Project 2 (Airline Delays) Data 101
Wesley Samimi
Research Question
Does the carrier have an affect on delay times for a flight?
Introduction
The data in is project is from the Bureau of Transportation Statistics. The data consists of airline delays for December of 2019 and 2020. The dataset consists of 3351 observations and has 21 variables. The two variables that will be the focus of the project to answer the research question will be carrier, which is the airline carrier, and arr_delay, which is the the total time, in minutes, of the flight delay.
Load Libraries and Data
library(tidyverse)
library(ggridges) # Library to create a visualization learned in Math 217
setwd("C:/Users/wesle/Downloads/Data 101")
alds <- read_csv("airline_delay - airline_delay.csv")head(alds)## # A tibble: 6 × 21
## year month carrier carrier_name airport airport_name arr_flights arr_del15
## <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 2020 12 9E Endeavor Air I… ABE Allentown/B… 44 3
## 2 2020 12 9E Endeavor Air I… ABY Albany, GA:… 90 1
## 3 2020 12 9E Endeavor Air I… AEX Alexandria,… 88 8
## 4 2020 12 9E Endeavor Air I… AGS Augusta, GA… 184 9
## 5 2020 12 9E Endeavor Air I… ALB Albany, NY:… 76 11
## 6 2020 12 9E Endeavor Air I… ATL Atlanta, GA… 5985 445
## # ℹ 13 more variables: carrier_ct <dbl>, weather_ct <dbl>, nas_ct <dbl>,
## # security_ct <dbl>, late_aircraft_ct <dbl>, arr_cancelled <dbl>,
## # arr_diverted <dbl>, arr_delay <dbl>, carrier_delay <dbl>,
## # weather_delay <dbl>, nas_delay <dbl>, security_delay <dbl>,
## # late_aircraft_delay <dbl>str(alds)## spc_tbl_ [3,351 × 21] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ year : num [1:3351] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ month : num [1:3351] 12 12 12 12 12 12 12 12 12 12 ...
## $ carrier : chr [1:3351] "9E" "9E" "9E" "9E" ...
## $ carrier_name : chr [1:3351] "Endeavor Air Inc." "Endeavor Air Inc." "Endeavor Air Inc." "Endeavor Air Inc." ...
## $ airport : chr [1:3351] "ABE" "ABY" "AEX" "AGS" ...
## $ airport_name : chr [1:3351] "Allentown/Bethlehem/Easton, PA: Lehigh Valley International" "Albany, GA: Southwest Georgia Regional" "Alexandria, LA: Alexandria International" "Augusta, GA: Augusta Regional at Bush Field" ...
