Title: “Stats_Assignment 5”
Date: “12/9/2019”
Chapter 1 : Importing the dataset
All used Packages for this Assignment
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(corrplot)## corrplot 0.84 loadedImported data from FAA1 dataset
Dataset1 = read.csv("FAA1.csv",header = TRUE)Printing the first 10 rows of the dataset
head(Dataset1,10)## ï..aircraft duration no_pasg speed_ground speed_air height pitch
## 1 boeing 98.47909 53 107.91568 109.3284 27.41892 4.043515
## 2 boeing 125.73330 69 101.65559 102.8514 27.80472 4.117432
## 3 boeing 112.01700 61 71.05196 NA 18.58939 4.434043
## 4 boeing 196.82569 56 85.81333 NA 30.74460 3.884236
## 5 boeing 90.09538 70 59.88853 NA 32.39769 4.026096
## 6 boeing 137.59582 55 75.01434 NA 41.21496 4.203853
## 7 boeing 73.02379 54 54.42980 NA 24.03532 3.837646
## 8 boeing 52.90319 57 57.10166 NA 19.38884 4.643672
## 9 boeing 155.51862 61 85.44362 NA 35.37539 4.228728
## 10 boeing 176.86203 56 61.79671 NA 36.74882 4.184399
## distance
## 1 3369.8364
## 2 2987.8039
## 3 1144.9224
## 4 1664.2182
## 5 1050.2645
## 6 1627.0682
## 7 805.3040
## 8 573.6218
## 9 1698.9928
## 10 1137.7458Final Observation from this dataset
There are in total 800 observation in the 8 columns in this dataset
nrow(Dataset1)## [1] 800ncol(Dataset1)## [1] 8Chapter 2 : Data Cleaning
TO CHECK MISSING VALUES
colSums(is.na(Dataset1))## ï..aircraft duration no_pasg speed_ground speed_air
## 0 0 0 0 600
## height pitch distance
## 0 0 0OMITTING OR REMOVING MISSING VALUES
subset_dataset = na.omit(Dataset1) CHECKING AGAIN FOR MISSING VALUES
colSums(is.na(subset_dataset))## ï..aircraft duration no_pasg speed_ground speed_air
## 0 0 0 0 0
## height pitch distance
## 0 0 0TO REMOVE DUPLICATES
distinct(subset_dataset)## ï..aircraft duration no_pasg speed_ground speed_air height
## 1 boeing 98.47909 53 107.91568 109.32838 27.418924
## 2 boeing 125.73330 69 101.65559 102.85141 27.804716
## 3 boeing 180.61656 54 141.21864 141.72494 23.575935
## 4 boeing 72.28963 54 93.39176 92.86956 32.223489
## 5 boeing 187.59955 58 94.03641 96.19646 33.661226
## 6 boeing 233.80250 69 104.80843 103.86846 43.882732
## 7 boeing 163.90650 55 119.38046 120.44471 38.558536
## 8 boeing 126.54029 70 94.78123 91.14207 39.476299
## 9 boeing 112.90010 53 98.18041 99.13583 28.152991
## 10 boeing 86.82891 62 91.71454 92.87485 28.773729
## 11 boeing 140.23631 65 118.74200 119.40215 19.856192
## 12 boeing 130.46356 52 116.71343 117.65650 36.195527
## 13 boeing 93.52863 61 90.33190 90.11101 42.408190
## 14 boeing 202.54628 65 93.61520 95.32244 18.586453
## 15 boeing 194.67245 63 95.73291 97.18505 47.586622
## 16 boeing 79.99931 64 100.17254 100.26067 48.602825
## 17 boeing 121.10832 63 101.04739 101.72214 40.038554
## 18 boeing 116.98454 67 122.75656 123.88257 30.216568
## 19 boeing 154.06174 63 94.63081 93.93528 41.423375
## 20 boeing 107.12014 