premium economy class

Reading sixairlines data in R

setwd("C:/Users/Taiyyab Ali/Desktop/R language")
Airlines <- read.csv(paste("SixAirlinesDataV2.csv",sep=""))
#View(Airlines)
str(Airlines)

## 'data.frame':    458 obs. of  18 variables:
##  $ Airline            : Factor w/ 6 levels "AirFrance","British",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Aircraft           : Factor w/ 2 levels "AirBus","Boeing": 2 2 2 2 2 2 2 2 2 2 ...
##  $ FlightDuration     : num  12.25 12.25 12.25 12.25 8.16 ...
##  $ TravelMonth        : Factor w/ 4 levels "Aug","Jul","Oct",..: 2 1 4 3 1 4 3 1 4 4 ...
##  $ IsInternational    : Factor w/ 2 levels "Domestic","International": 2 2 2 2 2 2 2 2 2 2 ...
##  $ SeatsEconomy       : int  122 122 122 122 122 122 122 122 122 122 ...
##  $ SeatsPremium       : int  40 40 40 40 40 40 40 40 40 40 ...
##  $ PitchEconomy       : int  31 31 31 31 31 31 31 31 31 31 ...
##  $ PitchPremium       : int  38 38 38 38 38 38 38 38 38 38 ...
##  $ WidthEconomy       : int  18 18 18 18 18 18 18 18 18 18 ...
##  $ WidthPremium       : int  19 19 19 19 19 19 19 19 19 19 ...
##  $ PriceEconomy       : int  2707 2707 2707 2707 1793 1793 1793 1476 1476 1705 ...
##  $ PricePremium       : int  3725 3725 3725 3725 2999 2999 2999 2997 2997 2989 ...
##  $ PriceRelative      : num  0.38 0.38 0.38 0.38 0.67 0.67 0.67 1.03 1.03 0.75 ...
##  $ SeatsTotal         : int  162 162 162 162 162 162 162 162 162 162 ...
##  $ PitchDifference    : int  7 7 7 7 7 7 7 7 7 7 ...
##  $ WidthDifference    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ PercentPremiumSeats: num  24.7 24.7 24.7 24.7 24.7 ...

summary(Airlines)

##       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.   :24.69

dim(Airlines)

## [1] 458  18

library(psych)
describe(Airlines[ ,c(3,6:18)])[c(3,4,5,7,8,9)]

##                        mean      sd  median     mad   min     max
## FlightDuration         7.58    3.54    7.79    4.81  1.25   14.66
## SeatsEconomy         202.31   76.37  185.00   85.99 78.00  389.00
## SeatsPremium          33.65   13.26   36.00   11.86  8.00   66.00
## PitchEconomy          31.22    0.66   31.00    0.00 30.00   33.00
## PitchPremium          37.91    1.31   38.00    0.00 34.00   40.00
## WidthEconomy          17.84    0.56   18.00    0.00 17.00   19.00
## WidthPremium          19.47    1.10   19.00    0.00 17.00   21.00
## PriceEconomy        1327.08  988.27 1242.00 1159.39 65.00 3593.00
## PricePremium        1845.26 1288.14 1737.00 1845.84 86.00 7414.00
## PriceRelative          0.49    0.45    0.36    0.41  0.02    1.89
## SeatsTotal           235.96   85.29  227.00   90.44 98.00  441.00
## PitchDifference        6.69    1.76    7.00    0.00  2.00   10.00
## WidthDifference        1.63    1.19    1.00    0.00  0.00    4.00
## PercentPremiumSeats   14.65    4.84   13.21    2.68  4.71   24.69

count<-table(Airlines$Airline)
count

## 
## AirFrance   British     Delta       Jet Singapore    Virgin 
##        74       175        46        61        40        62

plot(count)

set<-table(Airlines$Aircraft,Airlines$Airline)
set

##         
##          AirFrance British Delta Jet Singapore Virgin
##   AirBus        36      47    12   7        16     33
##   Boeing        38     128    34  54        24     29

library(RColorBrewer)
myColors <- brewer.pal(6,"Set1")
plot(set,color=myColors)

mytable1<-table(Airlines$FlightDuration)
plot(mytable1)

boxplot(Airlines$FlightDuration,horizontal=TRUE,main = "Flight Duration's distribution")

table(Airlines$TravelMonth)

