setwd("C:/Users/karansy/Downloads/Documents")
Airline.df <- read.csv(paste("SixAirlinesDataV2.csv", sep = ""))
View(Airline.df)
summary(Airline.df)
##       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
str(Airline.df)
## '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 ...

Adding another coloumn as price difference between two classes for a more thorough analysis

Airline.df$PriceDiff <- Airline.df$PricePremium - Airline.df$PriceEconomy

We would like to see the correlation of all the numeric variables with each other.

cor(Airline.df[6:18])
##                     SeatsEconomy SeatsPremium PitchEconomy PitchPremium
## SeatsEconomy         1.000000000  0.625056587   0.14412692  0.119221250
## SeatsPremium         0.625056587  1.000000000  -0.03421296  0.004883123
## PitchEconomy         0.144126924 -0.034212963   1.00000000 -0.550606241
## PitchPremium         0.119221250  0.004883123  -0.55060624  1.000000000
## WidthEconomy         0.373670252  0.455782883   0.29448586 -0.023740873
## WidthPremium         0.102431959 -0.002717527  -0.53929285  0.750259029
## PriceEconomy         0.128167220  0.113642176   0.36866123  0.050384550
## PricePremium         0.177000928  0.217612376   0.22614179  0.088539147
## PriceRelative        0.003956939 -0.097196009  -0.42302204  0.417539056
## SeatsTotal           0.992607966  0.715171053   0.12373524  0.107512784
## PitchDifference      0.035318044  0.016365566  -0.78254993  0.950591466
## WidthDifference     -0.080670148 -0.216168666  -0.63557430  0.703281797
## PercentPremiumSeats -0.330935223  0.485029771  -0.10280880 -0.175487414
##                     WidthEconomy WidthPremium PriceEconomy PricePremium
## SeatsEconomy          0.37367025  0.102431959   0.12816722   0.17700093
## SeatsPremium          0.45578288 -0.002717527   0.11364218   0.21761238
## PitchEconomy          0.29448586 -0.539292852   0.36866123   0.22614179
## PitchPremium         -0.02374087  0.750259029   0.05038455   0.08853915
## WidthEconomy          1.00000000  0.081918728   0.06799061   0.15054837
## WidthPremium          0.08191873  1.000000000  -0.05704522   0.06402004
## PriceEconomy          0.06799061 -0.057045224   1.00000000   0.90138870
## PricePremium          0.15054837  0.064020043   0.90138870   1.00000000
## PriceRelative        -0.04396116  0.504247591  -0.28856711   0.03184654
## SeatsTotal            0.40545860  0.091297500   0.13243313   0.19232533
## PitchDifference      -0.12722421  0.760121272  -0.09952511  -0.01806629
## WidthDifference      -0.39320512  0.884149655  -0.08449975  -0.01151218
## PercentPremiumSeats   0.22714172 -0.183312058   0.06532232   0.11639097
##                     PriceRelative  SeatsTotal PitchDifference
## SeatsEconomy          0.003956939  0.99260797      0.03531804
## SeatsPremium         -0.097196009  0.71517105      0.01636557
## PitchEconomy         -0.423022038  0.12373524     -0.78254993
## PitchPremium          0.417539056  0.10751278      0.95059147
## WidthEconomy         -0.043961160  0.40545860     -0.12722421
## WidthPremium          0.504247591  0.09129750      0.76012127
## PriceEconomy         -0.288567110  0.13243313     -0.09952511
## PricePremium          0.031846537  0.19232533     -0.01806629
## PriceRelative         1.000000000 -0.01156894      0.46873025
## SeatsTotal           -0.011568942  1.00000000      0.03416915
## PitchDifference       0.468730249  0.03416915      1.00000000
## WidthDifference       0.485802437 -0.10584398      0.76089108
## PercentPremiumSeats  -0.161565556 -0.22091465     -0.09264869
##                     WidthDifference PercentPremiumSeats
## 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

Now as we have to analyse the difference between price of premium seats w.r.t. to price of economic seats, we would analyse the pricerelative avriable with other factors. We firstly look at the correlation of Pricerelative varaible with other numeric variables.

