 227 228 229
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 230 231 232 233 234 235 236 237
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 238 239 296 297 298 302 303 306
## 1388.202 1388.202 1663.748 1663.748 1663.748 1663.748 1663.748 1663.748
## 308 309 310 311 312 313 314 410
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1112.655
## 411 412 413 414 415 416 417 418
## 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655
## 419 420 421 422 423 424 425 426
## 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1388.202
## 427 428 429 433 434 435 439
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202cor(Airbus$PriceEconomy,Airbus$WidthEconomy)## [1] -0.1027203fit<-lm(PricePremium~WidthEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1869.7 -1416.8 528.2 1101.2 1682.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4940.4 4906.7 1.007 0.316
## WidthEconomy -170.0 271.5 -0.626 0.532
##
## Residual standard error: 1254 on 149 degrees of freedom
## Multiple R-squared: 0.002623, Adjusted R-squared: -0.004071
## F-statistic: 0.3919 on 1 and 149 DF, p-value: 0.5323Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 70 71 72 73 99 100 101 102
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 103 104 105 106 107 108 109 110
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 111 112 113 114 115 116 117 118
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 119 120 121 122 123 124 125 126
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 127 128 129 130 131 132 133 134
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 135 136 137 139 140 141 142 143
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 145 146 150 185 186 187 188 189
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 190 191 192 193 194 195 196 197
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 198 199 200 201 202 203 204 205
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 206 207 208 209 210 211 212 213
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 214 215 216 217 218 219 220 221
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 222 223 224 225 226 227 228 229
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 230 231 232 233 234 235 236 237
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 238 239 296 297 298 302 303 306
## 1880.760 1880.760 2050.742 2050.742 2050.742 2050.742 2050.742 2050.742
## 308 309 310 311 312 313 314 410
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1710.778
## 411 412 413 414 415 416 417 418
## 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778
## 419 420 421 422 423 424 425 426
## 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1880.760
## 427 428 429 433 434 435 439
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760cor(Airbus$PricePremium,Airbus$WidthEconomy)## [1] -0.05121666fit<-lm(PriceEconomy~WidthEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1497.8 -986.2 45.8 663.8 1831.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6348.0 3950.1 1.607 0.110
## WidthEconomy -275.5 218.6 -1.261 0.209
##
## Residual standard error: 1010 on 149 degrees of freedom
## Multiple R-squared: 0.01055, Adjusted R-squared: 0.003911
## F-statistic: 1.589 on 1 and 149 DF, p-value: 0.2094Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68 69
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 70 71 72 73 99 100 101 102
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 103 104 105 106 107 108 109 110
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 111 112 113 114 115 116 117 118
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 119 120 121 122 123 124 125 126
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 127 128 129 130 131 132 133 134
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 135 136 137 139 140 141 142 143
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 145 146 150 185 186 187 188 189
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 190 191 192 193 194 195 196 197
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 198 199 200 201 202 203 204 205
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 206 207 208 209 210 211 212 213
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 214 215 216 217 218 219 220 221
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 222 223 224 225 226 227 228 229
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 230 231 232 233 234 235 236 237
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202
## 238 239 296 297 298 302 303 306
## 1388.202 1388.202 1663.748 1663.748 1663.748 1663.748 1663.748 1663.748
## 308 309 310 311 312 313 314 410
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1112.655
