Airline Industry
Anubhav Agarwal
23 December 2017
Read
airline=read.csv(paste("SixAirlinesDataV2.csv",sep=""))
attach(airline)View Dataset
View(airline)Summary
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. :24.69Data Types
str(airline)## '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 ...Describe
library(psych)## Warning: package 'psych' was built under R version 3.4.3describe(airline[,6:14])## vars n mean sd median trimmed mad min
## SeatsEconomy 1 458 202.31 76.37 185.00 194.64 85.99 78.00
## SeatsPremium 2 458 33.65 13.26 36.00 33.35 11.86 8.00
## PitchEconomy 3 458 31.22 0.66 31.00 31.26 0.00 30.00
## PitchPremium 4 458 37.91 1.31 38.00 38.05 0.00 34.00
## WidthEconomy 5 458 17.84 0.56 18.00 17.81 0.00 17.00
## WidthPremium 6 458 19.47 1.10 19.00 19.53 0.00 17.00
## PriceEconomy 7 458 1327.08 988.27 1242.00 1244.40 1159.39 65.00
## PricePremium 8 458 1845.26 1288.14 1737.00 1799.05 1845.84 86.00
## PriceRelative 9 458 0.49 0.45 0.36 0.42 0.41 0.02
## max range skew kurtosis se
## 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#describe(PricePremium)Comparing variables of the dataset
library(car)## Warning: package 'car' was built under R version 3.4.3##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitpar(mfrow=c(1,2))
boxplot(PriceEconomy, main="Boxplot of PriceEconomy")
boxplot(PricePremium, main="Boxplot of PricePremium")
library(car)
par(mfrow=c(1,2))
hist(PriceEconomy,col="Grey")
hist(PricePremium,xlim = c(0,5000),col="Beige")
library(car)
par(mfrow=c(1,2))
hist(FlightDuration,col="lightblue",xlim=c(0,15),ylim = c(0,60))
boxplot(FlightDuration,main="Boxplot of flight duration",ylab="Fight Duration")
table(IsInternational)## IsInternational
## Domestic International
## 40 418by(PriceEconomy,IsInternational,mean)## IsInternational: Domestic
## [1] 356.625
## --------------------------------------------------------
## IsInternational: International
## [1] 1419.943by(PricePremium,IsInternational,mean)## IsInternational: Domestic
## [1] 385.9
## --------------------------------------------------------
## IsInternational: International
## [1] 1984.909Comparing Domestic and International Flight prices
par(mfrow=c(1,2))
boxplot(PriceEconomy~IsInternational,data=airline, ylab="PriceEconomy",col = c("purple","navy"))
boxplot(PricePremium~IsInternational,data=airline, ylab="PricePremium",col = c("purple","navy"))
From the above tables and boxplots, we can conclude that the Domestic Flights have a much lower price (both PricePremium and PriceEconomy) as compared to International Flights
Pricing based on Aircrafts
table(Aircraft)## Aircraft
## AirBus Boeing
## 151 307by(PriceEconomy,Aircraft,mean)## Aircraft: AirBus
## [1] 1369.954
## --------------------------------------------------------
## Aircraft: Boeing
## [1] 1305.987by(PricePremium,Aircraft,mean)## Aircraft: AirBus
## [1] 1869.503
## --------------------------------------------------------
## Aircraft: Boeing
## [1] 1833.332Comparing prices of Airbus and Boeing Aircrafts
par(mfrow=c(1,2))
boxplot(PriceEconomy~Aircraft,data=airline, ylab="PriceEconomy",col = c("purple","navy"))
boxplot(PricePremium~Aircraft,data=airline, ylab="PricePremium",col = c("purple","navy"))
It can be observed that Airbus aircrafts have slightly higher price than Boeing aircrafts. The differnce is higher when we consider only PricePremium
Histogram to compare the number of Economy and Premium Seats
library(car)
par(mfrow=c(1,2))
hist(SeatsEconomy,col="Grey")
hist(SeatsPremium,col="Beige")
There are lower number of Premium Seats in a flight
library(car)
par(mfrow=c(1,2))
hist(WidthEconomy,breaks = 10,xlim = c(17,19), col="lightblue",main="Width of Economy Seat")
hist(WidthPremium,breaks=15,xlim=c(17,21),col="LightGreen",main="Width of Premium Seat")
Premium Seats have higher Width as compared to Economy Seats
Compare Pricing of different Airlines
boxplot(PriceEconomy~Airline,data=airline, ylab="PriceEconomy",col = c("purple","navy"))
par(mfrow=c(1,1))
boxplot(PricePremium~Airline,data=airline, ylab="PricePremium",col = c("purple","navy","yellow","red","pink","darkgreen"))
On an average it can be seen that prices vary with Airlines, with AirFrance being the costliest and Jet and Delta being the cheaper ones of all Airlines
plot(TravelMonth,main = "Comparing Monthly Travels",col="pink",ylim=c(0,140),ylab="Number Of Flights",xlab="Month")
The number of fligts is relatively low in July as compared to Aug, Sept and Oct which have almost similar number of flights
par(mfrow=c(1,1))
boxplot(PriceEconomy~TravelMonth,data=airline, ylab="PriceEconomy",col = c("purple","navy","red","darkgreen"))
par(mfrow=c(1,1))
boxplot(PricePremium~TravelMonth,data=airline, ylab="PricePremium",col = c("purple","navy","red","darkgreen"))
par(mfrow=c(1,1))