## 
## Aug Jul Oct Sep 
## 127  75 127 129

plot(Airlines$TravelMonth,col= "lightblue",main = "Frequecy of flights in travel months")

mytable2<-xtabs(~IsInternational+Airline,data=Airlines)
addmargins(mytable2)

##                Airline
## IsInternational AirFrance British Delta Jet Singapore Virgin Sum
##   Domestic              0       0    40   0         0      0  40
##   International        74     175     6  61        40     62 418
##   Sum                  74     175    46  61        40     62 458

boxplot(Airlines$SeatsEconomy,horizontal = TRUE,main = "no. of economy class seat distribution")

Inside box, maximum no of seats in economy class are 122 with 51 airline and outside the box maximum seat in economy class are 303 with 52 airline.

mytable3<-table(Airlines$SeatsPremium)
plot(mytable3,col="red",xlab = "unique no. of seats in airlines",ylab = "frequecy in data",main = "Premium economy seat distribution")

boxplot(Airlines$SeatsPremium,horizontal = TRUE)

mytable4 <- table(Airlines$PitchEconomy,Airlines$Airline)
addmargins(mytable4)

##      
##       AirFrance British Delta Jet Singapore Virgin Sum
##   30          0       0     0  54         0      0  54
##   31          0     175    18   0         0     62 255
##   32         74       0    23   7        40      0 144
##   33          0       0     5   0         0      0   5
##   Sum        74     175    46  61        40     62 458

library(lattice)
histogram(~PitchEconomy|Airline,data = Airlines,type = "count")

mytable4 <- xtabs(~Airline+PitchPremium,data = Airlines)
addmargins(mytable4)

##            PitchPremium
## Airline      34  35  38  40 Sum
##   AirFrance   0   0  74   0  74
##   British     0   0 175   0 175
##   Delta      31   9   6   0  46
##   Jet         0   0   7  54  61
##   Singapore   0   0  40   0  40
##   Virgin      0   0  62   0  62
##   Sum        31   9 364  54 458

library(lattice)
histogram(~PitchPremium|Airline,data = Airlines,type = "count")

table(Airlines$WidthEconomy)

## 
##  17  18  19 
## 114 304  40

median(Airlines$WidthEconomy)

## [1] 18

library(lattice)
histogram(~WidthEconomy|Airline,data = Airlines,type = "count")

table(Airlines$WidthPremium)

## 
##  17  18  19  20  21 
##  28  12 256  40 122

median(Airlines$WidthPremium)

## [1] 19

histogram(~WidthPremium|Airline, data = Airlines)

boxplot(Airlines$PriceEconomy,horizontal = TRUE, xlab = "Price of economy class",main = "distribution of price in economy class")

boxplot(PriceEconomy~Airline, data = Airlines,horizontal = TRUE,col= c("red","blue","yellow"), yaxt="n",xlab = "Price in USD", main= "Airline-wise Economy class-price distribution")
axis(side=2,at=c(1:6),labels = c("Aif","Bts","Delt","Jet","Sigp","Vg"))

boxplot(Airlines$PricePremium,horizontal = TRUE, xlab = "Price of Premiumeconomy class",main = "distribution of price in Premiumeconomy class")

boxplot(PricePremium~Airline, data = Airlines,horizontal = TRUE,col= c("red","blue","yellow"), yaxt="n",xlab = "Price in USD", main= "Airline-wise PremiumEconomy class-price distribution")
axis(side=2,at=c(1:6),labels = c("Aif","Bts","Delt","Jet","Sigp","Vg"))

boxplot(Airlines$SeatsTotal,horizontal = TRUE, main = "Total no. of seats in airlines" )

boxplot(Airlines$PriceRelative,horizontal = TRUE,main="relative price in USD")

boxplot(PriceRelative~Airline, data = Airlines,horizontal = TRUE,col= c("red","blue","yellow"), yaxt="n",xlab = "Price in USD", main= "Airline-wise relative price distribution")
axis(side=2,at=c(1:6),labels = c("Aif","Bts","Delt","Jet","Sigp","Vg"))

mytable6<-table(Airlines$PitchDifference,Airlines$Airline)
addmargins(mytable6)