## 411 412 413 414 415 416 417 418
## 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655
## 419 420 421 422 423 424 425 426
## 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1112.655 1388.202
## 427 428 429 433 434 435 439
## 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202 1388.202cor(Airbus$PriceEconomy,Airbus$WidthEconomy)## [1] -0.1027203fit<-lm(PricePremium~WidthEconomy,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthEconomy, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1869.7 -1416.8 528.2 1101.2 1682.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4940.4 4906.7 1.007 0.316
## WidthEconomy -170.0 271.5 -0.626 0.532
##
## Residual standard error: 1254 on 149 degrees of freedom
## Multiple R-squared: 0.002623, Adjusted R-squared: -0.004071
## F-statistic: 0.3919 on 1 and 149 DF, p-value: 0.5323Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 70 71 72 73 99 100 101 102
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 103 104 105 106 107 108 109 110
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 111 112 113 114 115 116 117 118
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 119 120 121 122 123 124 125 126
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 127 128 129 130 131 132 133 134
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 135 136 137 139 140 141 142 143
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 145 146 150 185 186 187 188 189
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 190 191 192 193 194 195 196 197
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 198 199 200 201 202 203 204 205
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 206 207 208 209 210 211 212 213
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 214 215 216 217 218 219 220 221
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 222 223 224 225 226 227 228 229
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 230 231 232 233 234 235 236 237
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760
## 238 239 296 297 298 302 303 306
## 1880.760 1880.760 2050.742 2050.742 2050.742 2050.742 2050.742 2050.742
## 308 309 310 311 312 313 314 410
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1710.778
## 411 412 413 414 415 416 417 418
## 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778
## 419 420 421 422 423 424 425 426
## 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1710.778 1880.760
## 427 428 429 433 434 435 439
## 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760 1880.760cor(Airbus$PricePremium,Airbus$WidthEconomy)## [1] -0.05121666fit<-lm(PriceEconomy~SeatsTotal,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ SeatsTotal, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1816.31 -834.66 48.75 750.89 2443.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2558.242 260.756 9.811 < 2e-16 ***
## SeatsTotal -4.173 0.875 -4.769 4.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 945.5 on 149 degrees of freedom
## Multiple R-squared: 0.1324, Adjusted R-squared: 0.1266
## F-statistic: 22.75 on 1 and 149 DF, p-value: 4.359e-06Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68
## 1585.8377 1585.8377 1585.8377 1585.8377 1585.8377 1585.8377 1585.8377
## 69 70 71 72 73 99 100
## 1585.8377 1585.8377 1585.8377 1585.8377 1585.8377 1064.1616 1064.1616
## 101 102 103 104 105 106 107
## 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616
## 108 109 110 111 112 113 114
## 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616
## 115 116 117 118 119 120 121
## 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616
## 122 123 124 125 126 127 128
## 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616
## 129 130 131 132 133 134 135
## 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616
## 136 137 139 140 141 142 143
## 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616 1064.1616
## 145 146 150 185 186 187 188
## 1064.1616 1064.1616 1026.6009 1427.2482 1427.2482 1427.2482 1427.2482
## 189 190 191 192 193 194 195
## 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482
## 196 197 198 199 200 201 202
## 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482
## 203 204 205 206 207 208 209
## 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482 1427.2482
## 210 211 212 213 214 215 216
## 1427.2482 1427.2482 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093
## 217 218 219 220 221 222 223
## 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093
## 224 225 226 227 228 229 230
## 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093
## 231 232 233 234 235 236 237
## 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093
## 238 239 296 297 298 302 303
## 1857.1093 1857.1093 1982.3116 1982.3116 1907.1902 1982.3116 1982.3116
## 306 308 309 310 311 312 313
## 1982.3116 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093 1857.1093
## 314 410 411 412 413 414 415
## 1857.1093 1018.2541 1018.2541 1018.2541 1018.2541 1018.2541 1018.2541
## 416 417 418 419 420 421 422
## 1018.2541 1018.2541 1018.2541 1018.2541 1018.2541 1018.2541 1018.2541
## 423 424 425 426 427 428 429
## 1018.2541 1018.2541 1018.2541 776.1964 776.1964 776.1964 776.1964
## 433 434 435 439
## 776.1964 776.1964 776.1964 776.1964cor(Airbus$PriceEconomy,Airbus$SeatsTotal)## [1] -0.3639357fit<-lm(PricePremium~SeatsTotal,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ SeatsTotal, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2231.6 -1316.8 485.4 973.5 1964.