Air_Ticket
Slide With Code
x=read.csv("AirlinePricingData.csv")
str(x)
'data.frame': 305 obs. of 25 variables:
$ FlightNumber : Factor w/ 63 levels "6E 129","6E 155",..: 25 32 62 4 61 45 57 16 59 17 ...
$ Airline : Factor w/ 4 levels "Air India","IndiGo",..: 3 3 4 2 4 3 4 2 4 3 ...
$ DepartureCityCode : Factor w/ 2 levels "BOM","DEL": 2 1 2 2 1 1 2 2 1 1 ...
$ ArrivalCityCode : Factor w/ 2 levels "BOM","DEL": 1 2 1 1 2 2 1 1 2 2 ...
$ DepartureTime : int 225 300 350 455 555 605 635 640 645 700 ...
$ ArrivalTime : int 435 505 605 710 805 815 850 855 855 915 ...
$ Departure : Factor w/ 2 levels "AM","PM": 1 1 1 1 1 1 1 1 1 1 ...
$ FlyingMinutes : int 130 125 135 135 130 130 135 135 130 135 ...
$ Aircraft : Factor w/ 2 levels "Airbus","Boeing": 2 2 2 1 2 2 2 1 2 2 ...
$ PlaneModel : Factor w/ 9 levels "738","739","77W",..: 1 1 1 6 1 1 1 6 1 2 ...
$ Capacity : int 156 156 189 180 189 156 189 180 189 138 ...
$ SeatPitch : int 30 30 29 30 29 30 29 30 29 30 ...
$ SeatWidth : num 17 17 17 18 17 17 17 18 17 17 ...
$ DataCollectionDate : Factor w/ 7 levels "Sep 10 2018",..: 2 4 6 7 6 4 6 7 6 4 ...
$ DateDeparture : Factor w/ 20 levels "Nov 6 2018","Nov 8 2018",..: 1 1 1 1 1 1 1 1 1 1 ...
$ IsWeekend : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ Price : int 4051 11587 3977 4234 6837 6518 3189 4234 8623 6833 ...
$ AdvancedBookingDays: int 54 52 48 59 48 52 48 59 48 52 ...
$ IsDiwali : int 1 1 1 1 1 1 1 1 1 1 ...
$ DayBeforeDiwali : int 1 1 1 1 1 1 1 1 1 1 ...
$ DayAfterDiwali : int 0 0 0 0 0 0 0 0 0 0 ...
$ MetroDeparture : int 1 1 1 1 1 1 1 1 1 1 ...
$ MetroArrival : int 1 1 1 1 1 1 1 1 1 1 ...
$ MarketShare : num 15.4 15.4 13.2 39.6 13.2 15.4 13.2 39.6 13.2 15.4 ...
$ LoadFactor : num 83.3 83.3 94.1 87.2 94.1 ...
summary(x)
FlightNumber Airline DepartureCityCode ArrivalCityCode
6E 129 : 5 Air India: 41 BOM:130 BOM:175
6E 155 : 5 IndiGo : 80 DEL:175 DEL:130
6E 167 : 5 Jet :144
6E 171 : 5 Spice Jet: 40
6E 179 : 5
6E 181 : 5
(Other):275
DepartureTime ArrivalTime Departure FlyingMinutes Aircraft
Min. : 225 Min. : 20 AM:169 Min. :125 Airbus:140
1st Qu.: 755 1st Qu.: 935 PM:136 1st Qu.:135 Boeing:165
Median :1035 Median :1215 Median :135
Mean :1250 Mean :1329 Mean :136
3rd Qu.:1800 3rd Qu.:1925 3rd Qu.:140
Max. :2320 Max. :2345 Max. :145
PlaneModel Capacity SeatPitch SeatWidth
738 :113 Min. :138.0 Min. :29.00 Min. :17.00
A320 : 80 1st Qu.:156.0 1st Qu.:30.00 1st Qu.:17.00
739 : 36 Median :180.0 Median :30.00 Median :17.00
A321 : 25 Mean :176.4 Mean :30.26 Mean :17.41
A332 : 25 3rd Qu.:189.0 3rd Qu.:30.00 3rd Qu.:18.00
77W : 10 Max. :303.0 Max. :33.00 Max. :18.00
(Other): 16
DataCollectionDate DateDeparture IsWeekend Price
Sep 10 2018:40 Nov 8 2018 : 62 No :264 Min. : 2607