## $ arr_flights : num [1:3351] 44 90 88 184 76 ...
## $ arr_del15 : num [1:3351] 3 1 8 9 11 445 14 10 14 19 ...
## $ carrier_ct : num [1:3351] 1.63 0.96 5.75 4.17 4.78 ...
## $ weather_ct : num [1:3351] 0 0 0 0 0 ...
## $ nas_ct : num [1:3351] 0.12 0.04 1.6 1.83 5.22 ...
## $ security_ct : num [1:3351] 0 0 0 0 0 1 0 0 0 0 ...
## $ late_aircraft_ct : num [1:3351] 1.25 0 0.65 3 1 ...
## $ arr_cancelled : num [1:3351] 0 0 0 0 1 5 1 0 1 3 ...
## $ arr_diverted : num [1:3351] 1 0 1 0 0 0 0 1 1 0 ...
## $ arr_delay : num [1:3351] 89 23 338 508 692 ...
## $ carrier_delay : num [1:3351] 56 22 265 192 398 ...
## $ weather_delay : num [1:3351] 0 0 0 0 0 ...
## $ nas_delay : num [1:3351] 3 1 45 92 178 5060 182 24 223 389 ...
## $ security_delay : num [1:3351] 0 0 0 0 0 16 0 0 0 0 ...
## $ late_aircraft_delay: num [1:3351] 30 0 28 224 116 ...
## - attr(*, "spec")=
## .. cols(
## .. year = col_double(),
## .. month = col_double(),
## .. carrier = col_character(),
## .. carrier_name = col_character(),
## .. airport = col_character(),
## .. airport_name = col_character(),
## .. arr_flights = col_double(),
## .. arr_del15 = col_double(),
## .. carrier_ct = col_double(),
## .. weather_ct = col_double(),
## .. nas_ct = col_double(),
## .. security_ct = col_double(),
## .. late_aircraft_ct = col_double(),
## .. arr_cancelled = col_double(),
## .. arr_diverted = col_double(),
## .. arr_delay = col_double(),
## .. carrier_delay = col_double(),
## .. weather_delay = col_double(),
## .. nas_delay = col_double(),
## .. security_delay = col_double(),
## .. late_aircraft_delay = col_double()
## .. )
## - attr(*, "problems")=<externalptr>Cleaning Data
colSums(is.na(alds))## year month carrier carrier_name
## 0 0 0 0
## airport airport_name arr_flights arr_del15
## 0 0 8 8
## carrier_ct weather_ct nas_ct security_ct
## 8 8 8 8
## late_aircraft_ct arr_cancelled arr_diverted arr_delay
## 8 8 8 8
## carrier_delay weather_delay nas_delay security_delay
## 8 8 8 8
## late_aircraft_delay
## 8alds <- alds |>
filter(!is.na(arr_delay))Adding a New Column
alds1 <- alds |>
mutate(arr_delay_h = arr_delay/60)Visualizations
# Code from Math 217
ggplot(alds, aes(x = arr_delay, y = carrier)) +
geom_density_ridges(aes(fill = carrier), alpha = 0.5)## Picking joint bandwidth of 559
Removed all NAs as with them some of the code used later would not work and there are only 8 of them out of the 3351 total observations so there shouldn’t be much of an impact on the final results. I also added a new column that would be the arrival delay in hours by dividing arr_delay by 60 as arr_delay is in minutes. The visualization created above used code learned from Math 217. The distribution of all of the graphs in the visualization appear to be similar with them all being right-skewed.
Hypothesis
\(H_0\): \(\mu_1\) = \(\mu_2\) = \(\mu_3\) = … = \(\mu_1\)\(_7\)
\(H_a\): At least one \(\mu_i\) is different from the rest
ANOVA Test
anovar <- aov(arr_delay ~ carrier, data = alds)
anovar## Call:
## aov(formula = arr_delay ~ carrier, data = alds)
##
## Terms:
## carrier Residuals
## Sum of Squares 11329280276 342186529393
## Deg. of Freedom 16 3326
##
## Residual standard error: 10143.09
## Estimated effects may be unbalancedanovar1 <- aov(arr_delay_h ~ carrier, data = alds1)
anovar1## Call:
## aov(formula = arr_delay_h ~ carrier, data = alds1)
##
## Terms:
## carrier Residuals
## Sum of Squares 3147022 95051814
## Deg. of Freedom 16 3326
##
## Residual standard error: 169.0515
## Estimated effects may be unbalancedsummary(anovar)## Df Sum Sq Mean Sq F value Pr(>F)
## carrier 16 1.133e+10 708080017 6.882 8.41e-16 ***
## Residuals 3326 3.422e+11 102882300
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(anovar1)## Df Sum Sq Mean Sq F value Pr(>F)
## carrier 16 3147022 196689 6.882 8.41e-16 ***
## Residuals 3326 95051814 28578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The p-value is 8.41e-16 which is very small. The p-value is much smaller than the default α of 0.05. This means we reject the null and that at least one of the mean arrival delays of one of the airline carriers is different than the rest.