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2923.372 334.376 8.743 4.43e-15 ***
## SeatsTotal -3.701 1.122 -3.299 0.00122 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1212 on 149 degrees of freedom
## Multiple R-squared: 0.06806, Adjusted R-squared: 0.0618
## F-statistic: 10.88 on 1 and 149 DF, p-value: 0.001215Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 2060.966 2060.966 2060.966 2060.966 2060.966 2060.966 2060.966 2060.966
## 70 71 72 73 99 100 101 102
## 2060.966 2060.966 2060.966 2060.966 1598.303 1598.303 1598.303 1598.303
## 103 104 105 106 107 108 109 110
## 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303
## 111 112 113 114 115 116 117 118
## 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303
## 119 120 121 122 123 124 125 126
## 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303
## 127 128 129 130 131 132 133 134
## 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303
## 135 136 137 139 140 141 142 143
## 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303 1598.303
## 145 146 150 185 186 187 188 189
## 1598.303 1598.303 1564.991 1920.317 1920.317 1920.317 1920.317 1920.317
## 190 191 192 193 194 195 196 197
## 1920.317 1920.317 1920.317 1920.317 1920.317 1920.317 1920.317 1920.317
## 198 199 200 201 202 203 204 205
## 1920.317 1920.317 1920.317 1920.317 1920.317 1920.317 1920.317 1920.317
## 206 207 208 209 210 211 212 213
## 1920.317 1920.317 1920.317 1920.317 1920.317 1920.317 2301.552 2301.552
## 214 215 216 217 218 219 220 221
## 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552
## 222 223 224 225 226 227 228 229
## 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552
## 230 231 232 233 234 235 236 237
## 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552
## 238 239 296 297 298 302 303 306
## 2301.552 2301.552 2412.591 2412.591 2345.967 2412.591 2412.591 2412.591
## 308 309 310 311 312 313 314 410
## 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 2301.552 1557.588
## 411 412 413 414 415 416 417 418
## 1557.588 1557.588 1557.588 1557.588 1557.588 1557.588 1557.588 1557.588
## 419 420 421 422 423 424 425 426
## 1557.588 1557.588 1557.588 1557.588 1557.588 1557.588 1557.588 1342.912
## 427 428 429 433 434 435 439
## 1342.912 1342.912 1342.912 1342.912 1342.912 1342.912 1342.912cor(Airbus$PricePremium,Airbus$SeatsTotal)## [1] -0.260877fit<-lm(PriceEconomy~PitchDifference,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ PitchDifference, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1320.39 -894.13 39.61 657.61 1867.07
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1104.23 526.17 2.099 0.0375 *
## PitchDifference 41.45 81.06 0.511 0.6099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1014 on 149 degrees of freedom
## Multiple R-squared: 0.001752, Adjusted R-squared: -0.004948
## F-statistic: 0.2615 on 1 and 149 DF, p-value: 0.6099Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68 69
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 70 71 72 73 99 100 101 102
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 103 104 105 106 107 108 109 110
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 111 112 113 114 115 116 117 118
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 119 120 121 122 123 124 125 126
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 127 128 129 130 131 132 133 134
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 135 136 137 139 140 141 142 143
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 145 146 150 185 186 187 188 189
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 190 191 192 193 194 195 196 197
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 198 199 200 201 202 203 204 205
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385
