Airline_Group5
Group 5
26-10-2018
First Slide
- Removing all the Columns that doesnt make sense rationally – Feature Selection
- We run linear regression to tell the values that significantly affect the predicition
- Our Top 3 terms are Departure Time
Slide with Trial 1
setwd("~/Desktop/Dropbox/Study Material/DAM/R_Workspace/Class 8 - Class Exercise/")
airline = read.csv("AirlinePricingData.csv")
colnames(airline)
[1] "FlightNumber" "Airline" "DepartureCityCode"
[4] "ArrivalCityCode" "DepartureTime" "ArrivalTime"
[7] "Departure" "FlyingMinutes" "Aircraft"
[10] "PlaneModel" "Capacity" "SeatPitch"
[13] "SeatWidth" "DataCollectionDate" "DateDeparture"
[16] "IsWeekend" "Price" "AdvancedBookingDays"
[19] "IsDiwali" "DayBeforeDiwali" "DayAfterDiwali"
[22] "MetroDeparture" "MetroArrival" "MarketShare"
[25] "LoadFactor"
m = lm(Price~DepartureTime + Capacity + SeatPitch + SeatWidth+ IsWeekend + AdvancedBookingDays + IsDiwali + DayBeforeDiwali + DayAfterDiwali + MarketShare + LoadFactor,data = airline)
summary(m)
Call:
lm(formula = Price ~ DepartureTime + Capacity + SeatPitch + SeatWidth +
IsWeekend + AdvancedBookingDays + IsDiwali + DayBeforeDiwali +
DayAfterDiwali + MarketShare + LoadFactor, data = airline)
Residuals:
Min 1Q Median 3Q Max
-3663.5 -1174.0 -412.4 792.9 11594.0
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9839.9687 11184.8065 0.880 0.379707
DepartureTime -0.7758 0.2295 -3.380 0.000824 ***
Capacity -5.7328 5.6353 -1.017 0.309846
SeatPitch -248.2057 259.8908 -0.955 0.340343
SeatWidth 1024.9418 525.8566 1.949 0.052236 .
IsWeekendYes -963.6867 385.0152 -2.503 0.012858 *
AdvancedBookingDays -90.5762 12.5944 -7.192 5.32e-12 ***
IsDiwali 3929.3414 615.3382 6.386 6.65e-10 ***
DayBeforeDiwali 789.1796 383.5974 2.057 0.040538 *
DayAfterDiwali NA NA NA NA
MarketShare -33.3012 20.8044 -1.601 0.110522
LoadFactor -129.8586 47.3710 -2.741 0.006494 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2104 on 294 degrees of freedom
Multiple R-squared: 0.2496, Adjusted R-squared: 0.224
F-statistic: 9.777 on 10 and 294 DF, p-value: 4.116e-14
Slide With Trial 2
Call:
lm(formula = Price ~ DepartureTime + IsWeekend + AdvancedBookingDays +
IsDiwali + DayBeforeDiwali + LoadFactor, data = airline)
Residuals:
Min 1Q Median 3Q Max
-3561.5 -1203.2 -454.7 750.1 11648.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16698.9425 2602.5654 6.416 5.48e-10 ***
DepartureTime -0.7245 0.2178 -3.326 0.000990 ***
IsWeekendYes -875.8204 376.5248 -2.326 0.020686 *
AdvancedBookingDays -89.7389 12.4418 -7.213 4.55e-12 ***
IsDiwali 3920.4401 610.3107 6.424 5.25e-10 ***
DayBeforeDiwali 790.0193 383.6761 2.059 0.040355 *
LoadFactor -110.3693 29.2958 -3.767 0.000199 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2105 on 298 degrees of freedom
Multiple R-squared: 0.2384, Adjusted R-squared: 0.2231