Tukey’s Honestly Significant Difference Test
TukeyHSD(anovar)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = arr_delay ~ carrier, data = alds)
##
## $carrier
## diff lwr upr p adj
## AA-9E 4046.40577 633.65887 7459.15267 0.0048042
## AS-9E 527.64129 -3215.66851 4270.95108 1.0000000
## B6-9E 5296.48156 1331.11280 9261.85032 0.0004956
## DL-9E 2365.41013 -850.84011 5581.66038 0.4671532
## EV-9E 487.52087 -3645.35407 4620.39581 1.0000000
## F9-9E -645.44960 -4131.38368 2840.48449 0.9999998
## G4-9E -907.12677 -4161.17130 2346.91776 0.9999355
## HA-9E -849.48015 -7075.53821 5376.57791 1.0000000
## MQ-9E -137.59300 -3264.56631 2989.38031 1.0000000
## NK-9E 1257.23844 -3004.15417 5518.63105 0.9998635
## OH-9E 656.48647 -2814.00017 4126.97311 0.9999997
## OO-9E 1856.07777 -977.02909 4689.18463 0.6787383
## UA-9E 2821.77781 -628.76562 6272.32124 0.2731122
## WN-9E 5803.06398 2301.24194 9304.88601 0.0000014
## YV-9E 512.70645 -2816.14260 3841.55550 1.0000000
## YX-9E 851.23496 -2575.37240 4277.84233 0.9999866
## AS-AA -3518.76448 -7359.63485 322.10589 0.1185265
## B6-AA 1250.07580 -2807.51787 5307.66946 0.9997607
## DL-AA -1680.99563 -5010.28724 1648.29598 0.9467335
## EV-AA -3558.88490 -7780.32686 662.55706 0.2247126
## F9-AA -4691.85536 -8282.35024 -1101.36049 0.0007870
## G4-AA -4953.53254 -8319.34935 -1587.71573 0.0000487
## HA-AA -4895.88592 -11181.08408 1389.31225 0.3585781
## MQ-AA -4183.99877 -7427.12674 -940.87079 0.0010213
## NK-AA -2789.16733 -7136.50950 1558.17484 0.7124853
## OH-AA -3389.91930 -6965.41850 185.57989 0.0866449
## OO-AA -2190.32800 -5151.14064 770.48464 0.4559362
## UA-AA -1224.62796 -4780.77291 2331.51700 0.9990381
## WN-AA 1756.65821 -1849.26394 5362.58036 0.9610622
## YV-AA -3533.69932 -6971.88968 -95.50895 0.0365189
## YX-AA -3195.17080 -6728.09523 337.75363 0.1323181
## B6-AS 4768.84028 429.53237 9108.14819 0.0153699
## DL-AS 1837.76885 -1829.61570 5505.15339 0.9500803
## EV-AS -40.12041 -4533.01469 4452.77387 1.0000000
## F9-AS -1173.09088 -5079.13494 2732.95318 0.9998278
## G4-AS -1434.76806 -5135.34228 2265.80617 0.9961570
## HA-AS -1377.12143 -7847.76613 5093.52326 0.9999985
## MQ-AS -665.23428 -4254.58044 2924.11187 0.9999998
## NK-AS 729.59715 -3881.79209 5340.98639 1.0000000
## OH-AS 128.84518 -3763.41911 4021.10947 1.0000000
## OO-AS 1328.43649 -2008.01946 4664.89243 0.9948383
## UA-AS 2294.13653 -1580.35618 6168.62923 0.8222852
## WN-AS 5275.42269 1355.19293 9195.65245 0.0004212
## YV-AS -14.93484 -3781.45575 3751.58607 1.0000000
## YX-AS 323.59368 -3529.59744 4176.78480 1.0000000
## DL-B6 -2931.07143 -6824.84731 962.70445 0.4226933
## EV-B6 -4808.96069 -9488.47778 -129.44360 0.0365683
## F9-B6 -5941.93116 -10061.27108 -1822.59123 0.0000818
## G4-B6 -6203.60833 -10128.66002 -2278.55664 0.0000064
## HA-B6 -6145.96171 -12747.55424 455.63082 0.1027259
## MQ-B6 -5434.07456 -9254.43934 -1613.70978 0.0001161
## NK-B6 -4039.24313 -8832.64400 754.15775 0.2253994
## OH-B6 -4639.99510 -8746.27108 -533.71911 0.0102781
## OO-B6 -3440.40379 -7024.21733 143.40974 0.0767305
## UA-B6 -2474.70375 -6564.13830 