60 93.89934 93.18624 26.470877
## 21 boeing 161.89247 72 129.26492 128.41773 33.948999
## 22 boeing 130.48885 68 94.37662 92.15346 19.574381
## 23 boeing 130.42435 60 111.88831 110.20458 10.255225
## 24 boeing 83.51501 44 95.06873 96.08420 17.376660
## 25 boeing 180.14354 65 91.00027 91.58605 17.641254
## 26 boeing 218.02088 61 98.20617 96.03340 24.531204
## 27 boeing 133.55693 72 94.27133 92.87774 33.046424
## 28 boeing 156.80569 47 95.32258 94.21516 30.270100
## 29 boeing 205.87361 62 113.99631 110.65992 34.443425
## 30 boeing 125.22795 58 99.16859 96.92801 36.694347
## 31 boeing 153.78655 58 109.53851 109.38230 12.990661
## 32 boeing 108.34193 58 108.13311 108.50090 42.952396
## 33 boeing 163.08537 61 106.67585 104.44716 18.847638
## 34 boeing 151.87842 57 91.50821 90.50332 17.087228
## 35 boeing 131.74744 61 105.06368 107.44169 25.443920
## 36 boeing 124.78661 68 106.12024 104.13468 42.895160
## 37 boeing 99.15687 59 93.25356 93.47108 25.763979
## 38 boeing 150.30400 60 95.82178 96.55238 27.403567
## 39 boeing 95.29773 52 92.76937 91.07250 35.463228
## 40 boeing 150.39249 46 112.10344 111.43507 19.715772
## 41 boeing 209.10635 58 124.56986 125.98691 40.101122
## 42 boeing 65.69551 64 92.64536 94.73526 23.529869
## 43 boeing 127.99133 59 114.29273 113.86394 25.468139
## 44 boeing 100.97740 57 91.22413 90.00286 45.024392
## 45 boeing 78.45566 70 107.98596 109.45813 19.703121
## 46 boeing 217.92742 59 96.37331 97.10336 29.911516
## 47 boeing 114.76272 49 107.13152 107.51990 25.200025
## 48 boeing 244.15893 54 102.86234 104.43771 27.615830
## 49 boeing 113.36296 56 113.96403 113.74609 44.735463
## 50 boeing 119.92455 64 136.65916 136.42342 44.286109
## 51 boeing 197.17730 58 113.88914 113.44548 33.455376
## 52 boeing 120.82453 57 102.05412 101.33561 20.834550
## 53 boeing 147.75443 46 110.14590 110.60412 25.817053
## 54 boeing 287.00252 53 91.39993 93.34317 26.329235
## 55 boeing 139.41387 61 93.27329 93.49604 21.128504
## 56 boeing 211.08978 54 89.95089 91.11934 33.658158
## 57 boeing 154.99002 66 108.99642 108.36091 15.538766
## 58 boeing 128.93811 79 106.93389 108.42651 30.457709
## 59 boeing 197.54635 68 126.66918 127.96414 23.764231
## 60 boeing 232.79386 56 123.95687 122.18668 26.367547
## 61 boeing 154.19447 66 107.05909 108.17747 27.407951
## 62 boeing 95.46378 57 90.82324 92.91770 40.490463
## 63 boeing 104.86600 59 90.20736 90.79071 28.275040
## 64 boeing 107.33618 64 99.02079 97.33032 32.890789
## 65 boeing 182.20102 61 104.29513 103.09644 32.659534
## 66 boeing 190.94950 53 100.58975 98.33931 18.835041
## 67 boeing 272.03906 59 118.92272 120.39704 15.049349
## 68 boeing 206.16697 58 104.66394 104.70414 38.214350
## 69 boeing 130.21785 56 93.54933 90.76800 16.292894
## 70 boeing 222.28989 63 101.52331 101.11892 27.158311
## 71 boeing 159.85144 61 107.30146 105.91314 15.326207
## 72 boeing 