cor(Airline.df$PriceDiff, Airline.df[6:19])
##      SeatsEconomy SeatsPremium PitchEconomy PitchPremium WidthEconomy
## [1,]    0.1735396    0.2877081   -0.1250755    0.1100397     0.217031
##      WidthPremium PriceEconomy PricePremium PriceRelative SeatsTotal
## [1,]    0.2377683    0.2959843    0.6804058     0.5586276  0.2001245
##      PitchDifference WidthDifference PercentPremiumSeats PriceDiff
## [1,]       0.1285851       0.1176138           0.1461979         1
cor(Airline.df$PriceRelative, Airline.df[6:19])
##      SeatsEconomy SeatsPremium PitchEconomy PitchPremium WidthEconomy
## [1,]  0.003956939  -0.09719601    -0.423022    0.4175391  -0.04396116
##      WidthPremium PriceEconomy PricePremium PriceRelative  SeatsTotal
## [1,]    0.5042476   -0.2885671   0.03184654             1 -0.01156894
##      PitchDifference WidthDifference PercentPremiumSeats PriceDiff
## [1,]       0.4687302       0.4858024          -0.1615656 0.5586276

Now, we would analyse the concerned variable of pricerelative with all other variables through various plots tests.

1. Analysing the type of airline with the pricerealtive factor.

table(Airline.df$Airline)
## 
## AirFrance   British     Delta       Jet Singapore    Virgin 
##        74       175        46        61        40        62
aggregate(Airline.df$PriceDiff, by = list(Airline.df$Airline), mean)
##     Group.1         x
## 1 AirFrance  295.4324
## 2   British  643.5486
## 3     Delta  123.7391
## 4       Jet  207.1967
## 5 Singapore  379.6750
## 6    Virgin 1118.1613
aggregate(Airline.df$PriceRelative, by = list(Airline.df$Airline), mean)
##     Group.1         x
## 1 AirFrance 0.2047297
## 2   British 0.4375429
## 3     Delta 0.1250000
## 4       Jet 0.9396721
## 5 Singapore 0.5297500
## 6    Virgin 0.7606452

Clearly evident from here that Virgin Airlines has maximum Price Difference between the two classes on avaerage when Compared to other airlines. Jet has maximum Price relative diff.

library(lattice)
histogram(Airline.df$Airline, xlab = "Type of Airline", ylab = " % Count", main = "Types of Airline and % count in the dataset")

boxplot(Airline.df$PriceRelative ~ Airline.df$Airline, xlab = "Type of Airline", ylab = "Relative Price of premium to Economy", main = " Box Plot of Type of Airline to Relative prive")

boxplot(Airline.df$PriceDiff ~ Airline.df$Airline, xlab = "Type of Airline", ylab = "Price Difference", main = " Box Plot of Type of Airline to Fare Difference")

2. Analysing the type of aircraft with the pricerealtive factor.

table(Airline.df$Aircraft)
## 
## AirBus Boeing 
##    151    307
aggregate(Airline.df$PriceDiff, by = list(Airline.df$Aircraft), mean)
##   Group.1        x
## 1  AirBus 499.5497
## 2  Boeing 527.3453
histogram(Airline.df$Aircraft, xlab = "Type of Aircraft", ylab = "% Count", main = "Types of Aircraft and % count in the dataset")

boxplot(Airline.df$PriceRelative ~ Airline.df$Aircraft, xlab = "Type of Aircraft", ylab = "Relative Price of premium to Economy", main = " Box Plot of Type of Aircraft to Relative prive")

boxplot(Airline.df$PriceDiff ~ Airline.df$Aircraft, xlab = "Type of Aircraft", ylab = "Price Difference", main = " Box Plot of Type of Aircraft to Difference fare")

3. Analysing Flight Duration with the pricerelative factor

cor(Airline.df$FlightDuration, Airline.df$PriceRelative)
## [1] 0.121075
cor(Airline.df$FlightDuration, Airline.df$PriceDiff)
## [1] 0.4720837
library(car)
scatterplot(Airline.df$PriceRelative ~ Airline.df$FlightDuration, xlab = "Flight Duration Hrs", ylab = "Relative Price", main = "Flight Duration vs Relative Price")

scatterplot(Airline.df$PriceDiff ~ Airline.df$FlightDuration, xlab = "Flight Duration Hrs", ylab = "Difference in Price", main = "Flight Duration vs Price Difference")