1614.73080 0.7957526
## WN-B6 506.58241 -3626.21114 4639.37596 1.0000000
## YV-B6 -4783.77511 -8771.06253 -796.48769 0.0039347
## YX-B6 -4445.24660 -8514.50488 -375.98831 0.0167099
## EV-DL -1877.88926 -5942.12307 2186.34454 0.9761373
## F9-DL -3010.85973 -6415.13317 393.41371 0.1585478
## G4-DL -3272.53690 -6438.94607 -106.12774 0.0341668
## HA-DL -3214.89028 -9395.59738 2965.81681 0.9316813
## MQ-DL -2503.00313 -5538.67493 532.66867 0.2595946
## NK-DL -1108.17170 -5303.02666 3086.68327 0.9999688
## OH-DL -1708.92367 -5097.37740 1679.53007 0.9472489
## OO-DL -509.33236 -3241.33450 2222.66978 0.9999998
## UA-DL 456.36768 -2911.65714 3824.39249 1.0000000
## WN-DL 3437.65384 17.11313 6858.19456 0.0472967
## YV-DL -1852.70369 -5095.93929 1390.53192 0.8606206
## YX-DL -1514.17517 -4857.67322 1829.32288 0.9803381
## F9-EV -1132.97047 -5413.79600 3147.85507 0.9999680
## G4-EV -1394.64764 -5488.85533 2699.56005 0.9991575
## HA-EV -1337.00102 -8040.54708 5366.54504 0.9999994
## MQ-EV -625.11387 -4619.07092 3368.84318 1.0000000
## NK-EV 769.71756 -4163.15156 5702.58669 1.0000000
## OH-EV 168.96559 -4099.29029 4437.22148 1.0000000
## OO-EV 1368.55690 -2399.76173 5136.87553 0.9981858
## UA-EV 2334.25694 -1917.79913 6586.31300 0.8956172
## WN-EV 5315.54310 1021.76989 9609.31632 0.0022560
## YV-EV 25.18558 -4128.72425 4179.09540 1.0000000
## YX-EV 363.71409 -3868.94097 4596.36915 1.0000000
## G4-F9 -261.67717 -3701.67977 3178.32542 1.0000000
## HA-F9 -204.03055 -6529.26660 6121.20549 1.0000000
## MQ-F9 507.85660 -2812.19974 3827.91294 1.0000000
## NK-F9 1902.68803 -2502.34080 6307.71686 0.9877488
## OH-F9 1301.93606 -2343.48429 4947.35641 0.9985111
## OO-F9 2501.52737 -543.35465 5546.40939 0.2655058
## UA-F9 3467.22741 -159.21189 7093.66671 0.0798643
## WN-F9 6448.51357 2773.24901 10123.77814 0.0000002
## YV-F9 1158.15605 -2352.69115 4669.00324 0.9994325
## YX-F9 1496.68456 -2106.98719 5100.35631 0.9918586
## HA-G4 57.64662 -6142.81146 6258.10470 1.0000000
## MQ-G4 769.53377 -2306.15208 3845.21962 0.9999852
## NK-G4 2164.36521 -2059.53682 6388.26723 0.9396591
## OH-G4 1563.61324 -1860.73472 4987.96119 0.9787013
## OO-G4 2763.20454 -13.19166 5539.60074 0.0526885
## UA-G4 3728.90458 324.77013 7133.03903 0.0160858
## WN-G4 6710.19075 3254.08905 10166.29244 0.0000000
## YV-G4 1419.83322 -1860.88582 4700.55225 0.9875014
## YX-G4 1758.36173 -1621.50800 5138.23147 0.9315778
## MQ-HA 711.88715 -5422.83669 6846.61099 1.0000000
## NK-HA 2106.71858 -4676.81601 8890.25317 0.9997348
## OH-HA 1505.96661 -4810.76926 7822.70249 0.9999925
## OO-HA 2705.55792 -3284.71495 8695.83079 0.9808465
## UA-HA 3671.25796 -2634.54290 9977.05882 0.8411499
## WN-HA 6652.54412 318.53811 12986.55014 0.0280336
## YV-HA 1362.18660 -4877.85431 7602.22751 0.9999978
## YX-HA 1700.71511 -4592.01979 7993.45002 0.9999575
## NK-MQ 1394.83143 -2731.97176 5521.63463 0.9992341
## OH-MQ 794.07946 -2509.75394 4097.91287 0.9999916
## OO-MQ 1993.67077 -632.64455 4619.98609 0.4067431
## UA-MQ 2959.37081 -323.50714 6242.24876 0.1359607
## WN-MQ 5940.65697 2603.92276 9277.39119 0.0000001