##      
##       AirFrance British Delta Jet Singapore Virgin Sum
##   2           0       0    24   0         0      0  24
##   3           0       0    16   0         0      0  16
##   6          74       0     0   7        40      0 121
##   7           0     175     6   0         0     62 243
##   10          0       0     0  54         0      0  54
##   Sum        74     175    46  61        40     62 458

histogram(~PitchDifference|Airline, data=Airlines,type = "count")

table(Airlines$WidthDifference)

## 
##   0   1   2   3   4 
##  40 264  32  68  54

histogram(~WidthDifference|Airline, data=Airlines,type = "count")

boxplot(Airlines$PercentPremiumSeats,horizontal = TRUE)

plot(Airlines$PitchEconomy,Airlines$PriceEconomy)

plot(Airlines$Airline,Airlines$PriceRelative,horizontal=TRUE,yaxt="n", col=c("red","blue","yellow"))
axis(side=2,at=c(1:6),labels = c("Aif","Bts","Delt","Jet","Sigp","Vg"))

library(car)

## 
## Attaching package: 'car'

## The following object is masked from 'package:psych':
## 
##     logit

scatterplotMatrix(~Airline+WidthEconomy+WidthPremium+PitchEconomy+PitchPremium+SeatsEconomy+SeatsPremium+SeatsTotal,data=Airlines,cex=0.6)

## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth

## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit negative part of the spread

## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit negative part of the spread

## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth

library(corrgram)
library(ellipse)

## 
## Attaching package: 'ellipse'

## The following object is masked from 'package:car':
## 
##     ellipse

corrgram(Airlines, upper.panel=panel.pie,main= "Corrgram of store variables" )

cor(Airlines[ ,c(3,6:18)])