4. Analysing Travel Month with the pricerelative factor

table(Airline.df$TravelMonth)
## 
## Aug Jul Oct Sep 
## 127  75 127 129
aggregate(Airline.df$PriceDiff, by = list(Airline.df$TravelMonth), mean)
##   Group.1        x
## 1     Aug 526.4646
## 2     Jul 462.9333
## 3     Oct 540.6850
## 4     Sep 519.9922

A key information from here is that with no. of flights the least in July, all airline companies kept the prices low stating lower no. of passengers. When demand of flight tickets rose in months of Aug, Sep and Oct the airline Companies increased the no. of flight as well as increased the price Difference.

histogram(Airline.df$TravelMonth, xlab = "Month", ylab = "% Count", main = "Month and % count in the dataset")

boxplot(Airline.df$PriceRelative ~ Airline.df$TravelMonth, xlab = "Month", ylab = "Relative Price of premium to Economy", main = " Box Plot of Travel Month to Relative prive")

boxplot(Airline.df$PriceDiff ~ Airline.df$TravelMonth, xlab = "Month", ylab = "Price Difference", main = " Box Plot of Month to Difference fare")

5. Analysing International/Domestic Flights with Pricerelative factor

table(Airline.df$IsInternational)
## 
##      Domestic International 
##            40           418
aggregate(Airline.df$PriceDiff, by = list(Airline.df$IsInternational), mean)
##         Group.1        x
## 1      Domestic  29.2750
## 2 International 564.9665
aggregate(Airline.df$PriceRelative, by = list(Airline.df$IsInternational), mean)
##         Group.1         x
## 1      Domestic 0.0847500
## 2 International 0.5257177

Clearly, International Flights have very high difference due to more fare of basic economic fare and the no. of flight hours increasing a lot too. Also the Relative Price is too very significant.

histogram(Airline.df$IsInternational, xlab = "Destination type", ylab = "% Count", main = "Destination type and % count in the dataset")

boxplot(Airline.df$PriceRelative ~ Airline.df$IsInternational, xlab = "Destination Type", ylab = "Relative Price of premium to Economy", main = " Box Plot of Destination type to Relative prive")

boxplot(Airline.df$PriceDiff ~ Airline.df$IsInternational, xlab = "Destination Type", ylab = "Price Difference", main = " Box Plot of Destination type to Difference fare")

For variables of coloumns 6-19, we would plot it the relations using Scatter plot matrix method, by showcasing multiple scatter plots together.

Scatter Plot Matrix of Relative Price with various variables

scatterplotMatrix(formula = ~ Airline.df$SeatsEconomy + Airline.df$SeatsPremium + Airline.df$PriceRelative, main = "Scatter plot matrix of Seats in Economy and Premium and Relative Price")

scatterplotMatrix(formula = ~ Airline.df$PitchEconomy + Airline.df$PitchPremium + Airline.df$PriceRelative, main = "Scatter plot matrix of Pitch Economy, Pitch Premium and Relative Price")

scatterplotMatrix(formula = ~ Airline.df$WidthEconomy + Airline.df$WidthPremium + Airline.df$PriceRelative, main = "Scatter plot matrix Width Economy, Width Premium and Relative Price")

scatterplotMatrix(formula = ~ Airline.df$PitchDifference + Airline.df$WidthDifference + Airline.df$PriceRelative, main = "Scatter plot matrix of Pitch diff, Width diff and Relative Price")

scatterplotMatrix(formula = ~ Airline.df$SeatsTotal + Airline.df$PercentPremiumSeats + Airline.df$PriceRelative, main = "Scatter plot matrix of Total Seats, % Premium seats and Relative Price")