## YV-MQ 650.29945 -2504.42299 3805.02188 0.9999991
## YX-MQ 988.82796 -2268.88223 4246.53815 0.9998024
## OH-NK -600.75197 -4993.56656 3792.06262 1.0000000
## OO-NK 598.83933 -3310.00155 4507.68022 1.0000000
## UA-NK 1564.53937 -2812.53642 5941.61517 0.9984964
## WN-NK 4545.82554 128.21305 8963.43803 0.0359971
## YV-NK -744.53199 -5026.32817 3537.26420 0.9999999
## YX-NK -406.00347 -4764.23483 3952.22789 1.0000000
## OO-OH 1199.59131 -1827.59346 4226.77607 0.9951009
## UA-OH 2165.29135 -1446.30152 5776.88422 0.8073991
## WN-OH 5146.57751 1485.96135 8807.19367 0.0001540
## YV-OH -143.78002 -3639.28986 3351.72983 1.0000000
## YX-OH 194.74850 -3393.98263 3783.47963 1.0000000
## UA-OO 965.70004 -2038.60022 3970.00030 0.9995895
## WN-OO 3946.98621 883.92766 7010.04475 0.0010460
## YV-OO -1343.37132 -4207.07628 1520.33363 0.9724616
## YX-OO -1004.84281 -3981.62095 1971.93533 0.9992460
## WN-UA 2981.28616 -660.42815 6623.00048 0.2713443
## YV-UA -2309.07136 -5784.78158 1166.63886 0.6551960
## YX-UA -1970.54285 -5539.99148 1598.90579 0.8911420
## YV-WN -5290.35753 -8816.98044 -1763.73462 0.0000291
## YX-WN -4951.82901 -8570.87187 -1332.78616 0.0002905
## YX-YV 338.52852 -3113.42015 3790.47718 1.0000000TukeyHSD(anovar1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = arr_delay_h ~ carrier, data = alds1)
##
## $carrier
## diff lwr upr p adj
## AA-9E 67.4400961 10.5609811 124.319211 0.0048042
## AS-9E 8.7940214 -53.5944751 71.182518 1.0000000
## B6-9E 88.2746927 22.1852134 154.364172 0.0004956
## DL-9E 39.4235022 -14.1806685 93.027673 0.4671532
## EV-9E 8.1253479 -60.7559011 77.006597 1.0000000
## F9-9E -10.7574933 -68.8563947 47.341408 0.9999998
## G4-9E -15.1187795 -69.3528550 39.115296 0.9999355
## HA-9E -14.1580025 -117.9256369 89.609632 1.0000000
## MQ-9E -2.2932166 -54.4094385 49.823005 1.0000000
## NK-9E 20.9539739 -50.0692362 91.977184 0.9998635
## OH-9E 10.9414411 -46.9000029 68.782885 0.9999997
## OO-9E 30.9346295 -16.2838182 78.153077 0.6787383
## UA-9E 47.0296302 -10.4794270 104.538687 0.2731122
## WN-9E 96.7177329 38.3540323 155.081434 0.0000014
## YV-9E 8.5451075 -46.9357100 64.025925 1.0000000
## YX-9E 14.1872494 -42.9228733 71.297372 0.9999866
## AS-AA -58.6460747 -122.6605808 5.368431 0.1185265
## B6-AA 20.8345966 -46.7919645 88.461158 0.9997607
## DL-AA -28.0165939 -83.5047874 27.471600 0.9467335
## EV-AA -59.3147483 -129.6721143 11.042618 0.2247126
## F9-AA -78.1975894 -138.0391706 -18.356008 0.0007870
## G4-AA -82.5588756 -138.6558224 -26.461929 0.0000487
## HA-AA -81.5980986 -186.3514014 23.155204 0.3585781
## MQ-AA -69.7333128 -123.7854457 -15.681180 0.0010213
## NK-AA -46.4861222 -118.9418250 25.969581 0.7124853
## OH-AA -56.4986550 -116.0903083 3.092998 0.0866449
## OO-AA -36.5054666 -85.8523439 12.841411 0.4559362
## UA-AA -20.4104659 -79.6795486 38.858617 0.9990381
## WN-AA 29.2776368 -30.8210657 89.376339 0.9610622
## YV-AA -58.8949886 -116.1981614 -1.591816 0.0365189
## YX-AA -53.2528467 -112.1349205 5.629227 0.1323181
## B6-AS 79.4806713 7.1588728 151.802470 0.0153699
## DL-AS 30.6294808 -30.4935949 91.752557 