boxplot(PriceRelative~WidthDifference,data=airline, ylab="PriceRelative",xlab="WidthDifference",col = c("purple","navy","red","darkgreen"))
scatterplot(WidthDifference,PriceRelative, main="PriceRelative vs WidthDifference")
par(mfrow=c(1,1))
boxplot(PriceRelative~PitchDifference,data=airline, ylab="PriceRelative",xlab="PitchDifference",col = c("purple","navy","red","darkgreen"))
scatterplot(PitchDifference,PriceRelative, main="PriceRelative vs PitchDifference")
scatterplot(FlightDuration,PriceEconomy,main="PriceEconomy vs FlightDuration")
scatterplot(FlightDuration,PricePremium,main="PricePremium vs FlightDuration")
scatterplot(SeatsEconomy,PriceEconomy,main="PriceEconomy vs SeatsEconomy")
scatterplot(SeatsPremium,PricePremium,main="PricePremium vs SeatsPremium")
scatterplot(PercentPremiumSeats,PricePremium,main="PricePremium vs PercentPremiumSeats")
scatterplot(SeatsPremium,PricePremium,main="PricePremium vs SeatsPremium")
Corrgram of Data
library(corrgram)## Warning: package 'corrgram' was built under R version 3.4.3corrgram(airline,upper.panel = panel.pie,main="Corrgram of data",cex.labels =.6)
Correlation Matrix
cor(airline[,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 1.00000000Pearson’s Correlation Tests
cor.test(PriceEconomy,FlightDuration)##
## Pearson's product-moment correlation
##
## data: PriceEconomy and FlightDuration
## t = 14.685, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5010266 0.6257772
## sample estimates:
## cor
## 0.5666404Therefore, the PriceEconomy has a high positive correlation with FlightDuration
cor.test(PricePremium,FlightDuration)##
## Pearson's product-moment correlation
##
## data: PricePremium and FlightDuration
## t = 18.204, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5923218 0.6988270
## sample estimates:
## cor
## 0.6487398Therefore, the PricePremiuim has a high positive correlation with FlightDuration and it is higher than that of PriceEconomy Therefore with increasing FlightDuration, the PricePremium goes higher as compared to PriceEconomy
cor.test(PriceRelative,FlightDuration)##
## Pearson's product-moment correlation
##
## data: PriceRelative and FlightDuration
## t = 2.6046, df = 456, p-value = 0.009498
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02977856 0.21036806
## sample estimates:
## cor
## 0.121075As FlightDuration increases, the relative price also increases slightly
cor.test(PriceRelative,WidthDifference)##
## Pearson's product-moment correlation
##
## data: PriceRelative and WidthDifference
## t = 11.869, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4125388 0.5528218
## sample estimates:
## cor
## 0.4858024The WidthDifference is a statistically significant variable which is positively correlated with PriceRelative. As WidthDifference increases, PriceRelative increases
cor.test(PriceRelative,PitchDifference)##
## Pearson's product-moment correlation
##
## data: PriceRelative and PitchDifference
## t = 11.331, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3940262 0.5372817
## sample estimates:
## cor
## 0.4687302Similar to WidthDifference, PitchDifference is also positively correlated with PriceRelative with nearly same correlation coefficients
cor.test(PriceRelative,SeatsTotal)##
## Pearson's product-moment correlation
##
## data: PriceRelative and SeatsTotal
## t = -0.24706, df = 456, p-value = 0.805
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.10308648 0.08014282
## sample estimates:
## cor
## -0.01156894Since p-value is greater than 0.05, therefore SeatsTotal is statistically insignificant for PriceRelative determination
cor.test(PriceRelative,PercentPremiumSeats)##
## Pearson's product-moment correlation
##
## data: PriceRelative and PercentPremiumSeats
## t = -3.496, df = 456, p-value = 0.0005185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.24949885 -0.07098966
## sample estimates:
## cor
## -0.1615656As PercentPremiumSeats increases, the PriceRelative decreases as they have a small negative correlation coefficient
cor.test(PriceRelative,PriceEconomy)##
## Pearson's product-moment correlation
##
## data: PriceRelative and PriceEconomy
## t = -6.4359, df = 456, p-value = 3.112e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3704004 -0.2022889
## sample estimates:
## cor
## -0.2885671cor.test(PriceRelative,PricePremium)##
## Pearson's product-moment correlation
##
## data: PriceRelative and PricePremium
## t = 0.6804, df = 456, p-value = 0.4966
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05995522 0.12311410
## sample estimates:
## cor
## 0.03184654From the above two tests, it is clear that PriceEconomy is statistically significant in determining PriceRelative whereas PricePremium is not. Moreover, PriceEconomy is negatively correlated with PriceRelative.