Sep 13 2018:30 Nov 6 2018 : 59 Yes: 41 1st Qu.: 4051
Sep 14 2018:30 Sep 21 2018: 23 Median : 4681
Sep 15 2018:45 Sep 17 2018: 17 Mean : 5395
Sep 17 2018:39 Oct 19 2018: 16 3rd Qu.: 5725
Sep 19 2018:81 Sep 26 2018: 16 Max. :18015
Sep 8 2018 :40 (Other) :112
AdvancedBookingDays IsDiwali DayBeforeDiwali DayAfterDiwali
Min. : 2.0 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.: 7.0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :30.0 Median :0.0000 Median :0.0000 Median :0.0000
Mean :28.9 Mean :0.3967 Mean :0.1934 Mean :0.2033
3rd Qu.:52.0 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
Max. :61.0 Max. :1.0000 Max. :1.0000 Max. :1.0000
MetroDeparture MetroArrival MarketShare LoadFactor
Min. :1 Min. :1 Min. :13.20 Min. :78.73
1st Qu.:1 1st Qu.:1 1st Qu.:13.30 1st Qu.:83.32
Median :1 Median :1 Median :15.40 Median :83.32
Mean :1 Mean :1 Mean :21.18 Mean :85.13
3rd Qu.:1 3rd Qu.:1 3rd Qu.:39.60 3rd Qu.:87.20
Max. :1 Max. :1 Max. :39.60 Max. :94.06
model=lm(Price~Airline,data=x)
summary(model)
Call:
lm(formula = Price ~ Airline, data = x)
Residuals:
Min 1Q Median 3Q Max
-2877.1 -1445.1 -683.1 273.5 13135.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6335.0 368.0 17.214 < 2e-16 ***
AirlineIndiGo -1455.5 452.6 -3.216 0.00144 **
AirlineJet -838.9 417.1 -2.011 0.04522 *
AirlineSpice Jet -1240.2 523.7 -2.368 0.01851 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2356 on 301 degrees of freedom
Multiple R-squared: 0.03608, Adjusted R-squared: 0.02647
F-statistic: 3.755 on 3 and 301 DF, p-value: 0.01131
lookup = data.frame(PlaneModel=levels(x$PlaneModel), PlaneMod=c(1,2,3,4,5,6,7,8,9))
lookup2 = data.frame(Airline=levels(x$Airline), AirlineCode=c(1,2,3,4))
lookup3 = data.frame(DepartureCityCode=levels(x$DepartureCityCode), DCC1=c(1,2))
lookup4 = data.frame(ArrivalCityCode=levels(x$ArrivalCityCode), ACC1=c(1,2))
lookup5 = data.frame(Aircraft=levels(x$Aircraft), A1=c(1,2))
lookup6 = data.frame(IsWeekend=levels(x$IsWeekend), A1=c(1,2))
x$dept_mins <- with(x, x$DepartureTime %% 100)
x$dept_hr <- with(x, x$DepartureTime %/% 100)
x1 = merge(x, lookup, by="PlaneModel")
x2= merge(x1, lookup2, by="Airline")
x3= merge(x2, lookup3, by="DepartureCityCode")
x4= merge(x3, lookup4, by="ArrivalCityCode")
x5= merge(x4, lookup5, by="Aircraft")
x6= merge(x5, lookup6, by="IsWeekend")
head(x6)
IsWeekend Aircraft ArrivalCityCode DepartureCityCode Airline PlaneModel
1 No Airbus BOM DEL IndiGo A320
2 No Airbus BOM DEL IndiGo A320
3 No Airbus BOM DEL IndiGo A320
4 No Airbus BOM DEL IndiGo A320
5 No Airbus BOM DEL IndiGo A320
6 No Airbus BOM DEL IndiGo A320
FlightNumber DepartureTime ArrivalTime Departure FlyingMinutes Capacity
1 6E 197 1930 2145 PM 135 180