F-statistic: 15.55 on 6 and 298 DF, p-value: 1.599e-15
Slide With Trial 3
Call:
lm(formula = Price ~ DepartureTime + IsWeekend + AdvancedBookingDays +
IsDiwali + LoadFactor, data = airline)
Residuals:
Min 1Q Median 3Q Max
-3159.1 -1240.2 -589.7 632.1 11626.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16721.2351 2616.6048 6.390 6.33e-10 ***
DepartureTime -0.7341 0.2189 -3.353 0.000902 ***
IsWeekendYes -877.5436 378.5583 -2.318 0.021117 *
AdvancedBookingDays -91.3020 12.4857 -7.312 2.42e-12 ***
IsDiwali 4369.0993 573.1695 7.623 3.32e-13 ***
LoadFactor -110.2523 29.4541 -3.743 0.000218 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2116 on 299 degrees of freedom
Multiple R-squared: 0.2276, Adjusted R-squared: 0.2147
F-statistic: 17.62 on 5 and 299 DF, p-value: 2.684e-15
Slide With Log Linear Regression (Appended)
fit <- lm(Price ~ FlightNumber+Airline+DepartureCityCode+ArrivalCityCode+DepartureTime+ArrivalTime+Departure+FlyingMinutes+Aircraft+PlaneModel+Capacity+SeatPitch+SeatWidth+DataCollectionDate+DateDeparture+IsWeekend+Price+AdvancedBookingDays+IsDiwali+DayBeforeDiwali+DayAfterDiwali+MetroDeparture+MetroArrival+MarketShare+LoadFactor, data = airline)
summary(fit)
Call:
lm(formula = Price ~ FlightNumber + Airline + DepartureCityCode +
ArrivalCityCode + DepartureTime + ArrivalTime + Departure +
FlyingMinutes + Aircraft + PlaneModel + Capacity + SeatPitch +
SeatWidth + DataCollectionDate + DateDeparture + IsWeekend +
Price + AdvancedBookingDays + IsDiwali + DayBeforeDiwali +
DayAfterDiwali + MetroDeparture + MetroArrival + MarketShare +
LoadFactor, data = airline)
Residuals:
Min 1Q Median 3Q Max
-5733.7 -931.7 -46.8 710.0 5247.3
Coefficients: (36 not defined because of singularities)
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
AirlineIndiGo NA NA NA NA
AirlineJet NA NA NA NA
AirlineSpice Jet NA NA NA NA
DepartureCityCodeDEL NA NA NA NA
ArrivalCityCodeDEL NA NA NA NA
DepartureTime NA NA NA NA
ArrivalTime NA NA NA NA
DeparturePM NA NA NA NA
FlyingMinutes NA NA NA NA
AircraftBoeing NA NA NA NA
PlaneModel739 NA NA NA NA
PlaneModel77W NA NA NA NA
PlaneModel788 NA NA NA NA
PlaneModelA319 NA NA NA NA
PlaneModelA320 NA NA NA NA
PlaneModelA321 NA NA NA NA
PlaneModelA332 NA NA NA NA
PlaneModelA333 NA NA NA NA
Capacity NA NA NA NA
SeatPitch NA NA NA NA
SeatWidth NA NA NA NA
DataCollectionDateSep 13 2018 NA NA NA NA
DataCollectionDateSep 14 2018 NA NA NA NA
DataCollectionDateSep 15 2018 NA NA NA NA
DataCollectionDateSep 17 2018 NA NA NA NA
DataCollectionDateSep 19 2018 NA NA NA NA
DataCollectionDateSep 8 2018 NA NA NA NA
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 ***
IsWeekendYes NA NA NA NA
AdvancedBookingDays NA NA NA NA
IsDiwali NA NA NA NA
DayBeforeDiwali NA NA NA NA
DayAfterDiwali NA NA NA NA
MetroDeparture NA NA NA NA
MetroArrival NA NA NA NA
