FlightData
Punith Kumar
12/22/2017
AirlinesData <- read.csv(paste("SixAirlinesDataV2.csv",sep = ","))
View(AirlinesData)library(psych)
describe(AirlinesData)## vars n mean sd median trimmed mad min
## Airline* 1 458 3.01 1.65 2.00 2.89 1.48 1.00
## Aircraft* 2 458 1.67 0.47 2.00 1.71 0.00 1.00
## FlightDuration 3 458 7.58 3.54 7.79 7.57 4.81 1.25
## TravelMonth* 4 458 2.56 1.17 3.00 2.58 1.48 1.00
## IsInternational* 5 458 1.91 0.28 2.00 2.00 0.00 1.00
## SeatsEconomy 6 458 202.31 76.37 185.00 194.64 85.99 78.00
## SeatsPremium 7 458 33.65 13.26 36.00 33.35 11.86 8.00
## PitchEconomy 8 458 31.22 0.66 31.00 31.26 0.00 30.00
## PitchPremium 9 458 37.91 1.31 38.00 38.05 0.00 34.00
## WidthEconomy 10 458 17.84 0.56 18.00 17.81 0.00 17.00
## WidthPremium 11 458 19.47 1.10 19.00 19.53 0.00 17.00
## PriceEconomy 12 458 1327.08 988.27 1242.00 1244.40 1159.39 65.00
## PricePremium 13 458 1845.26 1288.14 1737.00 1799.05 1845.84 86.00
## PriceRelative 14 458 0.49 0.45 0.36 0.42 0.41 0.02
## SeatsTotal 15 458 235.96 85.29 227.00 228.73 90.44 98.00
## PitchDifference 16 458 6.69 1.76 7.00 6.76 0.00 2.00
## WidthDifference 17 458 1.63 1.19 1.00 1.53 0.00 0.00
## PercentPremiumSeats 18 458 14.65 4.84 13.21 14.31 2.68 4.71
## max range skew kurtosis se
## Airline* 6.00 5.00 0.61 -0.95 0.08
## Aircraft* 2.00 1.00 -0.72 -1.48 0.02
## FlightDuration 14.66 13.41 -0.07 -1.12 0.17
## TravelMonth* 4.00 3.00 -0.14 -1.46 0.05
## IsInternational* 2.00 1.00 -2.91 6.50 0.01
## SeatsEconomy 389.00 311.00 0.72 -0.36 3.57
## SeatsPremium 66.00 58.00 0.23 -0.46 0.62
## PitchEconomy 33.00 3.00 -0.03 -0.35 0.03
## PitchPremium 40.00 6.00 -1.51 3.52 0.06
## WidthEconomy 19.00 2.00 -0.04 -0.08 0.03
## WidthPremium 21.00 4.00 -0.08 -0.31 0.05
## PriceEconomy 3593.00 3528.00 0.51 -0.88 46.18
## PricePremium 7414.00 7328.00 0.50 0.43 60.19
## PriceRelative 1.89 1.87 1.17 0.72 0.02
## SeatsTotal 441.00 343.00 0.70 -0.53 3.99
## PitchDifference 10.00 8.00 -0.54 1.78 0.08
## WidthDifference 4.00 4.00 0.84 -0.53 0.06
## PercentPremiumSeats 24.69 19.98 0.71 0.28 0.23attach(AirlinesData)
summary(AirlinesData)## 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. :24.69str(AirlinesData)## '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 ...par(mfrow =c(1,2))
boxplot(AirlinesData$SeatsPremium, main=" Distribution SeatsPremium")
barplot(AirlinesData$SeatsPremium, main="Disrtribution SeatsPremium",xlab = " count",
ylab = "SeatsPremium")
par(mfrow =c(1,2))
boxplot(AirlinesData$SeatsEconomy, main=" Distribution SeatsEconomy")
barplot(AirlinesData$SeatsEconomy, main="Disrtribution SeatsEconomy",xlab = " count",
ylab = "SeatsEconomy")
par(mfrow =c(1,2))
boxplot(AirlinesData$PitchEconomy, main=" Distribution PitchEconomy")
barplot(AirlinesData$PitchEconomy, main="Disrtribution PitchEconomy",xlab = " count",
ylab = "PitchEconomy")
par(mfrow =c(1,2))
boxplot(AirlinesData$PitchPremium, main=" Distribution PitchPremium")
barplot(AirlinesData$PitchPremium, main="Disrtribution PitchPremium",xlab = " count",
ylab = "PitchPremium")
par(mfrow =c(1,2))