2 6E 179 830 1045 AM 135 180
3 6E 755 1600 1815 PM 135 180
4 6E 129 1730 1945 PM 135 180
5 6E 843 1835 2055 PM 140 180
6 6E 755 1600 1815 PM 135 180
SeatPitch SeatWidth DataCollectionDate DateDeparture Price
1 30 18 Sep 10 2018 Sep 17 2018 3305
2 30 18 Sep 8 2018 Oct 8 2018 3031
3 30 18 Sep 10 2018 Sep 12 2018 4234
4 30 18 Sep 10 2018 Nov 6 2018 4234
5 30 18 Sep 10 2018 Sep 12 2018 5153
6 30 18 Sep 10 2018 Nov 6 2018 4234
AdvancedBookingDays IsDiwali DayBeforeDiwali DayAfterDiwali
1 7 0 0 0
2 30 0 0 0
3 2 0 0 0
4 57 1 1 0
5 2 0 0 0
6 57 1 1 0
MetroDeparture MetroArrival MarketShare LoadFactor dept_mins dept_hr
1 1 1 39.6 87.2 30 19
2 1 1 39.6 87.2 30 8
3 1 1 39.6 87.2 0 16
4 1 1 39.6 87.2 30 17
5 1 1 39.6 87.2 35 18
6 1 1 39.6 87.2 0 16
PlaneMod AirlineCode DCC1 ACC1 A1.x A1.y
1 6 2 2 1 1 1
2 6 2 2 1 1 1
3 6 2 2 1 1 1
4 6 2 2 1 1 1
5 6 2 2 1 1 1
6 6 2 2 1 1 1
str(x6)
'data.frame': 305 obs. of 33 variables:
$ IsWeekend : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ Aircraft : Factor w/ 2 levels "Airbus","Boeing": 1 1 1 1 1 1 1 1 1 1 ...
$ ArrivalCityCode : Factor w/ 2 levels "BOM","DEL": 1 1 1 1 1 1 1 1 1 2 ...
$ DepartureCityCode : Factor w/ 2 levels "BOM","DEL": 2 2 2 2 2 2 2 2 2 1 ...
$ Airline : Factor w/ 4 levels "Air India","IndiGo",..: 2 2 2 2 2 2 2 2 2 1 ...
$ PlaneModel : Factor w/ 9 levels "738","739","77W",..: 6 6 6 6 6 6 6 6 6 7 ...
$ FlightNumber : Factor w/ 63 levels "6E 129","6E 155",..: 10 5 13 1 14 13 16 8 2 51 ...
$ DepartureTime : int 1930 830 1600 1730 1835 1600 640 1500 2225 1300 ...
$ ArrivalTime : int 2145 1045 1815 1945 2055 1815 855 1710 40 1515 ...
$ Departure : Factor w/ 2 levels "AM","PM": 2 1 2 2 2 2 1 2 2 2 ...
$ FlyingMinutes : int 135 135 135 135 140 135 135 130 135 135 ...
$ Capacity : int 180 180 180 180 180 180 180 180 180 170 ...
$ SeatPitch : int 30 30 30 30 30 30 30 30 30 31 ...
$ SeatWidth : num 18 18 18 18 18 18 18 18 18 17 ...
$ DataCollectionDate : Factor w/ 7 levels "Sep 10 2018",..: 1 7 1 1 1 1 7 7 1 6 ...
$ DateDeparture : Factor w/ 20 levels "Nov 6 2018","Nov 8 2018",..: 14 9 11 1 11 1 1 10 3 17 ...
$ Price : int 3305 3031 4234 4234 5153 4234 4234 4597 2712 5357 ...
$ AdvancedBookingDays: int 7 30 2 57 2 57 59 2 30 2 ...
$ IsDiwali : int 0 0 0 1 0 1 1 0 0 0 ...
$ DayBeforeDiwali : int 0 0 0 1 0 1 1 0 0 0 ...
$ DayAfterDiwali : int 0 0 0 0 0 0 0 0 0 0 ...
$ MetroDeparture : int 1 1 1 1 1 1 1 1 1 1 ...
$ MetroArrival : int 1 1 1 1 1 1 1 1 1 1 ...
$ MarketShare : num 39.6 39.6 39.6 39.6 39.6 39.6 39.6 39.6 39.6 13.3 ...
$ LoadFactor : num 87.2 87.2 87.2 87.2 87.2 ...
$ dept_mins : num 30 30 0 30 35 0 40 0 25 0 ...
$ dept_hr : num 19 8 16 17 18 16 6 15 22 13 ...
$ PlaneMod : num 6 6 6 6 6 6 6 6 6 7 ...