MarketShare NA NA NA NA
LoadFactor NA NA NA NA
---
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
Slide With Log Linear Regression (Appended)
fit <- lm(log(Price) ~ FlightNumber+Airline+DepartureCityCode+ArrivalCityCode+DepartureTime+ArrivalTime+Departure+FlyingMinutes+Aircraft+PlaneModel+Capacity+SeatPitch+SeatWidth+DataCollectionDate+DateDeparture+IsWeekend+Price+AdvancedBookingDays+IsDiwali+DayBeforeDiwali+DayAfterDiwali+MetroDeparture+MetroArrival+MarketShare+LoadFactor, data = airline)
summary(fit)
Call:
lm(formula = log(Price) ~ FlightNumber + Airline + DepartureCityCode +
ArrivalCityCode + DepartureTime + ArrivalTime + Departure +
FlyingMinutes + Aircraft + PlaneModel + Capacity + SeatPitch +
SeatWidth + DataCollectionDate + DateDeparture + IsWeekend +
Price + AdvancedBookingDays + IsDiwali + DayBeforeDiwali +
DayAfterDiwali + MetroDeparture + MetroArrival + MarketShare +
LoadFactor, data = airline)
Residuals:
Min 1Q Median 3Q Max
-0.27989 -0.03110 0.00284 0.03459 0.22345
Coefficients: (36 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.760e+00 3.523e-02 220.243 < 2e-16 ***
FlightNumber6E 155 -8.345e-02 4.304e-02 -1.939 0.053791 .
FlightNumber6E 167 2.742e-02 4.630e-02 0.592 0.554239
FlightNumber6E 171 1.619e-02 4.590e-02 0.353 0.724618
FlightNumber6E 179 2.031e-02 4.610e-02 0.441 0.659900
FlightNumber6E 181 4.544e-02 4.615e-02 0.985 0.325906
FlightNumber6E 185 5.091e-02 4.313e-02 1.180 0.239152
FlightNumber6E 189 5.327e-02 4.595e-02 1.159 0.247556
FlightNumber6E 191 1.237e-02 4.302e-02 0.287 0.774004
FlightNumber6E 197 2.831e-02 4.305e-02 0.658 0.511407
FlightNumber6E 223 8.099e-02 4.589e-02 1.765 0.078971 .
FlightNumber6E 665 -5.946e-02 4.303e-02 -1.382 0.168406
FlightNumber6E 755 -1.386e-02 4.302e-02 -0.322 0.747690
FlightNumber6E 843 3.775e-02 4.305e-02 0.877 0.381520
FlightNumber6E 957 7.249e-02 4.591e-02 1.579 0.115711
FlightNumber6E 993 1.196e-03 4.603e-02 0.026 0.979292
FlightNumber9W 301 1.195e-01 4.544e-02 2.630 0.009144 **
FlightNumber9W 302 5.474e-02 4.623e-02 1.184 0.237608
FlightNumber9W 304 -1.232e-02 4.474e-02 -0.275 0.783240
FlightNumber9W 307 1.085e-01 4.758e-02 2.280 0.023550 *
FlightNumber9W 309 9.421e-02 5.441e-02 1.731 0.084753 .
FlightNumber9W 310 6.857e-02 5.398e-02 1.270 0.205323
FlightNumber9W 311 1.247e-01 4.792e-02 2.601 0.009915 **
FlightNumber9W 312 3.595e-02 4.479e-02 0.803 0.423117
FlightNumber9W 313 1.606e-02 4.618e-02 0.348 0.728290
FlightNumber9W 316 1.343e-02 4.618e-02 0.291 0.771406
FlightNumber9W 331 1.213e-01 4.770e-02 2.543 0.011673 *
FlightNumber9W 332 5.022e-03 4.618e-02 0.109 0.913504
FlightNumber9W 333 1.327e-01 4.584e-02 2.894 0.004179 **
FlightNumber9W 334 5.103e-02 4.608e-02 1.107 0.269397
FlightNumber9W 336 5.075e-02 4.635e-02 1.095 0.274670
FlightNumber9W 339 8.187e-02 4.544e-02 1.802 0.072921 .