boxplot(AirlinesData$WidthPremium, main=" Distribution WidthPremium")
barplot(AirlinesData$WidthPremium, main="Disrtribution WidthPremium",xlab = " count",
ylab = "WidthPremium")
par(mfrow =c(1,2))
boxplot(AirlinesData$WidthEconomy, main=" Distribution WidthEconomy")
barplot(AirlinesData$WidthEconomy, main="Disrtribution WidthEconomy",xlab = " count",
ylab = "WidthEconomy")
par(mfrow =c(1,2))
boxplot(AirlinesData$PercentPremiumSeats, main=" Distribution PercentPremiumSeats")
barplot(AirlinesData$PercentPremiumSeats, main="Disrtribution PercentPremiumSeats",xlab = " count",ylab = "PercentPermiumSeats")
par(mfrow =c(1,2))
boxplot(AirlinesData$PriceEconomy, main=" Distribution PriceEconomy")
barplot(AirlinesData$PriceEconomy, main="Disrtribution PriceEconomy",xlab = " count",ylab = "PriceEconomy")
par(mfrow =c(1,2))
boxplot(AirlinesData$PricePremium, main=" Distribution PricePremium")
barplot(AirlinesData$PricePremium, main="Disrtribution PricePremium",xlab = " count",ylab = "PricePremium")
par(mfrow =c(1,2))
boxplot(AirlinesData$PriceRelative, main=" Distribution PriceRelative")
barplot(AirlinesData$PriceRelative, main="Disrtribution PriceRelative",xlab = " count",ylab = "PricePremium")
par(mfrow =c(1,2))
boxplot(AirlinesData$PitchDifference, main=" Distribution PitchDifference")
barplot(AirlinesData$PitchDifference, main="Disrtribution PitchDifference",xlab = " count",ylab = "PitchDifference")
par(mfrow =c(1,2))
boxplot(AirlinesData$WidthDifference, main=" Distribution WidthDifference")
barplot(AirlinesData$WidthDifference, main="Disrtribution WidthDifference",xlab = " count",ylab = "WidthDifference")
library(car)## Warning: package 'car' was built under R version 3.4.3##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(AirlinesData$PriceRelative ~ AirlinesData$PitchDifference, data=AirlinesData,
spread=FALSE, smoother.args=list(lty=2),
main="Scatter plot of price relative vs pitch difference",
xlab="pitch difference",
ylab="price relative")
scatterplot(AirlinesData$PriceRelative ~ AirlinesData$WidthDifference, data= AirlinesData,
spread=FALSE, smoother.args=list(lty=2), pch=19,
main="Scatter plot of price relative vs Width difference",
xlab="Width difference",
ylab="Price relative")
corrgram
library(corrgram)
corrgram(AirlinesData,order = TRUE, lower.panel = panel.shade,upper.panel = panel.pie, text.panel = panel.txt,main = "corrgram Airlines Data ")
#correlation matrix
round(cor(AirlinesData[,6:18]),2)## SeatsEconomy SeatsPremium PitchEconomy PitchPremium
## SeatsEconomy 1.00 0.63 0.14 0.12
## SeatsPremium 0.63 1.00 -0.03 0.00
## PitchEconomy 0.14 -0.03 1.00 -0.55
## PitchPremium 0.12 0.00 -0.55 1.00
## WidthEconomy 0.37 0.46 0.29 -0.02
## WidthPremium 0.10 0.00 -0.54 0.75
## PriceEconomy 0.13 0.11 0.37 0.05
## PricePremium 0.18 0.22 0.23 0.09
## PriceRelative 0.00 -0.10 -0.42 0.42
## SeatsTotal 0.99 0.72 0.12 0.11
## PitchDifference 0.04 0.02 -0.78 0.95
## WidthDifference -0.08 -0.22 -0.64 0.70
## PercentPremiumSeats -0.33 0.49 -0.10 -0.18
## WidthEconomy WidthPremium PriceEconomy PricePremium