260.03402 46 91.65670 93.05483 44.601461
## 73 boeing 169.84419 59 91.78849 91.27691 31.441321
## 74 boeing 189.66294 46 105.68599 107.65427 36.485676
## 75 boeing 277.17601 52 119.65394 120.19232 25.182763
## 76 boeing 205.92345 59 92.09829 92.16903 44.697092
## 77 boeing 107.73178 64 93.74599 94.40577 29.612945
## 78 boeing 202.68866 69 90.94389 90.53335 39.461285
## 79 boeing 96.94122 70 94.03049 94.52062 23.594477
## 80 boeing 164.23895 59 113.02952 113.17469 38.348270
## 81 boeing 149.22418 60 99.47419 101.09512 19.852904
## 82 boeing 142.13800 61 105.10045 103.35311 28.633774
## 83 boeing 175.29995 62 89.53340 90.47672 39.563714
## 84 boeing 127.22258 59 107.61448 107.87262 31.388204
## 85 boeing 124.48006 60 114.48072 114.69029 45.077666
## 86 boeing 109.45172 66 117.64059 112.26495 35.910036
## 87 boeing 233.42754 65 92.48929 94.52436 20.912577
## 88 boeing 169.83635 63 93.98511 96.26242 29.633599
## 89 boeing 106.51409 64 92.88591 92.64579 20.386141
## 90 boeing 79.70586 75 106.74612 106.73318 18.346202
## 91 boeing 152.57631 60 101.44035 101.69323 23.452540
## 92 boeing 174.60690 48 100.27343 98.71605 25.320302
## 93 boeing 155.00793 43 92.13091 93.67582 35.186989
## 94 boeing 14.76421 59 108.29169 109.32758 46.930874
## 95 boeing 136.95960 57 95.22782 94.20363 22.603042
## 96 boeing 154.52460 67 129.30718 127.59332 23.978497
## 97 boeing 89.35808 55 111.20255 110.68408 41.852413
## 98 boeing 195.28768 52 111.12793 109.82162 28.883346
## 99 boeing 75.01276 60 99.37869 99.94451 33.945667
## 100 boeing 204.95501 64 95.67754 99.11260 41.444766
## 101 boeing 167.61572 56 110.99011 109.75179 58.081790
## 102 boeing 166.10453 48 116.59249 118.01142 13.263240
## 103 boeing 102.41984 66 104.03291 102.84676 27.833615
## 104 boeing 79.27589 55 90.25381 90.36740 40.279043
## 105 boeing 197.87679 59 88.68758 90.73047 38.027849
## 106 boeing 176.25513 60 93.06721 94.97020 35.551604
## 107 boeing 126.04012 57 105.71922 105.10448 37.325700
## 108 boeing 99.19386 60 119.67749 123.04933 27.558025
## 109 boeing 72.50494 60 93.14475 91.58630 28.311008
## 110 boeing 163.60834 60 97.50272 99.43391 34.082267
## 111 boeing 128.88757 63 102.54307 104.62440 28.788569
## 112 boeing 87.73641 67 92.40024 92.42514 34.107757
## 113 boeing 224.74613 59 91.22207 92.75258 31.363682
## 114 boeing 121.93412 52 99.85309 100.66156 17.884148
## 115 boeing 193.72629 61 91.51151 93.20778 29.836885
## 116 boeing 31.39101 51 98.21980 99.05751 52.473141
## 117 boeing 92.45029 49 101.54350 101.84980 18.169394
## 118 boeing 106.10805 69 97.17487 98.63772 38.441153
## 119 boeing 63.32952 52 132.78468 132.91146 18.177030
## 120 boeing 99.68150 61 121.83714 120.95341 33.184597
## 121 boeing 212.29018 46 89.53371 90.62618 35.494743
## 122 boeing 153.83445 61 126.83928 126.11865 20.547834
## 123 airbus 132.46942 80 100.01055 100.89168 