$ AirlineCode : num 2 2 2 2 2 2 2 2 2 1 ...
$ DCC1 : num 2 2 2 2 2 2 2 2 2 1 ...
$ ACC1 : num 1 1 1 1 1 1 1 1 1 2 ...
$ A1.x : num 1 1 1 1 1 1 1 1 1 1 ...
$ A1.y : num 1 1 1 1 1 1 1 1 1 1 ...
nums=unlist(sapply(x6,is.numeric))
air1=x6[,nums]
model=lm(Price~.,data=air1)
summary(model)
Call:
lm(formula = Price ~ ., data = air1)
Residuals:
Min 1Q Median 3Q Max
-2965.3 -1257.6 -413.6 677.6 11948.7
Coefficients: (5 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3826.1580 27928.8825 -0.137 0.8911
DepartureTime -0.6683 0.2791 -2.395 0.0173 *
ArrivalTime 0.2175 0.2486 0.875 0.3823
FlyingMinutes 39.1162 30.1560 1.297 0.1956
Capacity -8.9722 10.5692 -0.849 0.3966
SeatPitch 40.3964 578.3512 0.070 0.9444
SeatWidth 914.3721 720.7139 1.269 0.2056
AdvancedBookingDays -86.4992 12.3763 -6.989 1.94e-11 ***
IsDiwali 3881.0407 602.3680 6.443 4.93e-10 ***
DayBeforeDiwali 824.6751 375.5277 2.196 0.0289 *
DayAfterDiwali NA NA NA NA
MetroDeparture NA NA NA NA
MetroArrival NA NA NA NA
MarketShare -14.1518 32.9831 -0.429 0.6682
LoadFactor -52.3100 128.0880 -0.408 0.6833
dept_mins 2.6666 8.4963 0.314 0.7539
dept_hr NA NA NA NA
PlaneMod -216.4960 398.5895 -0.543 0.5874
AirlineCode 30.1896 471.4382 0.064 0.9490
DCC1 -1477.2846 356.1260 -4.148 4.42e-05 ***
ACC1 NA NA NA NA
A1.x -1350.5017 1957.2817 -0.690 0.4908
A1.y -403.0218 405.7635 -0.993 0.3214
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2058 on 287 degrees of freedom
Multiple R-squared: 0.2992, Adjusted R-squared: 0.2577
F-statistic: 7.207 on 17 and 287 DF, p-value: 1.241e-14
##Inferences
R squared => 29.92%
The model is significantly more valuable than a naive model, going by the p-value of the F-stat
The significant variables are DepartureTime, AdvancedBookingDays, isDiwali, DayBeforeDiwali and DepartureCityCode
The Departure Time was negatively affecting price, indicating morning flights were costlier
More the advanced booking days, lesser was the price. For every advanced booking day, the ticket was cheaper by 87 Rs
On Diwali day, the ticket was costlier by Rs 3,881
On the day before Diwali, the ticket was costlier by Rs 824
For Departure City Code, a flight from Bombay was costlier by Rs 1477
Slide With Plot
Log Linear Model
lnmodel=lm( log(Price) ~.,data=air1)
summary(lnmodel)
Call:
lm(formula = log(Price) ~ ., data = air1)
Residuals:
Min 1Q Median 3Q Max
-0.60819 -0.19249 -0.04363 0.14354 1.24224
Coefficients: (5 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.956e+00 3.924e+00 1.518 0.1302
DepartureTime -9.079e-05 3.921e-05 -2.315 0.0213 *
ArrivalTime 5.512e-05 3.493e-05 1.578 0.1157
FlyingMinutes 8.005e-03 4.237e-03 1.889 0.0599 .