## SeatsEconomy 0.37 0.10 0.13 0.18
## SeatsPremium 0.46 0.00 0.11 0.22
## PitchEconomy 0.29 -0.54 0.37 0.23
## PitchPremium -0.02 0.75 0.05 0.09
## WidthEconomy 1.00 0.08 0.07 0.15
## WidthPremium 0.08 1.00 -0.06 0.06
## PriceEconomy 0.07 -0.06 1.00 0.90
## PricePremium 0.15 0.06 0.90 1.00
## PriceRelative -0.04 0.50 -0.29 0.03
## SeatsTotal 0.41 0.09 0.13 0.19
## PitchDifference -0.13 0.76 -0.10 -0.02
## WidthDifference -0.39 0.88 -0.08 -0.01
## PercentPremiumSeats 0.23 -0.18 0.07 0.12
## PriceRelative SeatsTotal PitchDifference
## SeatsEconomy 0.00 0.99 0.04
## SeatsPremium -0.10 0.72 0.02
## PitchEconomy -0.42 0.12 -0.78
## PitchPremium 0.42 0.11 0.95
## WidthEconomy -0.04 0.41 -0.13
## WidthPremium 0.50 0.09 0.76
## PriceEconomy -0.29 0.13 -0.10
## PricePremium 0.03 0.19 -0.02
## PriceRelative 1.00 -0.01 0.47
## SeatsTotal -0.01 1.00 0.03
## PitchDifference 0.47 0.03 1.00
## WidthDifference 0.49 -0.11 0.76
## PercentPremiumSeats -0.16 -0.22 -0.09
## WidthDifference PercentPremiumSeats
## SeatsEconomy -0.08 -0.33
## SeatsPremium -0.22 0.49
## PitchEconomy -0.64 -0.10
## PitchPremium 0.70 -0.18
## WidthEconomy -0.39 0.23
## WidthPremium 0.88 -0.18
## PriceEconomy -0.08 0.07
## PricePremium -0.01 0.12
## PriceRelative 0.49 -0.16
## SeatsTotal -0.11 -0.22
## PitchDifference 0.76 -0.09
## WidthDifference 1.00 -0.28
## PercentPremiumSeats -0.28 1.00t.test(AirlinesData$PriceRelative,AirlinesData$PitchDifference)##
## Welch Two Sample t-test
##
## data: AirlinesData$PriceRelative and AirlinesData$PitchDifference
## t = -72.974, df = 516.54, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -6.367495 -6.033640
## sample estimates:
## mean of x mean of y
## 0.4872052 6.6877729since p is less than 0.05 we reject null hypothesis
t.test(AirlinesData$PriceRelative,AirlinesData$WidthDifference)##
## Welch Two Sample t-test
##
## data: AirlinesData$PriceRelative and AirlinesData$WidthDifference
## t = -19.284, df = 585.55, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.262697 -1.029268
## sample estimates:
## mean of x mean of y
## 0.4872052 1.6331878fit <- lm(PriceRelative~WidthDifference+PitchDifference+PriceEconomy)
summary(fit)##
## Call:
## lm(formula = PriceRelative ~ WidthDifference + PitchDifference +
## PriceEconomy)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.91001 -0.23829 -0.07413 0.16282 1.15356
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.878e-02 8.515e-02 0.925 0.355357
## WidthDifference 1.143e-01 2.265e-02 5.047 6.50e-07 ***
## PitchDifference 5.502e-02 1.531e-02 3.594 0.000362 ***
## PriceEconomy -1.102e-04 1.777e-05 -6.200 1.27e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3736 on 454 degrees of freedom
## Multiple R-squared: 0.3171, Adjusted R-squared: 0.3126
## F-statistic: 70.28 on 3 and 454 DF, p-value: < 2.2e-16Inference 1)Linear regression model has to increase for width and pitch difference 2)p value is than 0.05 hence we can reject null hypothesis 3)spacing has effected to cause the price of seats to be more inpremium class than economy class