FlightNumber9W 346 1.336e-01 4.593e-02 2.909 0.003997 **
FlightNumber9W 351 6.682e-02 4.830e-02 1.383 0.167911
FlightNumber9W 352 2.236e-02 4.598e-02 0.486 0.627195
FlightNumber9W 353 1.023e-01 4.741e-02 2.157 0.032059 *
FlightNumber9W 354 3.639e-02 4.602e-02 0.791 0.429940
FlightNumber9W 355 1.248e-01 4.576e-02 2.726 0.006916 **
FlightNumber9W 358 2.004e-02 4.619e-02 0.434 0.664815
FlightNumber9W 361 6.415e-02 5.417e-02 1.184 0.237609
FlightNumber9W 362 2.641e-02 4.599e-02 0.574 0.566402
FlightNumber9W 373 7.566e-02 4.627e-02 1.635 0.103440
FlightNumber9W 376 5.052e-02 4.607e-02 1.097 0.273939
FlightNumber9W 384 1.012e-01 4.496e-02 2.251 0.025351 *
FlightNumber9W 762 1.164e-01 4.558e-02 2.554 0.011329 *
FlightNumber9W 839 1.227e-01 4.811e-02 2.551 0.011427 *
FlightNumberAI 101 9.026e-02 4.566e-02 1.977 0.049280 *
FlightNumberAI 144 1.174e-01 4.581e-02 2.563 0.011039 *
FlightNumberAI 314 1.106e-01 6.059e-02 1.825 0.069328 .
FlightNumberAI 348 9.239e-02 4.792e-02 1.928 0.055158 .
FlightNumberAI 677 7.305e-02 4.656e-02 1.569 0.118086
FlightNumberAI 806 8.740e-02 4.643e-02 1.882 0.061099 .
FlightNumberAI 809 7.826e-02 4.671e-02 1.676 0.095231 .
FlightNumberAI 866 7.383e-02 4.627e-02 1.596 0.112021
FlightNumberAI 888 8.725e-02 4.682e-02 1.863 0.063720 .
FlightNumberSG 152 1.179e-01 4.612e-02 2.557 0.011228 *
FlightNumberSG 153 -3.336e-03 4.540e-02 -0.073 0.941486
FlightNumberSG 154 5.169e-02 4.578e-02 1.129 0.260089
FlightNumberSG 158 8.224e-02 4.619e-02 1.780 0.076382 .
FlightNumberSG 159 -1.604e-02 4.538e-02 -0.353 0.724067
FlightNumberSG 160 8.730e-02 4.580e-02 1.906 0.057940 .
FlightNumberSG 161 -1.472e-02 4.534e-02 -0.325 0.745803
FlightNumberSG 169 2.468e-02 4.550e-02 0.543 0.587968
AirlineIndiGo NA NA NA NA
AirlineJet NA NA NA NA
AirlineSpice Jet NA NA NA NA
DepartureCityCodeDEL NA NA NA NA
ArrivalCityCodeDEL NA NA NA NA
DepartureTime NA NA NA NA
ArrivalTime NA NA NA NA
DeparturePM NA NA NA NA
FlyingMinutes NA NA NA NA
AircraftBoeing NA NA NA NA
PlaneModel739 NA NA NA NA
PlaneModel77W NA NA NA NA
PlaneModel788 NA NA NA NA
PlaneModelA319 NA NA NA NA
PlaneModelA320 NA NA NA NA
PlaneModelA321 NA NA NA NA
PlaneModelA332 NA NA NA NA
PlaneModelA333 NA NA NA NA
Capacity NA NA NA NA
SeatPitch NA NA NA NA
SeatWidth NA NA NA NA
DataCollectionDateSep 13 2018 NA NA NA NA
DataCollectionDateSep 14 2018 NA NA NA NA
DataCollectionDateSep 15 2018 NA NA NA NA
DataCollectionDateSep 17 2018 NA NA NA NA
DataCollectionDateSep 19 2018 NA NA NA NA
DataCollectionDateSep 8 2018 NA NA NA NA
DateDepartureNov 8 2018 4.512e-02 1.277e-02 3.533 0.000500 ***
DateDepartureOct 10 2018 -1.641e-01 2.967e-02 -5.533 8.84e-08 ***
DateDepartureOct 13 2018 -7.448e-02 3.380e-02 -2.204 0.028577 *
DateDepartureOct 14 2018 3.081e-02 3.331e-02 0.925 0.355964