Linear Regression
model=lm((PricePremium-PriceEconomy)~Airline+TravelMonth+FlightDuration+PitchDifference+WidthDifference+PercentPremiumSeats+PriceRelative)
summary(model)##
## Call:
## lm(formula = (PricePremium - PriceEconomy) ~ Airline + TravelMonth +
## FlightDuration + PitchDifference + WidthDifference + PercentPremiumSeats +
## PriceRelative)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1011.49 -137.89 -7.32 126.76 2639.76
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -415.6657 249.0686 -1.669 0.095846 .
## AirlineBritish 207.6631 101.8512 2.039 0.042053 *
## AirlineDelta 190.0222 123.7042 1.536 0.125226
## AirlineJet -470.4240 112.4894 -4.182 3.48e-05 ***
## AirlineSingapore -246.5516 77.6113 -3.177 0.001593 **
## AirlineVirgin 357.4502 97.9908 3.648 0.000296 ***
## TravelMonthJul 3.8222 51.8044 0.074 0.941217
## TravelMonthOct -24.8072 44.2275 -0.561 0.575149
## TravelMonthSep 1.9367 43.9993 0.044 0.964911
## FlightDuration 51.1047 5.9508 8.588 < 2e-16 ***
## PitchDifference 15.5345 54.4353 0.285 0.775490
## WidthDifference 0.1372 73.4688 0.002 0.998511
## PercentPremiumSeats 0.3384 4.3547 0.078 0.938101
## PriceRelative 781.1639 46.6975 16.728 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 351.8 on 444 degrees of freedom
## Multiple R-squared: 0.6475, Adjusted R-squared: 0.6371
## F-statistic: 62.72 on 13 and 444 DF, p-value: < 2.2e-16Since this model has a very low p-value, therefore it is statistically significant.
Coefficients of Dependent Variables in the Linear Regression Model
model$coefficients## (Intercept) AirlineBritish AirlineDelta
## -415.6656568 207.6631450 190.0221516
## AirlineJet AirlineSingapore AirlineVirgin
## -470.4240358 -246.5516353 357.4502082
## TravelMonthJul TravelMonthOct TravelMonthSep
## 3.8222104 -24.8071526 1.9367112
## FlightDuration PitchDifference WidthDifference
## 51.1047292 15.5344580 0.1372069
## PercentPremiumSeats PriceRelative
## 0.3383643 781.1638907# Here is the PriceDifference, as predicted by the OLS model
fitted(model)## 1 2 3 4 5 6
## 835.927537 832.105327 834.042038 807.298174 849.624513 851.561224
## 7 8 9 10 11 12
## 824.817360 1046.009663 1047.946374 1084.744131 1058.000267 934.386280
## 13 14 15 16 17 18
## 703.859324 907.228103 909.164814 882.420950 674.191714 676.128425
## 19 20 21 22 23 24
## 649.384561 519.782761 521.719472 494.975608 507.371696 509.308407
## 25 26 27 28 29 30
## 482.564544 629.803858 616.117291 589.373428 425.750059 427.686770
## 31 32 33 34 35 36
## 400.942906 436.872273 433.050063 434.986774 408.242910 1106.900489
## 37 38 39 40 41 42
## 1108.837200 1082.093336 296.262773 292.440562 294.377274 267.633410
## 43 44 45 46 47 48
## 318.504562 320.441273 293.697410 393.337243 395.273954 368.530090
## 49 50 51 52 53 54
## 967.020636 968.957347 942.213484 1020.796473 756.283479 758.220190
## 55 56 57 58 59 60
## 731.476326 485.136555 487.073266 460.329402 711.677611 690.617194
## 61 62 63 64 65 66
## 999.336014 1040.817367 1036.995157 1038.931868 1012.188004 817.638569
## 67 68 69 70 71 72
## 813.816359 815.753070 789.009206 622.831777 624.768488 598.024624
## 73 74 75 76 77 78
## 532.085723 3.446377 11.369726 -8.354691 -39.539290 -23.185777
## 79 80 81 82 83 84
## -47.632074 -23.346683 -27.336111 1095.926338 1092.104128 1094.040839
## 85 86 87 88 89 90
## 1067.296975 607.052868 608.989579 582.245716 610.875079 -188.304635
## 91 92 93 94 95 96