Now looking at Scatter plot matrix of Price Difference with the same variables

scatterplotMatrix(formula = ~ Airline.df$SeatsEconomy + Airline.df$SeatsPremium + Airline.df$PriceDiff, main = "Scatter plot matrix of Seats in Economy and Premium and Price Difference")

scatterplotMatrix(formula = ~ Airline.df$PitchEconomy + Airline.df$PitchPremium + Airline.df$PriceDiff, main = "Scatter plot matrix of Pitch Economy, Pitch Premium and Price Difference")

scatterplotMatrix(formula = ~ Airline.df$WidthEconomy + Airline.df$WidthPremium + Airline.df$PriceDiff, main = "Scatter plot matrix Width Economy, Width Premium and Price Difference")

scatterplotMatrix(formula = ~ Airline.df$PitchDifference + Airline.df$WidthDifference + Airline.df$PriceDiff, main = "Scatter plot matrix of Pitch diff, Width diff and Price Difference")

scatterplotMatrix(formula = ~ Airline.df$SeatsTotal + Airline.df$PercentPremiumSeats + Airline.df$PriceDiff, main = "Scatter plot matrix of Total Seats, % Premium seats and Price Difference")

Looking at the Corrgram plotof the dataset.

library(corrgram)
corrgram(Airline.df, order=TRUE, lower.panel=panel.shade,
        upper.panel=panel.pie, text.panel=panel.txt,
        main="Corrgram of correlations between store variables")

Now We would like to see the price difference with relation to pitch, width and seat parameters.

aggregate(Airline.df$PriceDiff, by = list(Airline.df$PitchDifference), mean)
##   Group.1         x
## 1       2  29.33333
## 2       3  29.18750
## 3       6 325.04959
## 4       7 767.35802
## 5      10 191.79630

Clearly, the pitch difference between the two classes factors a lot to the price difference

aggregate(Airline.df$PriceDiff, by = list(Airline.df$WidthDifference), mean)
##   Group.1         x
## 1       0   29.2750
## 2       1  537.6742
## 3       2  312.6875
## 4       3 1085.9853
## 5       4  191.7963

Clearly, the waidth difference between the two classes too like the pitch difference factors a lot to the price difference

aggregate(Airline.df$PriceRelative, by = list(Airline.df$PitchDifference), mean)
##   Group.1          x
## 1       2 0.08708333
## 2       3 0.08125000
## 3       6 0.34082645
## 4       7 0.51888889
## 5      10 0.97074074

As can be seen from here, the price relative gives a more clear picture. As pitch Difference increases, so does the Relative price

aggregate(Airline.df$PriceRelative, by = list(Airline.df$WidthDifference), mean)
##   Group.1         x
## 1       0 0.0847500
## 2       1 0.4184091
## 3       2 0.2296875
## 4       3 0.7282353
## 5       4 0.9707407

Here too like the price Relative vs Pitch Diff case as the Width Difference increases the Relative price increases.

Tests and Linear Regression model of key variables

cor.test(Airline.df$PriceRelative, Airline.df$FlightDuration)
## 
##  Pearson's product-moment correlation
## 
## data:  Airline.df$PriceRelative and Airline.df$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 
## 0.121075

Null Hypotheses cannot be ignored in this case.

t.test(Airline.df$PriceRelative ~ Airline.df$IsInternational)
## 
##  Welch Two Sample t-test
## 
## data:  Airline.df$PriceRelative by Airline.df$IsInternational
## t = -19.451, df = 446.12, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.4855215 -0.3964139
## sample estimates:
##      mean in group Domestic mean in group International 
##                   0.0847500                   0.5257177

A significantly low p-value signifying that null hypotheses can be neglected

cor.test(Airline.df$PriceRelative , Airline.df$PitchPremium)
## 
##  Pearson's product-moment correlation
## 
## data:  Airline.df$PriceRelative and Airline.df$PitchPremium
## t = 9.8125, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3388769 0.4904041
## sample estimates:
##       cor 
## 0.4175391

A significantly low p-value signifying that null hypotheses can be neglected

cor.test(Airline.df$PriceRelative , Airline.df$WidthPremium)
## 
##  Pearson's product-moment correlation
## 
## data:  Airline.df$PriceRelative and Airline.df$WidthPremium
## t = 12.469, df = 456, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4326084 0.5695593
## sample estimates:
##       cor 
## 0.5042476

A significantly low p-value signifying that null hypotheses can be neglected

cor.test(Airline.df$PriceRelative , Airline.df$PitchDifference)
## 
##  Pearson's product-moment correlation
## 
## data:  Airline.df$PriceRelative and Airline.df$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 
## 0.4687302