DateDepartureOct 15 2018 -1.267e-01 2.929e-02 -4.327 2.28e-05 ***
DateDepartureOct 17 2018 -4.017e-02 3.580e-02 -1.122 0.263109
DateDepartureOct 19 2018 -2.008e-02 2.269e-02 -0.885 0.377261
DateDepartureOct 8 2018 -1.891e-01 2.999e-02 -6.306 1.53e-09 ***
DateDepartureSep 10 2018 -7.590e-02 3.467e-02 -2.189 0.029624 *
DateDepartureSep 12 2018 1.076e-01 2.931e-02 3.672 0.000301 ***
DateDepartureSep 15 2018 -7.558e-02 2.349e-02 -3.217 0.001487 **
DateDepartureSep 16 2018 1.305e-01 3.363e-02 3.879 0.000138 ***
DateDepartureSep 17 2018 1.541e-02 2.117e-02 0.728 0.467571
DateDepartureSep 19 2018 4.517e-02 3.051e-02 1.481 0.140106
DateDepartureSep 20 2018 3.369e-02 3.355e-02 1.004 0.316441
DateDepartureSep 21 2018 6.754e-02 1.890e-02 3.573 0.000433 ***
DateDepartureSep 22 2018 -2.997e-02 2.866e-02 -1.046 0.296799
DateDepartureSep 24 2018 -4.352e-02 3.174e-02 -1.371 0.171687
DateDepartureSep 26 2018 -4.464e-02 2.326e-02 -1.919 0.056270 .
IsWeekendYes NA NA NA NA
Price 1.314e-04 2.750e-06 47.786 < 2e-16 ***
AdvancedBookingDays NA NA NA NA
IsDiwali NA NA NA NA
DayBeforeDiwali NA NA NA NA
DayAfterDiwali NA NA NA NA
MetroDeparture NA NA NA NA
MetroArrival NA NA NA NA
MarketShare NA NA NA NA
LoadFactor NA NA NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06802 on 222 degrees of freedom
Multiple R-squared: 0.9737, Adjusted R-squared: 0.964
F-statistic: 100.4 on 82 and 222 DF, p-value: < 2.2e-16
Slide With Log Linear Regression (Appended)
newfit <- step(fit, trace=0, step=1000)
summary(newfit)
Call:
lm(formula = log(Price) ~ DateDeparture + Price, data = airline)
Residuals:
Min 1Q Median 3Q Max
-0.35606 -0.02387 0.00537 0.03646 0.25989
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.748e+00 1.696e-02 456.955 < 2e-16 ***
DateDepartureNov 8 2018 5.622e-02 1.329e-02 4.229 3.16e-05 ***
DateDepartureOct 10 2018 -1.862e-01 2.824e-02 -6.592 2.11e-10 ***
DateDepartureOct 13 2018 -7.358e-02 3.151e-02 -2.335 0.020244 *
DateDepartureOct 14 2018 3.774e-02 3.114e-02 1.212 0.226530
DateDepartureOct 15 2018 -6.185e-02 2.644e-02 -2.339 0.020033 *
DateDepartureOct 17 2018 3.138e-02 3.116e-02 1.007 0.314670
DateDepartureOct 19 2018 8.792e-03 2.074e-02 0.424 0.671906
DateDepartureOct 8 2018 -1.692e-01 2.818e-02 -6.004 5.88e-09 ***
DateDepartureSep 10 2018 -1.468e-01 2.969e-02 -4.945 1.31e-06 ***
DateDepartureSep 12 2018 5.955e-02 2.726e-02 2.185 0.029717 *
DateDepartureSep 15 2018 -6.795e-02 2.227e-02 -3.051 0.002496 **
DateDepartureSep 16 2018 1.065e-01 3.103e-02 3.433 0.000686 ***
DateDepartureSep 17 2018 1.600e-02 1.992e-02 0.803 0.422695
DateDepartureSep 19 2018 8.950e-02 2.581e-02 3.467 0.000608 ***
DateDepartureSep 20 2018 2.468e-02 3.117e-02 0.792 0.429190