## 206 207 208 209 210 211 212 213
## 1394.385 1394.385 1394.385 1394.385 1394.385 1394.385 1352.934 1352.934
## 214 215 216 217 218 219 220 221
## 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934
## 222 223 224 225 226 227 228 229
## 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934
## 230 231 232 233 234 235 236 237
## 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934
## 238 239 296 297 298 302 303 306
## 1352.934 1352.934 1187.130 1187.130 1187.130 1187.130 1187.130 1187.130
## 308 309 310 311 312 313 314 410
## 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934
## 411 412 413 414 415 416 417 418
## 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934
## 419 420 421 422 423 424 425 426
## 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934
## 427 428 429 433 434 435 439
## 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934 1352.934cor(Airbus$PriceEconomy,Airbus$PitchDifference)## [1] 0.04185429fit<-lm(PricePremium~PitchDifference,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ PitchDifference, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1885.0 -1002.2 427.1 1067.8 1581.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 646.47 643.63 1.004 0.3168
## PitchDifference 190.78 99.16 1.924 0.0563 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1241 on 149 degrees of freedom
## Multiple R-squared: 0.02424, Adjusted R-squared: 0.01769
## F-statistic: 3.702 on 1 and 149 DF, p-value: 0.05626Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 70 71 72 73 99 100 101 102
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 103 104 105 106 107 108 109 110
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 111 112 113 114 115 116 117 118
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 119 120 121 122 123 124 125 126
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 127 128 129 130 131 132 133 134
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 135 136 137 139 140 141 142 143
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 145 146 150 185 186 187 188 189
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 190 191 192 193 194 195 196 197
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 198 199 200 201 202 203 204 205
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952
## 206 207 208 209 210 211 212 213
## 1981.952 1981.952 1981.952 1981.952 1981.952 1981.952 1791.168 1791.168
## 214 215 216 217 218 219 220 221
## 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168
## 222 223 224 225 226 227 228 229
## 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168
## 230 231 232 233 234 235 236 237
## 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168
## 238 239 296 297 298 302 303 306
## 1791.168 1791.168 1028.035 1028.035 1028.035 1028.035 1028.035 1028.035
## 308 309 310 311 312 313 314 410
## 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168
## 411 412 413 414 415 416 417 418
## 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168
## 419 420 421 422 423 424 425 426
## 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168
## 427 428 429 433 434 435 439
## 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168 1791.168cor(Airbus$PricePremium,Airbus$PitchDifference)## [1] 0.1557009fit<-lm(PriceEconomy~WidthDifference,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PriceEconomy ~ WidthDifference, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1207.0 -830.7 -271.0 596.1 1968.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1004.40 152.26 6.597 6.89e-10 ***
## WidthDifference 247.53 87.52 2.828 0.00533 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 988.9 on 149 degrees of freedom
## Multiple R-squared: 0.05094, Adjusted R-squared: 0.04458
## F-statistic: 7.998 on 1 and 149 DF, p-value: 0.005327Airbus$PriceEconomy## [1] 1813 1813 1813 1813 2052 2052 2052 2052 1919 1919 1919 540 2384 2384
## [15] 2384 2384 1848 1848 1848 1848 1758 1758 1758 719 719 1198 457 402
## [29] 402 392 356 356 322 297 303 303 276 249 238 238 228 231
## [43] 203 201 207 207 182 171 168 140 147 138 126 126 109 109
## [57] 104 97 74 1778 1778 1999 1999 1999 1985 1434 1434 1434 1434 1476
## [71] 1476 1476 1476 1903 1903 1903 2369 2369 1767 1767 1767 1767 1919 540
## [85] 540 540 630 743 990 2659 2659 2659 2659 2659 2659 2659 2659 2607
## [99] 2607 2607 2860 2860 2609 2609 2609 2813 3165 3165 3165 3165 3165 3165
## [113] 3165 3165 166 329 243 293 293 416 336 429 462 557 557 661
## [127] 676 505 505 505 505 505 505 505 505 690 690 690 690 690
## [141] 690 690 690 1522 1522 2581 2581 2979 2979 2979 3220fitted(fit)## 62 63 64 65 66 67 68 69
## 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980
## 70 71 72 73 99 100 101 102
## 1746.980 1746.980 1746.980 1746.980 1251.928 1251.928 1251.928 1251.928
## 103 104 105 106 107 108 109 110
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 111 112 113 114 115 116 117 118
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 119 120 121 122 123 124 125 126