## -192.126846 -190.190135 -216.933998 -309.301429 -307.364718 -334.108582
## 97 98 99 100 101 102
## -360.160691 102.827994 862.994472 859.172261 861.108972 834.365109
## 103 104 105 106 107 108
## 1157.354184 1153.531974 1155.468685 1128.724821 941.670063 943.606775
## 109 110 111 112 113 114
## 916.862911 1470.416807 1443.672943 757.620955 163.387017 167.144496
## 115 116 117 118 119 120
## 169.081208 64.304340 158.091575 160.028286 151.521219 87.509354
## 121 122 123 124 125 126
## 68.293769 70.230480 115.163602 156.719339 136.214903 189.207177
## 127 128 129 130 131 132
## 253.219042 59.109894 244.035167 140.204332 142.420729 192.516124
## 133 134 135 136 137 138
## 239.566694 193.245772 278.590670 169.067271 246.006098 173.056700
## 139 140 141 142 143 144
## 179.593425 181.199237 154.455374 204.303256 206.239967 206.239967
## 145 146 147 148 149 150
## 259.511927 252.429936 200.579995 202.516706 257.612492 246.464033
## 151 152 153 154 155 156
## 207.019398 148.700934 134.464869 121.276589 107.040524 2056.488305
## 157 158 159 160 161 162
## 2052.666094 2054.602806 2027.858942 2024.778270 2026.714981 1999.971117
## 163 164 165 166 167 168
## 1755.193118 1324.001743 1320.179532 1322.116243 1295.372379 1324.165178
## 169 170 171 172 173 174
## 1320.342968 1322.279679 1295.535815 1219.390356 1136.368882 1007.594325
## 175 176 177 178 179 180
## 1003.772115 1005.708826 978.964962 1064.691896 992.999346 763.769602
## 181 182 183 184 185 186
## 759.947392 761.884103 735.140239 903.956426 673.290973 648.483820
## 187 188 189 190 191 192
## 670.590395 672.527106 645.783242 550.241462 1256.294137 1252.471926
## 193 194 195 196 197 198
## 1254.408638 1227.664774 1200.371382 1196.549171 1198.485882 1171.742019
## 199 200 201 202 203 204
## 1247.681355 1243.859145 1245.795856 1017.468134 1019.404845 758.466695
## 205 206 207 208 209 210
## 754.644484 756.581195 729.837332 624.427550 533.681496 531.795997
## 211 212 213 214 215 216
## 505.052133 1361.211296 1083.301243 659.536031 173.925568 170.103357
## 217 218 219 220 221 222
## 172.040069 145.296205 173.925568 170.103357 172.040069 145.296205
## 223 224 225 226 227 228
## 118.998628 120.935339 94.191476 87.571336 60.827472 191.932462
## 229 230 231 232 233 234
## 193.869173 167.125309 128.017331 178.306393 174.484183 179.998225
## 235 236 237 238 239 240
## 153.254361 177.284299 173.462088 179.998225 153.254361 1319.956228
## 241 242 243 244 245 246
## 1295.149076 642.280354 818.916573 820.853284 794.109420 696.729878
## 247 248 249 250 251 252
## 692.907668 694.844379 668.100515 1109.259990 1111.196701 1084.452837
## 253 254 255 256 257 258
## 749.952055 746.129845 748.066556 721.322692 663.266925 665.203636
## 259 260 261 262 263 264
## 638.459772 1245.235846 675.896590 677.833301 651.089437 588.215644
## 265 266 267 268 269 270
## 590.152355 563.408491 985.515050 321.743756 323.680467 296.936603
## 271 272 273 274 275 276
## 1355.437680 1330.630527 1203.027112 646.515250 960.707898 608.101272
## 277 278 279 280 281 282
## 604.279062 606.215773 433.043412 891.937465 139.682861 111.405856
## 283 284 285 286 287 288
## 140.009528 142.968333 116.735517 155.345058 153.459559 126.715695
## 289 290 291 292 293 294
## 120.336323 87.970939 97.169790 143.711208 105.477500 114.568873
## 295 296 297 298 299 300
## -47.129128 -18.266635 -10.235808 -22.233741 2.573412 104.773544
## 301 302 303 304 305 306
## 