DateDepartureSep 21 2018 7.949e-02 1.777e-02 4.473 1.12e-05 ***
DateDepartureSep 22 2018 2.663e-02 2.611e-02 1.020 0.308668
DateDepartureSep 24 2018 1.831e-02 2.614e-02 0.700 0.484192
DateDepartureSep 26 2018 -1.196e-02 2.086e-02 -0.573 0.566890
Price 1.417e-04 2.207e-06 64.227 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07212 on 284 degrees of freedom
Multiple R-squared: 0.9622, Adjusted R-squared: 0.9596
F-statistic: 361.7 on 20 and 284 DF, p-value: < 2.2e-16
Slide With Log Linear Regression (Appended)
fit <- lm(Price ~ IsDiwali+DepartureCityCode+AdvancedBookingDays, data = airline)
summary(fit)
Call:
lm(formula = Price ~ IsDiwali + DepartureCityCode + AdvancedBookingDays,
data = airline)
Residuals:
Min 1Q Median 3Q Max
-3214.5 -1246.7 -503.8 487.0 12616.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6854.58 249.87 27.433 < 2e-16 ***
IsDiwali 4253.85 565.19 7.526 6.09e-13 ***
DepartureCityCodeDEL -1289.79 244.43 -5.277 2.52e-07 ***
AdvancedBookingDays -83.29 12.45 -6.692 1.07e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2093 on 301 degrees of freedom
Multiple R-squared: 0.2393, Adjusted R-squared: 0.2318
F-statistic: 31.57 on 3 and 301 DF, p-value: < 2.2e-16
Slide With Log Linear Regression (Appended)
fit <- lm(log(Price) ~ IsDiwali+DepartureCityCode+AdvancedBookingDays, data = airline)
summary(fit)
Call:
lm(formula = log(Price) ~ IsDiwali + DepartureCityCode + AdvancedBookingDays,
data = airline)
Residuals:
Min 1Q Median 3Q Max
-0.65586 -0.19803 -0.05366 0.11702 1.29163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.78076 0.03493 251.391 < 2e-16 ***
IsDiwali 0.73768 0.07901 9.337 < 2e-16 ***
DepartureCityCodeDEL -0.24494 0.03417 -7.169 5.89e-12 ***
AdvancedBookingDays -0.01425 0.00174 -8.187 7.66e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2926 on 301 degrees of freedom
Multiple R-squared: 0.3409, Adjusted R-squared: 0.3343
F-statistic: 51.89 on 3 and 301 DF, p-value: < 2.2e-16
Slide With Log Linear Regression (Appended)
stepfit <- step(fit, step=1000, trace=0)
summary(stepfit)
Call:
lm(formula = log(Price) ~ IsDiwali + DepartureCityCode + AdvancedBookingDays,
data = airline)
Residuals:
Min 1Q Median 3Q Max
-0.65586 -0.19803 -0.05366 0.11702 1.29163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.78076 0.03493 251.391 < 2e-16 ***
IsDiwali 0.73768 0.07901 9.337 < 2e-16 ***
DepartureCityCodeDEL -0.24494 0.03417 -7.169 5.89e-12 ***
AdvancedBookingDays -0.01425 0.00174 -8.187 7.66e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2926 on 301 degrees of freedom
Multiple R-squared: 0.3409, Adjusted R-squared: 0.3343
F-statistic: 51.89 on 3 and 301 DF, p-value: < 2.2e-16
Slide With Log Linear Regression (Appended)
library(coefplot)
coefplot(stepfit)
Slide With Visualization