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 127 128 129 130 131 132 133 134
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 135 136 137 139 140 141 142 143
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 145 146 150 185 186 187 188 189
## 1251.928 1251.928 1251.928 1746.980 1746.980 1746.980 1746.980 1746.980
## 190 191 192 193 194 195 196 197
## 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980
## 198 199 200 201 202 203 204 205
## 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980
## 206 207 208 209 210 211 212 213
## 1746.980 1746.980 1746.980 1746.980 1746.980 1746.980 1251.928 1251.928
## 214 215 216 217 218 219 220 221
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 222 223 224 225 226 227 228 229
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 230 231 232 233 234 235 236 237
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 238 239 296 297 298 302 303 306
## 1251.928 1251.928 1004.402 1004.402 1004.402 1004.402 1004.402 1004.402
## 308 309 310 311 312 313 314 410
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 411 412 413 414 415 416 417 418
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 419 420 421 422 423 424 425 426
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928
## 427 428 429 433 434 435 439
## 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928 1251.928cor(Airbus$PriceEconomy,Airbus$WidthDifference)## [1] 0.2257098fit<-lm(PricePremium~WidthDifference,data = Airbus)
summary(fit)##
## Call:
## lm(formula = PricePremium ~ WidthDifference, data = Airbus)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2117.9 -844.3 -123.9 1181.2 1957.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1052.8 176.6 5.962 1.73e-08 ***
## WidthDifference 553.0 101.5 5.448 2.06e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1147 on 149 degrees of freedom
## Multiple R-squared: 0.1661, Adjusted R-squared: 0.1605
## F-statistic: 29.68 on 1 and 149 DF, p-value: 2.056e-07Airbus$PricePremium## [1] 3128 3128 3128 3128 2856 2856 2856 2856 2409 2409 2409 594 3563 3563
## [15] 3563 3563 3536 3536 3536 3536 2592 2592 2592 1634 1634 1634 486 442
## [29] 442 407 396 396 348 323 319 319 306 285 278 276 263 247
## [43] 238 237 237 234 211 201 198 175 175 165 156 156 141 141
## [57] 131 125 97 2588 2588 2765 2765 2765 2588 2982 2982 2982 2982 2997
## [71] 2997 2997 2997 3509 3509 3509 3540 3540 2499 2499 2499 2499 2409 594
## [85] 594 594 1611 1611 1611 2859 2859 2859 2859 2859 2859 2859 2859 2807
## [99] 2807 2807 3063 3063 2787 2787 2787 2922 3275 3275 3275 3275 3275 3275
## [113] 3275 3275 181 354 262 308 308 431 841 841 841 789 789 928
## [127] 931 1004 1004 1004 1004 1004 1004 1004 1004 1110 1110 1110 1110 1110
## [141] 1110 1110 1110 3289 3289 2781 2781 3088 3088 3088 3289fitted(fit)## 62 63 64 65 66 67 68 69
## 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855
## 70 71 72 73 99 100 101 102
## 2711.855 2711.855 2711.855 2711.855 1605.810 1605.810 1605.810 1605.810
## 103 104 105 106 107 108 109 110
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 111 112 113 114 115 116 117 118
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 119 120 121 122 123 124 125 126
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 127 128 129 130 131 132 133 134
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 135 136 137 139 140 141 142 143
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 145 146 150 185 186 187 188 189
## 1605.810 1605.810 1605.810 2711.855 2711.855 2711.855 2711.855 2711.855
## 190 191 192 193 194 195 196 197
## 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855
## 198 199 200 201 202 203 204 205
## 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855
## 206 207 208 209 210 211 212 213
## 2711.855 2711.855 2711.855 2711.855 2711.855 2711.855 1605.810 1605.810
## 214 215 216 217 218 219 220 221
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 222 223 224 225 226 227 228 229
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 230 231 232 233 234 235 236 237
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 238 239 296 297 298 302 303 306
## 1605.810 1605.810 1052.788 1052.788 1052.788 1052.788 1052.788 1052.788
## 308 309 310 311 312 313 314 410
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 411 412 413 414 415 416 417 418
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 419 420 421 422 423 424 425 426
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810
## 427 428 429 433 434 435 439
## 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810 1605.810cor(Airbus$PricePremium,Airbus$WidthDifference)## [1] 0.4075861Now It’s time for all overall Analysis
mean(airline$PriceEconomy)## [1] 1327.076mean(airline$PricePremium)## [1] 1845.258library(plotly)