##                     FlightDuration SeatsEconomy SeatsPremium PitchEconomy
## FlightDuration          1.00000000  0.195621187  0.161236400   0.29377174
## SeatsEconomy            0.19562119  1.000000000  0.625056587   0.14412692
## SeatsPremium            0.16123640  0.625056587  1.000000000  -0.03421296
## PitchEconomy            0.29377174  0.144126924 -0.034212963   1.00000000
## PitchPremium            0.09621471  0.119221250  0.004883123  -0.55060624
## WidthEconomy            0.45647720  0.373670252  0.455782883   0.29448586
## WidthPremium            0.10343747  0.102431959 -0.002717527  -0.53929285
## PriceEconomy            0.56664039  0.128167220  0.113642176   0.36866123
## PricePremium            0.64873981  0.177000928  0.217612376   0.22614179
## PriceRelative           0.12107501  0.003956939 -0.097196009  -0.42302204
## SeatsTotal              0.20023299  0.992607966  0.715171053   0.12373524
## PitchDifference        -0.03749288  0.035318044  0.016365566  -0.78254993
## WidthDifference        -0.11856070 -0.080670148 -0.216168666  -0.63557430
## PercentPremiumSeats     0.06051625 -0.330935223  0.485029771  -0.10280880
##                     PitchPremium WidthEconomy WidthPremium PriceEconomy
## FlightDuration       0.096214708   0.45647720  0.103437469   0.56664039
## SeatsEconomy         0.119221250   0.37367025  0.102431959   0.12816722
## SeatsPremium         0.004883123   0.45578288 -0.002717527   0.11364218
## PitchEconomy        -0.550606241   0.29448586 -0.539292852   0.36866123
## PitchPremium         1.000000000  -0.02374087  0.750259029   0.05038455
## WidthEconomy        -0.023740873   1.00000000  0.081918728   0.06799061
## WidthPremium         0.750259029   0.08191873  1.000000000  -0.05704522
## PriceEconomy         0.050384550   0.06799061 -0.057045224   1.00000000
## PricePremium         0.088539147   0.15054837  0.064020043   0.90138870
## PriceRelative        0.417539056  -0.04396116  0.504247591  -0.28856711
## SeatsTotal           0.107512784   0.40545860  0.091297500   0.13243313
## PitchDifference      0.950591466  -0.12722421  0.760121272  -0.09952511
## WidthDifference      0.703281797  -0.39320512  0.884149655  -0.08449975
## PercentPremiumSeats -0.175487414   0.22714172 -0.183312058   0.06532232
##                     PricePremium PriceRelative  SeatsTotal PitchDifference
## FlightDuration        0.64873981   0.121075014  0.20023299     -0.03749288
## SeatsEconomy          0.17700093   0.003956939  0.99260797      0.03531804
## SeatsPremium          0.21761238  -0.097196009  0.71517105      0.01636557
## PitchEconomy          0.22614179  -0.423022038  0.12373524     -0.78254993
## PitchPremium          0.08853915   0.417539056  0.10751278      0.95059147
## WidthEconomy          0.15054837  -0.043961160  0.40545860     -0.12722421
## WidthPremium          0.06402004   0.504247591  0.09129750      0.76012127
## PriceEconomy          0.90138870  -0.288567110  0.13243313     -0.09952511
## PricePremium          1.00000000   0.031846537  0.19232533     -0.01806629
## PriceRelative         0.03184654   1.000000000 -0.01156894      0.46873025
## SeatsTotal            0.19232533  -0.011568942  1.00000000      0.03416915
## PitchDifference      -0.01806629   0.468730249  0.03416915      1.00000000
## WidthDifference      -0.01151218   0.485802437 -0.10584398      0.76089108
## PercentPremiumSeats   0.11639097  -0.161565556 -0.22091465     -0.09264869
##                     WidthDifference PercentPremiumSeats
## FlightDuration          -0.11856070          0.06051625
## SeatsEconomy            -0.08067015         -0.33093522
## SeatsPremium            -0.21616867          0.48502977
## PitchEconomy            -0.63557430         -0.10280880
## PitchPremium             0.70328180         -0.17548741
## WidthEconomy            -0.39320512          0.22714172
## WidthPremium             0.88414965         -0.18331206
## PriceEconomy            -0.08449975          0.06532232
## PricePremium            -0.01151218          0.11639097
## PriceRelative            0.48580244         -0.16156556
## SeatsTotal              -0.10584398         -0.22091465
## PitchDifference          0.76089108         -0.09264869
## WidthDifference          1.00000000         -0.27559416
## PercentPremiumSeats     -0.27559416          1.00000000

cov(Airlines[ ,c(3,6:18)])

