title: “Airlines Travel Class review Case” author: “Puneet Rajput” output: html_document

airlines.df <- read.csv(paste("SixAirlinesDataV2.csv", sep=""))
View(airlines.df)

summary(airlines.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

library(car)
scatterplot(PricePremium~PriceEconomy, data=airlines.df, spread=FALSE,  smoother.args=list(lty=2), pch=19,xlab="Economy Price", ylab="Premium Price", main= "Premium Vs. Economy Price")

Most of the people prefer travelling through economy class due to it’s low price

Analysing the role of pitch difference.

PD <- table(airlines.df$PitchDifference)
PD

## 
##   2   3   6   7  10 
##  24  16 121 243  54

mean(airlines.df$PitchDifference)

## [1] 6.687773

CD <- aggregate(PriceRelative ~ PitchDifference, data= airlines.df,mean)
CD

##   PitchDifference PriceRelative
## 1               2    0.08708333
## 2               3    0.08125000
## 3               6    0.34082645
## 4               7    0.51888889
## 5              10    0.97074074

library(lattice)
hist(airlines.df$PitchDifference, xlab= "pitch difference", main="Pich Difference")

boxplot(PriceRelative~PitchDifference,data=airlines.df, main="Relative Price Difference vs. Pitch", ylab="Pitch Difference", xlab="Relative Price b/w Economy and Premium Economy", horizontal=TRUE)

scatterplot(PriceRelative ~ PitchDifference, data=airlines.df, spread=FALSE, smoother.args=list(lty=2), pch=19, xlab="Pitch", main="Relative PriceDifference Vs. Pitch")

Average pitch difference = 6.687, most frequently occuring pitch Difference = 7 with an average PriceRelative = 0.518

BoxPlot and scatterplot reveals that the increased pitch difference increases the price of the premium class

Analysing the role of WidthDifference.

PD <- table(airlines.df$WidthDifference)
PD

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

mean(airlines.df$WidthDifference)

## [1] 1.633188

CD <- aggregate(PriceRelative ~ WidthDifference, data= airlines.df,mean)
CD

##   WidthDifference PriceRelative
## 1               0     0.0847500
## 2               1     0.4184091
## 3               2     0.2296875
## 4               3     0.7282353
## 5               4     0.9707407

library(lattice)
hist(airlines.df$WidthDifference, xlab= "width difference", main="width Difference")

boxplot(PriceRelative~WidthDifference,data=airlines.df, main="Relative Price Difference vs. Width", ylab="Width Difference", xlab="Relative Price b/w Economy and Premium Economy", horizontal=TRUE)

scatterplot(PriceRelative ~ WidthDifference, data=airlines.df, spread=FALSE, smoother.args=list(lty=2), pch=19,xlab="Width", main="Relative Price Difference Vs. Width")

Average Width Difference = 1.633, most frequently occuring width Difference = 1 with an average PriceRelative = 0.418

BoxPlot and scatterplot reveals that the increased WidthDifference increases the price of Premium Class

Correlation Visualisation

attach(airlines.df)
scatterplotMatrix(~PriceRelative+PricePremium+PriceEconomy+PercentPremiumSeats, main="Premium Vs. Economy Travel Class Analysis")

scatterplotMatrix(~PriceEconomy+PricePremium+WidthDifference+PitchDifference, main="Premium Vs. Economy Travel Class Analysis")

library(corrplot)

## corrplot 0.84 loaded

corrplot(corr = cor(airlines.df[,c("PriceRelative","PricePremium","PriceEconomy","PitchDifference","WidthDifference","PercentPremiumSeats")], use="complete.obs"),method="ellipse")

library(corrgram)
corrgram(airlines.df[,c("PriceRelative","PricePremium","PriceEconomy","PitchDifference","WidthDifference","PercentPremiumSeats")], lower.panel = panel.shade, text.panel = panel.txt, upper.panel = panel.pie,diag.panel = panel.minmax)

Correlation numerics

options(digits=2)
cor(airlines.df[,c("PriceRelative","PricePremium","PriceEconomy","PitchDifference","WidthDifference","PercentPremiumSeats")], use="complete.obs", method="kendall")