x<-c('Jul','Aug','Sept','Oct')
y1<-c(by(airline$PriceEconomy,airline$TravelMonth,mean))
y2<-c(by(airline$PricePremium,airline$TravelMonth,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Months", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')mean(airline$PriceEconomy)## [1] 1327.076mean(airline$PricePremium)## [1] 1845.258library(plotly)
x<-c('British','Virgin','Delta','Jet','AirFrance','Singapore')
y1<-c(by(airline$PriceEconomy,airline$Airline,mean))
y2<-c(by(airline$PricePremium,airline$Airline,mean))
data<-data.frame(x,y1,y2)
data$x <- factor(data$x, levels = data[["x"]])
plot_ly(data, x = ~x, y = ~y1, type = 'bar', name = 'Economy Ticket Price', marker = list(color = 'rgb(49,130,189)')) %>%
add_trace(y = ~y2, name = 'Premium Ticket Price', marker = list(color = 'rgb(204,204,204)')) %>%
layout(xaxis = list(title = "Airlines", tickangle = -45),
yaxis = list(title = "Price"),
margin = list(b = 100),
barmode = 'group')by(airline$PriceEconomy,airline$Airline,mean)## airline$Airline: AirFrance
## [1] 2769.784
## --------------------------------------------------------
## airline$Airline: British
## [1] 1293.48
## --------------------------------------------------------
## airline$Airline: Delta
## [1] 560.9348
## --------------------------------------------------------
## airline$Airline: Jet
## [1] 276.1639
## --------------------------------------------------------
## airline$Airline: Singapore
## [1] 860.25
## --------------------------------------------------------
## airline$Airline: Virgin
## [1] 1603.532by(airline$PricePremium,airline$Airline,mean)## airline$Airline: AirFrance
## [1] 3065.216
## --------------------------------------------------------
## airline$Airline: British
## [1] 1937.029
## --------------------------------------------------------
## airline$Airline: Delta
## [1] 684.6739
## --------------------------------------------------------
## airline$Airline: Jet
## [1] 483.3607
## --------------------------------------------------------
## airline$Airline: Singapore
## [1] 1239.925
## --------------------------------------------------------
## airline$Airline: Virgin
## [1] 2721.694by(airline$SeatsEconomy,airline$Airline,mean)## airline$Airline: AirFrance
## [1] 214.4595
## --------------------------------------------------------
## airline$Airline: British
## [1] 216.5886
## --------------------------------------------------------
## airline$Airline: Delta
## [1] 137.2174
## --------------------------------------------------------
## airline$Airline: Jet
## [1] 140.3115
## --------------------------------------------------------
## airline$Airline: Singapore
## [1] 243.6
## --------------------------------------------------------
## airline$Airline: Virgin
## [1] 230.1774by(airline$SeatsPremium,airline$Airline,mean)## airline$Airline: AirFrance
## [1] 26.7027
## --------------------------------------------------------
## airline$Airline: British
## [1] 43.18286
## --------------------------------------------------------
## airline$Airline: Delta
## [1] 22.56522
## --------------------------------------------------------
## airline$Airline: Jet
## [1] 15.65574
## --------------------------------------------------------
## airline$Airline: Singapore
## [1] 31.2
## --------------------------------------------------------
## airline$Airline: Virgin
## [1] 42.53226by(airline$WidthEconomy,airline$Airline,mean)## airline$Airline: AirFrance
## [1] 17.56757
## --------------------------------------------------------
## airline$Airline: British
## [1] 18
## --------------------------------------------------------
## airline$Airline: Delta
## [1] 17.3913
## --------------------------------------------------------
## airline$Airline: Jet
## [1] 17.11475
## --------------------------------------------------------
## airline$Airline: Singapore
## [1] 19
## --------------------------------------------------------
## airline$Airline: Virgin
## [1] 18by(airline$WidthPremium,airline$Airline,mean)## airline$Airline: AirFrance
## [1] 19
## --------------------------------------------------------
## airline$Airline: British
## [1] 19
## --------------------------------------------------------
## airline$Airline: Delta
## [1] 17.78261
## --------------------------------------------------------
## airline$Airline: Jet
## [1] 20.77049
## --------------------------------------------------------
## airline$Airline: Singapore
## [1] 20
## --------------------------------------------------------
## airline$Airline: Virgin
## [1] 21Drawing boxplots among comparable variables
Reading variables except Airline,Aircraft,IsInternational,TravelMonth because they are strings
airline1=airline[,6:18]
View(airline1)