0.9500803
## EV-AS -0.6686736 -75.5502449 74.212898 1.0000000
## F9-AS -19.5515147 -84.6522491 45.549220 0.9998278
## G4-AS -23.9128009 -85.5890379 37.763436 0.9961570
## HA-AS -22.9520239 -130.7961021 84.892054 0.9999985
## MQ-AS -11.0872381 -70.9096740 48.735198 0.9999998
## NK-AS 12.1599525 -64.6965348 89.016440 1.0000000
## OH-AS 2.1474197 -62.7236518 67.018491 1.0000000
## OO-AS 22.1406081 -33.4669910 77.748207 0.9948383
## UA-AS 38.2356088 -26.3392697 102.810487 0.8222852
## WN-AS 87.9237115 22.5865489 153.260874 0.0004212
## YV-AS -0.2489139 -63.0242625 62.526435 1.0000000
## YX-AS 5.3932280 -58.8266240 69.613080 1.0000000
## DL-B6 -48.8511905 -113.7474551 16.045074 0.4226933
## EV-B6 -80.1493449 -158.1412964 -2.157393 0.0365683
## F9-B6 -99.0321860 -167.6878514 -30.376521 0.0000818
## G4-B6 -103.3934722 -168.8110004 -37.975944 0.0000064
## HA-B6 -102.4326952 -212.4592374 7.593847 0.1027259
## MQ-B6 -90.5679094 -154.2406557 -26.895163 0.0001161
## NK-B6 -67.3207188 -147.2107333 12.569296 0.2253994
## OH-B6 -77.3332516 -145.7711847 -8.895319 0.0102781
## OO-B6 -57.3400632 -117.0702888 2.390162 0.0767305
## UA-B6 -41.2450625 -109.4023050 26.912180 0.7957526
## WN-B6 8.4430402 -60.4368523 77.322933 1.0000000
## YV-B6 -79.7295852 -146.1843756 -13.274795 0.0039347
## YX-B6 -74.0874433 -141.9084147 -6.266472 0.0167099
## EV-DL -31.2981544 -99.0353845 36.439076 0.9761373
## F9-DL -50.1809955 -106.9188862 6.556895 0.1585478
## G4-DL -54.5422817 -107.3157678 -1.768796 0.0341668
## HA-DL -53.5815047 -156.5932896 49.430280 0.9316813
## MQ-DL -41.7167189 -92.3112489 8.877811 0.2595946
## NK-DL -18.4695283 -88.3837777 51.444721 0.9999688
## OH-DL -28.4820612 -84.9562901 27.992168 0.9472489
## OO-DL -8.4888727 -54.0222417 37.044496 0.9999998
## UA-DL 7.6061279 -48.5276190 63.739875 1.0000000
## WN-DL 57.2942307 0.2852188 114.303243 0.0472967
## YV-DL -30.8783948 -84.9323216 23.175532 0.8606206
## YX-DL -25.2362528 -80.9612204 30.488715 0.9803381
## F9-EV -18.8828411 -90.2299334 52.464251 0.9999680
## G4-EV -23.2441274 -91.4809222 44.992667 0.9991575
## HA-EV -22.2833503 -134.0091180 89.442417 0.9999994
## MQ-EV -10.4185645 -76.9845153 56.147386 1.0000000
## NK-EV 12.8286261 -69.3858593 95.043111 1.0000000
## OH-EV 2.8160932 -68.3215049 73.953691 1.0000000
## OO-EV 22.8092817 -39.9960289 85.614592 0.9981858
## UA-EV 38.9042823 -31.9633188 109.771883 0.8956172
## WN-EV 88.5923851 17.0294982 160.155272 0.0022560
## YV-EV 0.4197596 -68.8120708 69.651590 1.0000000
## YX-EV 6.0619016 -64.4823494 76.606153 1.0000000
## G4-F9 -4.3612862 -61.6946628 52.972090 1.0000000
## HA-F9 -3.4005092 -108.8211100 102.020092 1.0000000
## MQ-F9 8.4642766 -46.8699957 63.798549 1.0000000
## NK-F9 31.7114672 -41.7056800 105.128614 0.9877488
## OH-F9 21.6989344 -39.0580715 82.455940 0.9985111
## OO-F9 41.6921228 -9.0559109 92.440156 0.2655058
## UA-F9 57.7871235 -2.6535316 118.227778 0.0798643
## WN-F9 107.4752262 46.2208168 168.729636 0.0000002
## YV-F9 19.3026008 -39.2115192 77.816721 0.9994325
## YX-F9 24.9447427 -35.1164531 85.005938 0.9918586
## HA-G4 0.9607770 -102.3801909 104.301745 1.0000000
## MQ-G4 12.8255629 -38.4358680 64.086994 0.9999852
## NK-G4 36.0727534 -34.3256137 106.471121 0.9396591
## OH-G4 26.0602206 -31.0122453 83.132686 0.9787013
## OO-G4 46.0534090 -0.2198610 92.326679 0.0526885
## UA-G4 62.1484097 5.4128355 118.883984 0.0160858
## WN-G4 111.8365124 54.2348175 169.438207 0.0000000
## YV-G4 23.6638870 -31.0147636 78.342538 0.9875014
## YX-G4 29.3060289 -27.0251333 85.637191 0.9315778
## MQ-HA 11.8647858 -90.3806116 114.110183 1.0000000
## NK-HA 35.1119764 -77.9469334 148.170886 0.9997348
## OH-HA 25.0994436 -80.1794876 130.378375 0.9999925
## OO-HA 45.0926320 -54.7452492 144.930513 0.9808465
## UA-HA 61.1876327 -43.9090484 166.284314 0.8411499
## WN-HA 110.8757354 5.3089686 216.442502 0.0280336
## YV-HA 22.7031100 -81.2975719 126.703792 0.9999978
## YX-HA 28.3452519 -76.5336632 133.224167 0.9999575
## NK-MQ 23.2471906 -45.5328627 92.027244 0.9992341
## OH-MQ 13.2346577 -41.8292323 68.298548 0.9999916
## OO-MQ 33.2278461 -10.5440758 76.999768 0.4067431
## UA-MQ 49.3228468 -5.3917856 104.037479 0.1359607
## WN-MQ 99.0109496 43.3987126 154.623186 0.0000001
## YV-MQ 10.8383241 -41.7403832 63.417031 0.9999991
## YX-MQ 16.4804660 -37.8147038 70.775636 0.9998024
## OH-NK -10.0125328 -83.2261094 63.201044 1.0000000
## OO-NK 9.9806556 -55.1666924 75.128004 1.0000000
## UA-NK 26.0756562 -46.8756070 99.026919 0.9984964
## WN-NK 75.7637590 2.1368841 149.390634 0.0359971
## YV-NK -12.4088665 -83.7721362 58.954403 0.9999999
## YX-NK -6.7667245 -79.4039138 65.870465 1.0000000
## OO-OH 19.9931884 -30.4598909 70.446268 0.9951009
## UA-OH 36.0881891 -24.1050254 96.281404 0.8073991
## WN-OH 85.7762918 24.7660225 146.786561 0.0001540
## YV-OH -2.3963336 -60.6548311 55.862164 1.0000000
## YX-OH 3.2458083 -56.5663772 63.057994 1.0000000
## UA-OO 16.0950007 -33.9766703 66.166672 0.9995895
## WN-OO 65.7831034 14.7321277 116.834079 0.0010460
## YV-OO -22.3895220 -70.1179380 25.338894 0.9724616
## YX-OO -16.7473801 -66.3603491 32.865589 0.9992460
## WN-UA 49.6881027 -11.0071358 110.383341 0.2713443
## YV-UA -38.4845227 -96.4130264 19.443981 0.6551960
## YX-UA -32.8423808 -92.3331914 26.648430 0.8911420
## YV-WN -88.1726254 -146.9496740 -29.395577 0.0000291
## YX-WN -82.5304835 -142.8478644 -22.213103 0.0002905
## YX-YV 5.6421419 -51.8903359 63.174620 1.0000000Findings and Conclusion
Overall the findings of the Tukey Test show that there are a lot of variance between the mean arrival delays among the 17 carriers. This means that there is an impact on delay times depending on the carrier. Carriers like G4, HA, and F9 may be more favorable as they have the lowest average arrival delays, being 17.677, 21.078, and 29.542 hours respectively. While ones like WN, B6, and AA may be less favorable as the have the highest average arrival delays, being 128.553, 120.11, and 99.275 hours respectively. However there could be many other factors at play like the 360 different airports included in this dataset or the month of the year as well as this dataset only has data for December in both 2019 and 2020.