A significantly low p-value signifying that null hypotheses can be neglected

cor.test(Airline.df$PriceRelative , Airline.df$WidthDifference)
## 
##  Pearson's product-moment correlation
## 
## data:  Airline.df$PriceRelative and Airline.df$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 
## 0.4858024

A significantly low p-value signifying that null hypotheses can be neglected

cor.test(Airline.df$PriceRelative , Airline.df$PercentPremiumSeats)
## 
##  Pearson's product-moment correlation
## 
## data:  Airline.df$PriceRelative and Airline.df$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 
## -0.1615656

A lower p-value then 0.005 signifying that null hypotheses can be neglected

lm model

fitt <- lm(formula = Airline.df$PriceRelative~ ., data = Airline.df)
summary(fitt)
## 
## Call:
## lm(formula = Airline.df$PriceRelative ~ ., data = Airline.df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.76373 -0.08269  0.00438  0.08002  0.84672 
## 
## Coefficients: (4 not defined because of singularities)
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -3.993e-01  2.948e+00  -0.135 0.892302    
## AirlineBritish               -3.971e-01  1.107e-01  -3.586 0.000373 ***
## AirlineDelta                 -3.865e-01  2.203e-01  -1.755 0.080020 .  
## AirlineJet                   -2.584e-01  9.594e-02  -2.693 0.007354 ** 
## AirlineSingapore             -3.535e-01  1.297e-01  -2.725 0.006685 ** 
## AirlineVirgin                -3.575e-01  2.031e-01  -1.761 0.078997 .  
## AircraftBoeing                4.003e-02  2.968e-02   1.349 0.178089    
## FlightDuration                2.613e-02  4.727e-03   5.526 5.63e-08 ***
## TravelMonthJul                2.111e-02  3.145e-02   0.671 0.502475    
## TravelMonthOct                2.778e-02  2.670e-02   1.041 0.298619    
## TravelMonthSep               -6.617e-03  2.664e-02  -0.248 0.803924    
## IsInternationalInternational  2.785e-02  2.502e-01   0.111 0.911400    
## SeatsEconomy                  8.090e-04  5.462e-04   1.481 0.139313    
## SeatsPremium                 -7.374e-03  3.615e-03  -2.040 0.041967 *  
## PitchEconomy                 -1.756e-02  7.994e-02  -0.220 0.826207    
## PitchPremium                  5.960e-02  9.165e-02   0.650 0.515823    
## WidthEconomy                 -9.207e-02  5.266e-02  -1.748 0.081085 .  
## WidthPremium                  4.904e-02  1.365e-01   0.359 0.719527    
## PriceEconomy                 -9.325e-04  3.318e-05 -28.105  < 2e-16 ***
## PricePremium                  5.781e-04  2.294e-05  25.197  < 2e-16 ***
## SeatsTotal                           NA         NA      NA       NA    
## PitchDifference                      NA         NA      NA       NA    
## WidthDifference                      NA         NA      NA       NA    
## PercentPremiumSeats           1.114e-02  7.653e-03   1.456 0.146197    
## PriceDiff                            NA         NA      NA       NA    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2123 on 437 degrees of freedom
## Multiple R-squared:  0.7878, Adjusted R-squared:  0.7781 
## F-statistic: 81.12 on 20 and 437 DF,  p-value: < 2.2e-16

#Executive Summary

  1. Virgin Airlines has the maximum difference in actual Price whereas Jet has maximum Relative Price between the two classes
  2. July consisting of least no. of flights has least price difference then other 3 months of August, September and October
  3. International Flights have a significant difference when it comes to the Actual Price difference as well as the Relative Price.
  4. The Relative price of Premium Economy class to that of Economy class has positive and strong correlation with the Pitch and Width dimensions in Permium class and has very low p-values thereby indicating that on increasing pitch or width in premium class, the Relative price also increases.
  5. Similarly, the Relative price of Premium Economy class to that of Economy class has positive and strong correlation with the Pitch difference and Width difference between Permium class and Economy class and has very low p-values thereby indicating that on increasing pitch difference or width difference between the two classes, the Relative price also increases.