##                     FlightDuration  SeatsEconomy  SeatsPremium
## FlightDuration          12.5462183    52.9194291    7.57372426
## SeatsEconomy            52.9194291  5832.9154300  633.07060954
## SeatsPremium             7.5737243   633.0706095  175.86521648
## PitchEconomy             0.6817421     7.2117665   -0.29725856
## PitchPremium             0.4477835    11.9637325    0.08508595
## WidthEconomy             0.9014224    15.9105138    3.36977440
## WidthPremium             0.4019845     8.5832800   -0.03954019
## PriceEconomy          1983.5401655  9673.7944684 1489.38359627
## PricePremium          2959.9783043 17413.2541733 3717.36428960
## PriceRelative            0.1932368     0.1361699   -0.58078765
## SeatsTotal              60.4931534  6465.9860396  808.93582602
## PitchDifference         -0.2339587     4.7519660    0.38234451
## WidthDifference         -0.4994380    -7.3272338   -3.40931459
## PercentPremiumSeats      1.0379912  -122.3914537   31.14753127
##                     PitchEconomy PitchPremium WidthEconomy WidthPremium
## FlightDuration         0.6817421   0.44778348   0.90142242   0.40198446
## SeatsEconomy           7.2117665  11.96373253  15.91051379   8.58327998
## SeatsPremium          -0.2972586   0.08508595   3.36977440  -0.03954019
## PitchEconomy           0.4292471  -0.47398546   0.10756500  -0.38766208
## PitchPremium          -0.4739855   1.72639580  -0.01739081   1.08157435
## WidthEconomy           0.1075650  -0.01739081   0.31081765   0.05010845
## WidthPremium          -0.3876621   1.08157435   0.05010845   1.20378776
## PriceEconomy         238.7031905  65.42513354  37.46095191 -61.85450011
## PricePremium         190.8517195 149.85356368 108.11611707  90.47997668
## PriceRelative         -0.1248808   0.24719874  -0.01104335   0.24928593
## SeatsTotal             6.9145079  12.04881848  19.28028819   8.54373979
## PitchDifference       -0.9032326   2.20038126  -0.12495581   1.46923643
## WidthDifference       -0.4952271   1.09896515  -0.26070920   1.15367930
## PercentPremiumSeats   -0.3261739  -1.11655834   0.61321816  -0.97393787
##                      PriceEconomy  PricePremium PriceRelative
## FlightDuration         1983.54017    2959.97830    0.19323683
## 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
## WidthEconomy             37.46095     108.11612   -0.01104335
## WidthPremium            -61.85450      90.47998    0.24928593
## PriceEconomy         976684.06198 1147494.76801 -128.49991725
## PricePremium        1147494.76801 1659293.11947   18.48428836
## PriceRelative          -128.49992      18.48429    0.20302893
## SeatsTotal            11163.17806   21130.61846   -0.44461774
## PitchDifference        -173.27806     -40.99816    0.37207954
## WidthDifference         -99.31545     -17.63614    0.26032928
## PercentPremiumSeats     312.61077     726.01582   -0.35252750
##                        SeatsTotal PitchDifference WidthDifference
## FlightDuration         60.4931534      -0.2339587      -0.4994380
## SeatsEconomy         6465.9860396       4.7519660      -7.3272338
## SeatsPremium          808.9358260       0.3823445      -3.4093146
## PitchEconomy            6.9145079      -0.9032326      -0.4952271
## PitchPremium           12.0488185       2.2003813       1.0989652
## WidthEconomy           19.2802882      -0.1249558      -0.2607092
## WidthPremium            8.5437398       1.4692364       1.1536793
## PriceEconomy        11163.1780647    -173.2780570     -99.3154520
## PricePremium        21130.6184629     -40.9981558     -17.6361404
## PriceRelative          -0.4446177       0.3720795       0.2603293
## SeatsTotal           7274.9218656       5.1343105     -10.7365484
## PitchDifference         5.1343105       3.1036138       1.5941922
## WidthDifference       -10.7365484       1.5941922       1.4143885
## PercentPremiumSeats   -91.2439224      -0.7903844      -1.5871560
##                     PercentPremiumSeats
## FlightDuration                1.0379912
## SeatsEconomy               -122.3914537
## SeatsPremium                 31.1475313
## PitchEconomy                 -0.3261739
## PitchPremium                 -1.1165583
## WidthEconomy                  0.6132182
## WidthPremium                 -0.9739379
## PriceEconomy                312.6107669
## PricePremium                726.0158229
## PriceRelative                -0.3525275
## SeatsTotal                  -91.2439224
## PitchDifference              -0.7903844
## WidthDifference              -1.5871560
## PercentPremiumSeats          23.4493343

Britishairways <- Airlines[which(Airlines$Airline=="British"),]
View(Britishairways)

