airline <- read.csv(paste("SixAirlinesDataV2.csv",sep=""))
View(airline)
library(psych)
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.   :24.69
describe(airline)
##                     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  0.23
Hypothesis - There is no relation between seating prices and other factors like width and pitch difference
boxplot(FlightDuration~Aircraft,data=airline,xlab="Aircraft type", ylab="Flight duration",col = c("blue","red"))

airline_agg_air_eco<-aggregate(PriceEconomy~Airline,data=airline,mean)
airline_agg_air_eco
##     Airline PriceEconomy
## 1 AirFrance    2769.7838
## 2   British    1293.4800
## 3     Delta     560.9348
## 4       Jet     276.1639
## 5 Singapore     860.2500
## 6    Virgin    1603.5323
library(lattice)
barchart(PriceEconomy~Airline, data=airline_agg_air_eco,xlab="Airlines",ylab="Economy price in $")

boxplot(FlightDuration~Airline,data=airline,xlab="Airline", ylab="Flight duration",col = c("violet","blue","green","yellow","orange"))

airline_agg_air_pre<-aggregate(PricePremium~Airline,data=airline,mean)
airline_agg_air_pre
##     Airline PricePremium
## 1 AirFrance    3065.2162
## 2   British    1937.0286
## 3     Delta     684.6739
## 4       Jet     483.3607
## 5 Singapore    1239.9250
## 6    Virgin    2721.6935
library(lattice)
barchart(PricePremium~Airline, data=airline_agg_air_pre, xlab="Airlines",ylab="Premium Economy price in $")

plot(airline$FlightDuration,airline$PriceEconomy, xlab = "Flight duration(hrs)", ylab="Economy price n $")

plot(airline$FlightDuration,airline$PricePremium, xlab = "Flight duration(hrs)", ylab="Premium Economy price in $")

airline_agg_trav_eco <-aggregate(PriceEconomy~TravelMonth, data=airline,mean)
airline_agg_trav_eco
##   TravelMonth PriceEconomy
## 1         Aug     1344.661
## 2         Jul     1280.493
## 3         Oct     1295.370
## 4         Sep     1368.062
barchart(PriceEconomy~TravelMonth,data=airline_agg_trav_eco, xlab="Month", ylab="Average economy price", )

airline_agg_trav_pre<-aggregate(PricePremium~TravelMonth, data=airline,mean)
airline_agg_trav_pre
##   TravelMonth PricePremium
## 1         Aug     1871.126
## 2         Jul     1743.427
## 3         Oct     1836.055
## 4         Sep     1888.054
barchart(PricePremium~TravelMonth,data=airline_agg_trav_pre, xlab="Month", ylab="Average premium economy price")

library(car)
scatterplot(PriceRelative ~PitchDifference,     data=airline,
            spread=FALSE, smoother.args=list(lty=2),
            main="Scatter plot of price relative vs pitch difference",
            xlab="pitch difference",
            ylab="price relative")

scatterplot(PriceRelative ~ WidthDifference, data= airline,
            spread=FALSE, smoother.args=list(lty=2),
            main="Scatter plot of price relative vs Width difference",
            xlab="Width difference",
            ylab="Price relative")

library(corrgram)
corrgram(airline, order=TRUE, upper.panel=panel.pie,lower.panel=panel.shade, text.panel=panel.txt,main="Correlogram")

attach(airline)
cor.test(PriceRelative,WidthPremium)
## 
##  Pearson's product-moment correlation
## 
## data:  PriceRelative and 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
cor.test(PriceEconomy,PitchEconomy)
## 
##  Pearson's product-moment correlation
## 
## data:  PriceEconomy and PitchEconomy
## t = 8.469, df = 456, p-value = 3.428e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2867196 0.4452479
## sample estimates:
##       cor 
## 0.3686612
t.test(airline$PriceEconomy, airline$PricePremium)
## 
##  Welch Two Sample t-test
## 
## data:  airline$PriceEconomy and airline$PricePremium
## t = -6.8304, df = 856.56, p-value = 1.605e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -667.0831 -369.2793
## sample estimates:
## mean of x mean of y 
##  1327.076  1845.258
airline_reg_1 <- lm(PriceRelative ~ PitchEconomy + PitchPremium + WidthPremium + PriceEconomy + PitchDifference + WidthDifference + PercentPremiumSeats, data = airline)
summary(airline_reg_1)
## 
## Call:
## lm(formula = PriceRelative ~ PitchEconomy + PitchPremium + WidthPremium + 
##     PriceEconomy + PitchDifference + WidthDifference + PercentPremiumSeats, 
##     data = airline)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.90093 -0.22133 -0.02915  0.15791  1.16165 
## 
## Coefficients: (1 not defined because of singularities)
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -1.102e+00  1.752e+00  -0.629 0.529437    
## PitchEconomy        -6.810e-02  4.511e-02  -1.510 0.131826    
## PitchPremium         3.359e-02  2.192e-02   1.533 0.126056    
## WidthPremium         1.371e-01  3.827e-02   3.583 0.000377 ***
## PriceEconomy        -1.056e-04  2.085e-05  -5.064 5.99e-07 ***
## PitchDifference             NA         NA      NA       NA    
## WidthDifference      7.238e-03  3.769e-02   0.192 0.847790    
## PercentPremiumSeats -6.789e-03  4.267e-03  -1.591 0.112312    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3688 on 451 degrees of freedom
## Multiple R-squared:  0.339,  Adjusted R-squared:  0.3302 
## F-statistic: 38.56 on 6 and 451 DF,  p-value: < 2.2e-16
airline_reg_2<-lm(PriceRelative ~ PitchDifference + WidthPremium + PriceEconomy)
coefficients(airline_reg_2)
##     (Intercept) PitchDifference    WidthPremium    PriceEconomy 
##   -2.5405744540    0.0432138143    0.1484583578   -0.0001144987
Since p<0.05, the hypothesis we assumed is wrong.