##Taking Logarithm
airline2=log(airline1+1)
boxplot(airline2,xlab="Value",ylab="Parameters",main="Boxplot Presentation of different Parameters")
individual boxplots for comparable variables
par(mfrow=c(1,2))
with(airline,boxplot(airline$PriceEconomy,main="Price of Economy Seats",ylab="Price of tickets"))
with(airline,boxplot(airline$PricePremium,main="Price of Premium Seats",ylab="Price of tickets"))
par(mfrow=c(1,1))
par(mfrow=c(1,2))
with(airline,boxplot(airline$WidthEconomy,main="Width of Economy Seats",ylab="Width of seats"))
with(airline,boxplot(airline$WidthPremium,main="Width of Premium Seats",ylab="Width of seats"))
par(mfrow=c(1,1))
par(mfrow=c(1,2))
with(airline,boxplot(airline$PitchEconomy,main="Pitch of Economy Seats",ylab="Pitch of seats"))
with(airline,boxplot(airline$PitchPremium,main="Pitch of Premium Seats",ylab="Pitch of seats"))
par(mfrow=c(1,1))
par(mfrow=c(1,2))
with(airline,boxplot(airline$SeatsEconomy,main="No of Economy Seats",ylab="Count"))
with(airline,boxplot(airline$SeatsPremium,main="No of Premium Seats",ylab="Count"))
par(mfrow=c(1,1))
boxplot(airline$FlightDuration,main="Duration of Flights",ylab="Hours")
Price of seats with different Aircrafts
boxplot(airline$PriceEconomy~airline$Aircraft,yaxt="n",horizontal=TRUE,main="Price of Economy Seats with Aircraft",xlab="Price of tickets",ylab="Name Of Aircraft")
axis(side=2,at=c(1,2),labels=c("Boeing","AirBus"))
boxplot(airline$PricePremium~airline$Aircraft,yaxt="n",horizontal=TRUE,main="Price of Premium Seats with Aircraft",xlab="Price of tickets",ylab="Name Of Aircraft")
axis(side=2,at=c(1,2),labels=c("Boeing","AirBus"))
price of seat varies with flightduration
plot(airline$FlightDuration~airline$PriceEconomy,main="Price of Economy seat with FlightDuration",xlab="FlightDuration(hours)",ylab="Price of seats",cex=1.1)
plot(airline$FlightDuration~airline$PricePremium,main="Price of Premium seat with FlightDuration",xlab="FlightDuration(hours)",ylab="Price of seats",cex=1.1)
plot(airline$FlightDuration~airline$PriceRelative,main="PriceRelative with FlightDuration",xlab="ratio",ylab="PriceRelative ",cex=1.1)
plot(airline$PitchEconomy~airline$PriceRelative,main="PriceRelative with FlightDuration",xlab="ratio",ylab="PriceRelative ",cex=1.1)
scatterplots
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(airline$PriceEconomy,airline$FlightDuration,main="Price of economy seats with FlightDuration",ylab="FlightDuration(hours)",xlab="Price of seats",cex=1.1,pch=19)
library(car)
scatterplot(airline$PricePremium,airline$FlightDuration,main="Price of premium seats with FlightDuration",ylab="FlightDuration(hours)",xlab="Price of seats",cex=1.1,pch=19)
library(car)
scatterplot(airline$PriceRelative,airline$FlightDuration,main="PriceRelative with FlightDuration",ylab="FlightDuration(hours)",xlab="ratio",cex=1.1,pch=19)
library(car)
scatterplot(airline$PriceRelative,airline$PitchEconomy,main="PriceRelative with FlightDuration",ylab="FlightDuration(hours)",xlab="ratio",cex=1.1,pch=19)
scatterplotmatrix
library(car)
scatterplotMatrix(airline[,c("FlightDuration","PriceEconomy","PricePremium")],spread=FALSE,smoother.args=list(lty=2),main="Scatter Plot Matrix",diagonal="histogram")
ggvis Data Visualization
library(ggvis)##
## Attaching package: 'ggvis'## The following objects are masked from 'package:plotly':
##
## add_data, hide_legend## The following object is masked from 'package:ggplot2':
##
## resolutionairline %>% ggvis(~PriceEconomy, ~PricePremium, fill = ~PriceRelative) %>% layer_points()library(ggvis)
airline %>% ggvis(~PriceEconomy, ~WidthEconomy, fill = ~PriceRelative) %>% layer_points()library(ggvis)
airline %>% ggvis(~PricePremium, ~WidthPremium, fill = ~PriceRelative) %>% layer_points()library(ggvis)
airline %>% ggvis(~PriceEconomy, ~PriceRelative, fill = ~PricePremium) %>% layer_points()correlation test among comparable variables
correlationmatrix,cov-matrix,corrplot,corrgram
x<-airline[,c("FlightDuration","WidthEconomy","WidthPremium","SeatsEconomy","SeatsPremium","PitchEconomy","PitchPremium","PitchDifference","WidthDifference","SeatsTotal","PercentPremiumSeats")]
y<-airline[,c("PriceEconomy","PricePremium","PriceRelative")]
cor(x,y)## PriceEconomy PricePremium PriceRelative