Capacity -1.572e-03 1.485e-03 -1.058 0.2907
SeatPitch 1.901e-02 8.126e-02 0.234 0.8152
SeatWidth 1.538e-01 1.013e-01 1.519 0.1298
AdvancedBookingDays -1.460e-02 1.739e-03 -8.398 2.12e-15 ***
IsDiwali 7.117e-01 8.463e-02 8.409 1.96e-15 ***
DayBeforeDiwali 5.881e-02 5.276e-02 1.115 0.2660
DayAfterDiwali NA NA NA NA
MetroDeparture NA NA NA NA
MetroArrival NA NA NA NA
MarketShare -3.353e-03 4.634e-03 -0.723 0.4700
LoadFactor -5.519e-03 1.800e-02 -0.307 0.7593
dept_mins 4.396e-04 1.194e-03 0.368 0.7129
dept_hr NA NA NA NA
PlaneMod -2.881e-02 5.600e-02 -0.514 0.6074
AirlineCode 2.805e-03 6.623e-02 0.042 0.9662
DCC1 -2.655e-01 5.003e-02 -5.306 2.24e-07 ***
ACC1 NA NA NA NA
A1.x -1.444e-01 2.750e-01 -0.525 0.6000
A1.y -4.913e-02 5.701e-02 -0.862 0.3895
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2891 on 287 degrees of freedom
Multiple R-squared: 0.3866, Adjusted R-squared: 0.3502
F-statistic: 10.64 on 17 and 287 DF, p-value: < 2.2e-16
Log Linear Model with Plot
coefplot(model, intercept=FALSE)
Step Function
fullModel <- lm(Price ~ FlightNumber+Airline+DepartureCityCode+ArrivalCityCode+DepartureTime+ArrivalTime+Departure+FlyingMinutes+Aircraft+PlaneModel+Capacity+SeatPitch+SeatWidth+DataCollectionDate+DateDeparture+IsWeekend+AdvancedBookingDays+IsDiwali+DayBeforeDiwali+DayAfterDiwali+MetroDeparture+MetroArrival+MarketShare+LoadFactor, data = x)
stepmodel<-step(fullModel,trace=0,steps=100000)
stepModelSummary <- summary(stepmodel)
stepModelSummary
Call:
lm(formula = Price ~ FlightNumber + DateDeparture, data = x)
Residuals:
Min 1Q Median 3Q Max
-5733.7 -931.7 -46.8 710.0 5247.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4523.0 802.7 5.635 5.26e-08 ***
FlightNumber6E 155 -437.2 1047.6 -0.417 0.676839
FlightNumber6E 167 2236.6 1117.4 2.002 0.046532 *
FlightNumber6E 171 -378.2 1117.4 -0.338 0.735331
FlightNumber6E 179 1605.6 1117.4 1.437 0.152133
FlightNumber6E 181 1791.4 1117.4 1.603 0.110298
FlightNumber6E 185 1125.6 1047.6 1.074 0.283786
FlightNumber6E 189 -867.8 1117.4 -0.777 0.438195
FlightNumber6E 191 121.4 1047.6 0.116 0.907850
FlightNumber6E 197 551.8 1047.6 0.527 0.598911
FlightNumber6E 223 -248.8 1117.4 -0.223 0.824005
FlightNumber6E 665 -256.4 1047.6 -0.245 0.806878
FlightNumber6E 755 -63.8 1047.6 -0.061 0.951493
FlightNumber6E 843 536.4 1047.6 0.512 0.609143
FlightNumber6E 957 -468.6 1117.4 -0.419 0.675349
FlightNumber6E 993 1314.0 1117.4 1.176 0.240855
FlightNumber9W 301 2998.4 1088.1 2.756 0.006339 **
FlightNumber9W 302 881.6 1124.2 0.784 0.433761
FlightNumber9W 304 799.9 1088.1 0.735 0.463041
FlightNumber9W 307 3759.6 1131.0 3.324 0.001036 **
FlightNumber9W 309 3698.4 1301.6 2.841 0.004907 **
FlightNumber9W 310 2732.8 1301.6 2.100 0.036893 *
FlightNumber9W 311 4294.2 1131.0 3.797 0.000189 ***
FlightNumber9W 312 1133.7 1088.1 1.042 0.298585
FlightNumber9W 313 449.0 1124.2 0.399 0.690002
FlightNumber9W 316 476.4 1124.2 0.424 0.672160
FlightNumber9W 331 3959.0 1131.0 3.501 0.000561 ***
FlightNumber9W 332 448.0 1124.2 0.398 0.690656
FlightNumber9W 333 3722.8 1088.1 3.422 0.000741 ***
FlightNumber9W 334 1312.5 1118.7 1.173 0.241984
FlightNumber9W 336 1483.4 1124.2 1.320 0.188349
FlightNumber9W 339 2993.8 1088.1 2.752 0.006419 **
FlightNumber9W 346 3867.8 1088.1 3.555 0.000462 ***
FlightNumber9W 351 4816.0 1131.0 4.258 3.04e-05 ***
FlightNumber9W 352 702.1 1118.7 0.628 0.530945
FlightNumber9W 353 3460.4 1131.0 3.060 0.002487 **
FlightNumber9W 354 975.1 1118.7 0.872 0.384381
FlightNumber9W 355 3586.4 1088.1 3.296 0.001140 **
FlightNumber9W 358 576.2 1124.2 0.513 0.608793
FlightNumber9W 361 3197.8 1301.6 2.457 0.014781 *
FlightNumber9W 362 744.1 1118.7 0.665 0.506683
FlightNumber9W 373 2008.3 1118.7 1.795 0.073991 .