10.604239 -56.667853 -82.389622 -71.052416 -27.406170 -91.734403
## 307 308 309 310 311 312
## -75.041844 843.917427 446.896079 341.355344 -3.147501 -29.891365
## 313 314 315 316 317 318
## 11.381011 -36.330768 1013.555157 796.765979 770.022115 751.781645
## 319 320 321 322 323 324
## 538.504479 540.441190 513.697327 422.523023 364.019340 365.956051
## 325 326 327 328 329 330
## 286.343091 288.279802 261.535939 30.819445 32.756156 6.012293
## 331 332 333 334 335 336
## -286.733832 -290.556043 -288.619332 -315.363195 157.486503 159.423214
## 337 338 339 340 341 342
## 132.679350 161.308713 388.892014 390.828725 364.084861 102.942046
## 343 344 345 346 347 348
## 85.697391 187.640682 183.818472 185.755183 104.679400 64.199186
## 349 350 351 352 353 354
## 37.455322 194.722673 190.900463 192.837174 166.093310 105.235259
## 355 356 357 358 359 360
## 101.413048 103.349760 80.694274 186.301037 188.237748 161.493885
## 361 362 363 364 365 366
## 309.654176 305.831965 317.885071 314.062860 315.999571 289.255708
## 367 368 369 370 371 372
## 1697.048625 1698.985336 1672.241472 695.041397 696.978108 670.234244
## 373 374 375 376 377 378
## 961.291745 736.690928 709.947064 425.282273 775.433788 685.003068
## 379 380 381 382 383 384
## 916.161342 918.098054 891.354190 904.360275 719.498134 771.197884
## 385 386 387 388 389 390
## 656.640493 489.879933 389.521755 387.636255 360.892392 280.523928
## 391 392 393 394 395 396
## 278.638429 251.894565 337.735533 267.795313 296.525672 273.870186
## 397 398 399 400 401 402
## 199.062744 34.175794 26.729272 76.838186 73.015976 74.952687
## 403 404 405 406 407 408
## 48.208823 -167.719755 -423.618339 1317.378319 1290.634455 1159.208829
## 409 410 411 412 413 414
## 475.606816 892.829601 889.007391 890.944102 864.200238 526.408693
## 415 416 417 418 419 420
## 522.586482 524.523193 497.779330 561.747154 557.924944 559.861655
## 421 422 423 424 425 426
## 533.117791 246.942022 243.119812 245.056523 218.312659 1253.138045
## 427 428 429 430 431 432
## 1226.394181 126.468321 128.405032 280.147626 282.084337 255.340474
## 433 434 435 436 437 438
## 145.549279 146.326494 148.263206 121.519342 293.765754 267.021890
## 439 440 441 442 443 444
## 364.496708 896.440431 749.065902 720.436539 579.985447 576.163236
## 445 446 447 448 449 450
## 578.099947 551.356084 517.492335 268.733718 4.743808 6.680519
## 451 452 453 454 455 456
## -20.063344 -57.418405 -124.231843 -168.484484 -245.228637 -243.291926
## 457 458
## -261.847730 -499.190510# Compare PriceDifference predicted by the model with the actual profit given in the data
predictedDifference = data.frame(fitted(model))
actualDifference = data.frame(PricePremium-PriceEconomy)
DifferenceComparison = cbind(actualDifference, predictedDifference)
View(DifferenceComparison)Inferences
*The price of Premium seats is higher than price of Economy seats in a flight
*The factors that affect the difference in price of Premium and Economy seats in a flight are FlightDuration, WidthDifference, PitchDifference, Airline, PercentPremiumSeats
*Domestic Flights are cheaper than international flights, but their number is also too low as compared to number of international flights
*AirFrance is the most expensive flight whereas Jet and Delta are the cheaper ones.
*In the month of July, the number of flights goes down and so does the price due to low demand. In the other three months, it is similar.