## FlightDuration 0.56664039 0.64873981 0.121075014
## WidthEconomy 0.06799061 0.15054837 -0.043961160
## WidthPremium -0.05704522 0.06402004 0.504247591
## SeatsEconomy 0.12816722 0.17700093 0.003956939
## SeatsPremium 0.11364218 0.21761238 -0.097196009
## PitchEconomy 0.36866123 0.22614179 -0.423022038
## PitchPremium 0.05038455 0.08853915 0.417539056
## PitchDifference -0.09952511 -0.01806629 0.468730249
## WidthDifference -0.08449975 -0.01151218 0.485802437
## SeatsTotal 0.13243313 0.19232533 -0.011568942
## PercentPremiumSeats 0.06532232 0.11639097 -0.161565556cov(x,y)## PriceEconomy PricePremium PriceRelative
## FlightDuration 1983.54017 2959.97830 0.19323683
## WidthEconomy 37.46095 108.11612 -0.01104335
## WidthPremium -61.85450 90.47998 0.24928593
## SeatsEconomy 9673.79447 17413.25417 0.13616991
## SeatsPremium 1489.38360 3717.36429 -0.58078765
## PitchEconomy 238.70319 190.85172 -0.12488080
## PitchPremium 65.42513 149.85356 0.24719874
## PitchDifference -173.27806 -40.99816 0.37207954
## WidthDifference -99.31545 -17.63614 0.26032928
## SeatsTotal 11163.17806 21130.61846 -0.44461774
## PercentPremiumSeats 312.61077 726.01582 -0.35252750var(x,y)## PriceEconomy PricePremium PriceRelative
## FlightDuration 1983.54017 2959.97830 0.19323683
## WidthEconomy 37.46095 108.11612 -0.01104335
## WidthPremium -61.85450 90.47998 0.24928593
## SeatsEconomy 9673.79447 17413.25417 0.13616991
## SeatsPremium 1489.38360 3717.36429 -0.58078765
## PitchEconomy 238.70319 190.85172 -0.12488080
## PitchPremium 65.42513 149.85356 0.24719874
## PitchDifference -173.27806 -40.99816 0.37207954
## WidthDifference -99.31545 -17.63614 0.26032928
## SeatsTotal 11163.17806 21130.61846 -0.44461774
## PercentPremiumSeats 312.61077 726.01582 -0.35252750library(corrplot)## corrplot 0.84 loadedcorrplot(corr=cor(airline[,c(3,6:18)],use="complete.obs"),method="ellipse")
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowesscorrplot.mixed(corr=cor(airline[,c(3,6:18)],use ="complete.obs"), lower = "number", upper = "circle", tl.pos = c("d",
"lt", "n"), diag = c("n", "l", "u"), bg = "white", addgrid.col = "grey",
lower.col = NULL, upper.col = NULL)
library(corrgram)
corrgram(airline,order=FALSE,lower.panel=panel.shade,upper.panel=panel.pie,text.panel=panel.txt,main="Corrgram of Airline Ticket Pricing")
Hypothesis(B) Average cost of Economy seats in Boeing Aircraft is less than that of AirBus
t.test(PriceEconomy~Aircraft,alternative="less",data=airline)##
## Welch Two Sample t-test
##
## data: PriceEconomy by Aircraft
## t = 0.64317, df = 289.45, p-value = 0.7397
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 228.0809
## sample estimates:
## mean in group AirBus mean in group Boeing
## 1369.954 1305.987Hypothesis(C) Average cost of Economy seats in Domestic Aircraft is less than that of International
t.test(PriceEconomy~IsInternational,alternative="less",data=airline)##
## Welch Two Sample t-test
##
## data: PriceEconomy by IsInternational
## t = -20.368, df = 433.89, p-value < 2.2e-16
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf -977.2634
## sample estimates:
## mean in group Domestic mean in group International
## 356.625 1419.943Linear Regression Model fo Price of Economy Flights as Dependent variable
fit1<-lm(PriceEconomy~PitchEconomy+WidthEconomy+FlightDuration+PriceRelative+IsInternational,data=airline)
summary(fit1)##
## Call:
## lm(formula = PriceEconomy ~ PitchEconomy + WidthEconomy + FlightDuration +
## PriceRelative + IsInternational, data = airline)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1520.33 -470.75 53.75 499.90 1599.59
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1094.27 1876.87 -0.583 0.56
## PitchEconomy 424.30 58.30 7.278 1.52e-12 ***
## WidthEconomy -722.84 60.98 -11.854 < 2e-16 ***
## FlightDuration 157.98 10.33 15.286 < 2e-16 ***
## PriceRelative -801.77 73.05 -10.976 < 2e-16 ***
## IsInternationalInternational 1384.18 126.83 10.914 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 610.4 on 452 degrees of freedom
## Multiple R-squared: 0.6227, Adjusted R-squared: 0.6185
## F-statistic: 149.2 on 5 and 452 DF, p-value: < 2.2e-16Linear Regression Model fo PriceRelative as Dependent variable
fit<-lm(PriceRelative~Aircraft-SeatsEconomy-SeatsPremium-WidthEconomy,data=airline)
summary(fit)##
## Call:
## lm(formula = PriceRelative ~ Aircraft - SeatsEconomy - SeatsPremium -
## WidthEconomy, data = airline)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.4928 -0.3528 -0.1428 0.2422 1.3672
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.41477 0.03647 11.372 <2e-16 ***
## AircraftBoeing 0.10807 0.04455 2.426 0.0157 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4482 on 456 degrees of freedom
## Multiple R-squared: 0.01274, Adjusted R-squared: 0.01057
## F-statistic: 5.884 on 1 and 456 DF, p-value: 0.01566** Note:-Here no decriptive conclusion is written here,I’m trying to show the calculations,charts,plots,matrices which are very much needed for analysing the whole dataset and the answers or solutions about the RESEARCH QUESTION:-What factors explain the difference in price between an economy ticket and a premium-economy airline ticket?**
** So,here is the end of Airline Pricing Project. Thank You!**