FlightNumber9W 376 1229.1 1118.7 1.099 0.273123
FlightNumber9W 384 1799.2 1088.1 1.654 0.099609 .
FlightNumber9W 762 3270.4 1088.1 3.006 0.002952 **
FlightNumber9W 839 4568.0 1131.0 4.039 7.39e-05 ***
FlightNumberAI 101 2201.9 1102.0 1.998 0.046914 *
FlightNumberAI 144 2590.3 1102.0 2.351 0.019614 *
FlightNumberAI 314 2979.3 1461.8 2.038 0.042717 *
FlightNumberAI 348 2088.8 1158.6 1.803 0.072754 .
FlightNumberAI 677 3986.7 1102.0 3.618 0.000367 ***
FlightNumberAI 806 3776.9 1102.0 3.427 0.000725 ***
FlightNumberAI 809 4207.3 1102.0 3.818 0.000174 ***
FlightNumberAI 866 3514.3 1102.0 3.189 0.001632 **
FlightNumberAI 888 4370.3 1102.0 3.966 9.85e-05 ***
FlightNumberSG 152 3240.9 1102.0 2.941 0.003616 **
FlightNumberSG 153 1336.1 1102.0 1.212 0.226621
FlightNumberSG 154 2526.9 1102.0 2.293 0.022775 *
FlightNumberSG 158 3366.9 1102.0 3.055 0.002522 **
FlightNumberSG 159 1252.1 1102.0 1.136 0.257081
FlightNumberSG 160 2568.9 1102.0 2.331 0.020635 *
FlightNumberSG 161 1031.7 1102.0 0.936 0.350174
FlightNumberSG 169 1703.3 1102.0 1.546 0.123599
DateDepartureNov 8 2018 -1042.0 303.1 -3.438 0.000699 ***
DateDepartureOct 10 2018 -1808.9 712.1 -2.540 0.011764 *
DateDepartureOct 13 2018 -1636.6 815.7 -2.006 0.046020 *
DateDepartureOct 14 2018 -1011.9 808.2 -1.252 0.211876
DateDepartureOct 15 2018 -3413.9 675.5 -5.054 9.02e-07 ***
DateDepartureOct 17 2018 -4054.7 828.5 -4.894 1.89e-06 ***
DateDepartureOct 19 2018 -2712.8 521.9 -5.198 4.54e-07 ***
DateDepartureOct 8 2018 -2125.2 716.2 -2.967 0.003331 **
DateDepartureSep 10 2018 6675.4 716.2 9.321 < 2e-16 ***
DateDepartureSep 12 2018 714.4 712.1 1.003 0.316892
DateDepartureSep 15 2018 -1515.5 563.0 -2.692 0.007641 **
DateDepartureSep 16 2018 1978.8 808.2 2.448 0.015119 *
DateDepartureSep 17 2018 -392.7 514.8 -0.763 0.446394
DateDepartureSep 19 2018 -2141.6 728.9 -2.938 0.003648 **
DateDepartureSep 20 2018 -676.9 815.7 -0.830 0.407486
DateDepartureSep 21 2018 -1000.5 455.4 -2.197 0.029045 *
DateDepartureSep 22 2018 -2610.9 675.5 -3.865 0.000146 ***
DateDepartureSep 24 2018 -3837.8 728.9 -5.265 3.29e-07 ***
DateDepartureSep 26 2018 -3027.3 528.9 -5.724 3.33e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1656 on 223 degrees of freedom
Multiple R-squared: 0.6471, Adjusted R-squared: 0.519
F-statistic: 5.049 on 81 and 223 DF, p-value: < 2.2e-16