fit <- lm(PriceEconomy~WidthEconomy+PitchEconomy+TravelMonth+FlightDuration+Aircraft+SeatsEconomy,data=Britishairways)
summary(fit)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy + PitchEconomy + TravelMonth + 
##     FlightDuration + Aircraft + SeatsEconomy, data = Britishairways)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1543.90  -286.68    56.45   340.33  1087.57 
## 
## Coefficients: (2 not defined because of singularities)
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    191.7574   272.9826   0.702   0.4834    
## WidthEconomy         NA         NA      NA       NA    
## PitchEconomy         NA         NA      NA       NA    
## TravelMonthJul 240.9518   157.4555   1.530   0.1278    
## TravelMonthOct -65.8724   107.1421  -0.615   0.5395    
## TravelMonthSep   0.4950   106.6531   0.005   0.9963    
## FlightDuration 124.1934    12.3685  10.041   <2e-16 ***
## AircraftBoeing 352.6527   136.2346   2.589   0.0105 *  
## SeatsEconomy    -0.6186     0.7884  -0.785   0.4338    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 548.9 on 168 degrees of freedom
## Multiple R-squared:  0.5237, Adjusted R-squared:  0.5067 
## F-statistic: 30.79 on 6 and 168 DF,  p-value: < 2.2e-16

Airfrance <- Airlines[which(Airlines$Airline=="AirFrance"),]

model <- lm(PriceEconomy~WidthEconomy+PitchEconomy+TravelMonth+FlightDuration+Aircraft+SeatsEconomy,data=Airfrance)
summary(model)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy + PitchEconomy + TravelMonth + 
##     FlightDuration + Aircraft + SeatsEconomy, data = Airfrance)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1999.00   -31.15   188.87   421.55   712.66 
## 
## Coefficients: (1 not defined because of singularities)
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    -6733.767   7278.745  -0.925   0.3583  
## WidthEconomy     489.737    414.926   1.180   0.2421  
## PitchEconomy          NA         NA      NA       NA  
## TravelMonthJul   138.990    272.623   0.510   0.6119  
## TravelMonthOct  -149.180    235.937  -0.632   0.5294  
## TravelMonthSep   -56.372    229.178  -0.246   0.8065  
## FlightDuration    94.981     58.059   1.636   0.1066  
## AircraftBoeing   765.273    408.974   1.871   0.0658 .
## SeatsEconomy      -1.455      1.255  -1.160   0.2503  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 737.2 on 66 degrees of freedom
## Multiple R-squared:  0.1258, Adjusted R-squared:  0.03306 
## F-statistic: 1.357 on 7 and 66 DF,  p-value: 0.2386

Delta <- Airlines[which(Airlines$Airline=="Delta"),]

model1 <- lm(PriceEconomy~WidthEconomy+PitchEconomy+TravelMonth+FlightDuration+Aircraft+SeatsEconomy,data=Delta)
summary(model1)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy + PitchEconomy + TravelMonth + 
##     FlightDuration + Aircraft + SeatsEconomy, data = Delta)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -352.03  -78.85  -21.92   92.57  365.29 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -1869.365   2940.172  -0.636 0.528819    
## WidthEconomy     171.853     71.732   2.396 0.021758 *  
## PitchEconomy     -35.754     66.068  -0.541 0.591639    
## TravelMonthJul   158.611     74.474   2.130 0.039908 *  
## TravelMonthOct   -28.649     73.659  -0.389 0.699552    
## TravelMonthSep   -50.231     79.468  -0.632 0.531214    
## FlightDuration   143.263     34.161   4.194 0.000164 ***
## AircraftBoeing  -383.143     84.959  -4.510 6.34e-05 ***
## SeatsEconomy       1.947      1.800   1.082 0.286374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 170.1 on 37 degrees of freedom
## Multiple R-squared:  0.9207, Adjusted R-squared:  0.9035 
## F-statistic: 53.69 on 8 and 37 DF,  p-value: < 2.2e-16

Jet <- Airlines[which(Airlines$Airline=="Jet"),]