##                     PriceRelative PricePremium PriceEconomy
## PriceRelative              1.0000      -0.0078      -0.2042
## PricePremium              -0.0078       1.0000       0.8060
## PriceEconomy              -0.2042       0.8060       1.0000
## PitchDifference            0.4224      -0.0887      -0.1863
## WidthDifference            0.3370       0.1034       0.0236
## PercentPremiumSeats        0.0050       0.0084      -0.0028
##                     PitchDifference WidthDifference PercentPremiumSeats
## PriceRelative                 0.422           0.337              0.0050
## PricePremium                 -0.089           0.103              0.0084
## PriceEconomy                 -0.186           0.024             -0.0028
## PitchDifference               1.000           0.595              0.1613
## WidthDifference               0.595           1.000             -0.1698
## PercentPremiumSeats           0.161          -0.170              1.0000

attach(airlines.df)

## The following objects are masked from airlines.df (pos = 5):
## 
##     Aircraft, Airline, FlightDuration, IsInternational,
##     PercentPremiumSeats, PitchDifference, PitchEconomy,
##     PitchPremium, PriceEconomy, PricePremium, PriceRelative,
##     SeatsEconomy, SeatsPremium, SeatsTotal, TravelMonth,
##     WidthDifference, WidthEconomy, WidthPremium

t.test(PricePremium,PriceEconomy)

## 
##  Welch Two Sample t-test
## 
## data:  PricePremium and PriceEconomy
## t = 7, df = 900, p-value = 2e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  369 667
## sample estimates:
## mean of x mean of y 
##      1845      1327

t.test(PitchPremium,PitchEconomy)

## 
##  Welch Two Sample t-test
## 
## data:  PitchPremium and PitchEconomy
## t = 100, df = 700, p-value <2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  6.6 6.8
## sample estimates:
## mean of x mean of y 
##        38        31

t.test(WidthPremium,PitchPremium)

## 
##  Welch Two Sample t-test
## 
## data:  WidthPremium and PitchPremium
## t = -200, df = 900, p-value <2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -19 -18
## sample estimates:
## mean of x mean of y 
##        19        38

Prices and Pitch/widthdifference are negatively and positively correlated with the PriceRelative

Pitch and width differences are positively correlated with each other

Premuim and Economy Price are strongly correlated positively

Pitches, Widths and Prices of Premium and Economy class are statistically significant with each other

Main reason of higher premium price is because of higher asscociation of pitch and width difference with it as compared to economy class

model <- PricePremium~PriceEconomy+PitchDifference+WidthDifference
fit <- lm(model, data=airlines.df)
fit

## 
## Call:
## lm(formula = model, data = airlines.df)
## 
## Coefficients:
##     (Intercept)     PriceEconomy  PitchDifference  WidthDifference  
##          -33.38             1.18            39.44            26.26

summary(fit)

## 
## Call:
## lm(formula = model, data = airlines.df)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##   -810   -326    -97    176   3471 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -33.3762   125.6968   -0.27    0.791    
## PriceEconomy      1.1846     0.0262   45.15   <2e-16 ***
## PitchDifference  39.4389    22.5994    1.75    0.082 .  
## WidthDifference  26.2554    33.4303    0.79    0.433    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 551 on 454 degrees of freedom
## Multiple R-squared:  0.818,  Adjusted R-squared:  0.817 
## F-statistic:  680 on 3 and 454 DF,  p-value: <2e-16

plot(fit)

Most of the people prefer travelling through economy class due to it’s low price

Analysing the role of pitch difference.

Average pitch difference = 6.687, most frequently occuring pitch Difference = 7 with an average PriceRelative = 0.518

BoxPlot and scatterplot reveals that the increased pitch difference increases the price of the premium class

Analysing the role of WidthDifference.

Average Width Difference = 1.633, most frequently occuring width Difference = 1 with an average PriceRelative = 0.418

BoxPlot and scatterplot reveals that the increased WidthDifference increases the price of Premium Class

Correlation Visualisation

Correlation numerics

Prices and Pitch/widthdifference are negatively and positively correlated with the PriceRelative

Pitch and width differences are positively correlated with each other

Premuim and Economy Price are strongly correlated positively

Pitches, Widths and Prices of Premium and Economy class are statistically significant with each other

Main reason of higher premium price is because of higher asscociation of pitch and width difference with it as compared to economy class

Prices are proved statistically correlated with each other

Pitch and Width differences aren’t statistically associated with each other