model2 <- lm(PriceEconomy~WidthEconomy+PitchEconomy+TravelMonth+FlightDuration+Aircraft+SeatsEconomy,data=Jet)
summary(model2)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy + PitchEconomy + TravelMonth + 
##     FlightDuration + Aircraft + SeatsEconomy, data = Jet)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -179.78  -87.99  -30.03   73.55  339.25 
## 
## Coefficients: (2 not defined because of singularities)
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -7375.3950  1852.4467  -3.981 0.000206 ***
## WidthEconomy     438.5707   111.3965   3.937 0.000238 ***
## PitchEconomy           NA         NA      NA       NA    
## TravelMonthJul    37.9981    44.0750   0.862 0.392432    
## TravelMonthOct   -37.7187    43.9830  -0.858 0.394919    
## TravelMonthSep    18.1947    43.9877   0.414 0.680782    
## FlightDuration   -28.7435    17.4256  -1.649 0.104851    
## AircraftBoeing         NA         NA      NA       NA    
## SeatsEconomy       1.8537     0.9751   1.901 0.062628 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 122.2 on 54 degrees of freedom
## Multiple R-squared:  0.4375, Adjusted R-squared:  0.375 
## F-statistic: 6.999 on 6 and 54 DF,  p-value: 1.529e-05

Singapore <- Airlines[which(Airlines$Airline=="Singapore"),]

model3 <- lm(PriceEconomy~WidthEconomy+PitchEconomy+TravelMonth+FlightDuration+Aircraft+SeatsEconomy,data=Singapore)
summary(model3)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy + PitchEconomy + TravelMonth + 
##     FlightDuration + Aircraft + SeatsEconomy, data = Singapore)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -416.66 -214.39   31.11  209.34  382.63 
## 
## Coefficients: (3 not defined because of singularities)
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     2.508e+02  1.481e+02   1.693  0.09964 .  
## WidthEconomy           NA         NA      NA       NA    
## PitchEconomy           NA         NA      NA       NA    
## TravelMonthJul -4.642e+01  1.201e+02  -0.386  0.70164    
## TravelMonthOct  4.167e+01  1.124e+02   0.371  0.71319    
## TravelMonthSep -1.195e-13  1.097e+02   0.000  1.00000    
## FlightDuration  3.601e+01  1.173e+01   3.071  0.00418 ** 
## AircraftBoeing  3.849e+02  8.492e+01   4.533 6.87e-05 ***
## SeatsEconomy           NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 257.2 on 34 degrees of freedom
## Multiple R-squared:  0.5278, Adjusted R-squared:  0.4583 
## F-statistic: 7.599 on 5 and 34 DF,  p-value: 6.991e-05

Virgin <- Airlines[which(Airlines$Airline=="Virgin"),]

model4 <- lm(PriceEconomy~WidthEconomy+PitchEconomy+TravelMonth+FlightDuration+Aircraft+SeatsEconomy,data=Virgin)
summary(model4)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy + PitchEconomy + TravelMonth + 
##     FlightDuration + Aircraft + SeatsEconomy, data = Virgin)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1199.1  -262.9   125.8   402.9   960.5 
## 
## Coefficients: (2 not defined because of singularities)
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     784.911    504.113   1.557  0.12520   
## WidthEconomy         NA         NA      NA       NA   
## PitchEconomy         NA         NA      NA       NA   
## TravelMonthJul  -35.824    189.780  -0.189  0.85097   
## TravelMonthOct   28.172    184.053   0.153  0.87891   
## TravelMonthSep    5.480    182.475   0.030  0.97615   
## FlightDuration   11.558     47.790   0.242  0.80980   
## AircraftBoeing -246.385    190.636  -1.292  0.20161   
## SeatsEconomy      3.590      1.167   3.076  0.00327 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 516 on 55 degrees of freedom
## Multiple R-squared:  0.1546, Adjusted R-squared:  0.06234 
## F-statistic: 1.676 on 6 and 55 DF,  p-value: 0.1443

table(Airlines$IsInternational)

## 
##      Domestic International 
##            40           418

model5 <- lm(PriceRelative ~ WidthDifference + PitchDifference + IsInternational + Airline + SeatsTotal + PercentPremiumSeats, data = Airlines)
summary(model5)

## 
## Call:
## lm(formula = PriceRelative ~ WidthDifference + PitchDifference + 
##     IsInternational + Airline + SeatsTotal + PercentPremiumSeats, 
##     data = Airlines)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.94187 -0.20225 -0.05734  0.11501  1.38928 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.1749001  0.2696423   0.649 0.516906    
## WidthDifference               0.0270691  0.0774293   0.350 0.726805    
## PitchDifference               0.0365747  0.0634478   0.576 0.564599    
## IsInternationalInternational  0.1149537  0.2334593   0.492 0.622683    
## AirlineBritish                0.3348407  0.1099740   3.045 0.002466 ** 
## AirlineDelta                  0.1709651  0.1769791   0.966 0.334557    
## AirlineJet                    0.4746211  0.1436209   3.305 0.001027 ** 
## AirlineSingapore              0.3586353  0.0798399   4.492 9.00e-06 ***
## AirlineVirgin                 0.5719661  0.1076120   5.315 1.69e-07 ***
## SeatsTotal                   -0.0005150  0.0003017  -1.707 0.088506 .  
## PercentPremiumSeats          -0.0189117  0.0056374  -3.355 0.000862 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3685 on 447 degrees of freedom
## Multiple R-squared:  0.3459, Adjusted R-squared:  0.3312 
## F-statistic: 23.63 on 10 and 447 DF,  p-value: < 2.2e-16

model6 <- lm(PriceRelative ~ PercentPremiumSeats + WidthDifference + PitchDifference + IsInternational, data = Airlines)
summary(model6)

## 
## Call:
## lm(formula = PriceRelative ~ PercentPremiumSeats + WidthDifference + 
##     PitchDifference + IsInternational, data = Airlines)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.91971 -0.28760 -0.05318  0.20534  1.19107 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.037996   0.097299  -0.391 0.696342    
## PercentPremiumSeats          -0.005733   0.003969  -1.445 0.149278    
## WidthDifference               0.093953   0.026269   3.577 0.000386 ***
## PitchDifference               0.085906   0.023499   3.656 0.000286 ***
## IsInternationalInternational -0.130161   0.104195  -1.249 0.212235    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3879 on 453 degrees of freedom
## Multiple R-squared:  0.2653, Adjusted R-squared:  0.2588 
## F-statistic: 40.89 on 4 and 453 DF,  p-value: < 2.2e-16

model7 <- lm(PriceEconomy ~ WidthEconomy + PitchEconomy + SeatsEconomy + SeatsPremium,data = Airlines)
summary(model7)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthEconomy + PitchEconomy + SeatsEconomy + 
##     SeatsPremium, data = Airlines)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2298.01  -729.33   -25.68   665.18  2091.50 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.428e+04  2.228e+03  -6.411 3.64e-10 ***
## WidthEconomy -2.534e+02  9.132e+01  -2.775  0.00575 ** 
## PitchEconomy  6.293e+02  7.024e+01   8.958  < 2e-16 ***
## SeatsEconomy  1.663e-02  7.291e-01   0.023  0.98181    
## SeatsPremium  1.433e+01  4.461e+00   3.212  0.00141 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 906.5 on 453 degrees of freedom
## Multiple R-squared:  0.1661, Adjusted R-squared:  0.1587 
## F-statistic: 22.56 on 4 and 453 DF,  p-value: < 2.2e-16

model8 <- lm(PriceEconomy ~ WidthPremium + PitchPremium + SeatsPremium + SeatsEconomy , data = Airlines)
summary(model8)

## 
## Call:
## lm(formula = PriceEconomy ~ WidthPremium + PitchPremium + SeatsPremium + 
##     SeatsEconomy, data = Airlines)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1578.0  -782.2  -122.1   763.0  2231.4 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -1009.1466  1349.9841  -0.748  0.45513   
## WidthPremium  -197.2073    62.7312  -3.144  0.00178 **
## PitchPremium   152.9595    52.4996   2.914  0.00375 **
## SeatsPremium     4.0458     4.4129   0.917  0.35972   
## SeatsEconomy     1.1958     0.7719   1.549  0.12201   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 972.2 on 453 degrees of freedom
## Multiple R-squared:  0.04075,    Adjusted R-squared:  0.03228 
## F-statistic: 4.811 on 4 and 453 DF,  p-value: 0.0008269

conclusion

The difference in price of economy and premium economy class dependence on Type of airline,flightduration,width of seat , Pitch of seat and no. of seats present in airline.