41.033011
## 124 airbus 93.95293 58 96.87869 98.08588 29.178095
## 125 airbus 99.14806 63 97.09691 96.91374 33.144246
## 126 airbus 112.87150 60 104.45540 103.67154 23.783587
## 127 airbus 148.49500 53 99.87452 98.72406 39.520426
## 128 airbus 217.12308 66 94.81426 97.63134 33.058366
## 129 airbus 131.73110 60 131.03518 131.33795 28.277966
## 130 airbus 160.39282 55 103.27582 105.18710 54.198540
## 131 airbus 122.73330 68 94.35155 97.56674 45.457866
## 132 airbus 123.30242 41 97.56820 96.97844 38.409193
## 133 airbus 45.50278 58 107.28767 105.68512 21.833564
## 134 airbus 16.89345 54 94.51105 95.93093 37.476967
## 135 airbus 113.37605 52 98.89157 100.52806 33.573007
## 136 airbus 247.49599 66 100.75477 100.83679 19.028711
## 137 airbus 193.12008 59 96.19656 98.11607 42.307732
## 138 airbus 249.89360 70 99.06971 98.87142 36.195843
## 139 airbus 108.72207 64 99.81738 100.30990 16.215281
## 140 airbus 145.17027 67 109.61058 107.31454 27.894764
## 141 airbus 223.95233 56 99.32504 99.39736 36.782683
## 142 airbus 123.18315 63 106.92923 106.76025 26.927860
## 143 airbus 77.46403 66 103.31639 98.12993 20.116604
## 144 airbus 184.61819 65 95.61697 95.01136 23.278395
## 145 airbus 168.79553 47 92.90759 95.76298 23.784897
## 146 airbus 140.41824 61 100.08904 100.51143 35.489405
## 147 airbus 157.16729 48 101.83850 103.63497 23.011622
## 148 airbus 55.91944 71 98.67330 98.93870 40.214749
## 149 airbus 190.79171 58 93.67228 95.09882 33.630979
## 150 airbus 207.39498 73 104.70011 107.78666 9.697216
## 151 airbus 151.67842 68 100.64167 102.11015 32.977374
## 152 airbus 214.72721 65 114.64797 114.16252 35.640282
## 153 airbus 199.33501 63 106.32542 105.11007 42.972776
## 154 airbus 199.91969 48 109.30116 109.55663 33.066514
## 155 airbus 139.31381 44 99.59684 99.16027 35.187030
## 156 airbus 84.37515 49 112.18972 112.19018 30.202559
## 157 airbus 177.25965 63 98.59362 97.48393 22.126151
## 158 airbus 192.58242 60 110.40812 110.03857 39.817281
## 159 airbus 173.77141 65 94.74180 95.28745 38.731918
## 160 airbus 158.53503 62 118.51898 117.65423 25.785069
## 161 airbus 145.77939 52 111.67392 111.33236 36.042803
## 162 airbus 194.68890 61 113.81884 113.57055 33.466774
## 163 airbus 157.97521 56 109.18165 108.23199 21.249105
## 164 airbus 201.18798 54 99.01740 98.22192 39.319647
## 165 airbus 121.91996 65 108.14498 106.70400 21.444463
## 166 airbus 95.84732 62 96.92270 99.83695 21.418855
## 167 airbus 149.33131 62 98.72752 98.14793 16.097139
## 168 airbus 191.42502 66 98.26399 99.96063 45.148967
## 169 airbus 140.67120 48 120.45476 118.67260 30.351507
## 170 airbus 220.30837 57 101.13020 101.80643 32.952280
## 171 airbus 137.58573 66 126.24430 127.93711 35.175701
## 172 airbus 71.56955 65 95.78227 96.31288 27.686330
## 173 airbus 170.11618 54 112.82932 108.21624 35.479682
## 174 airbus 163.67501 55 113.02073 114.87786 28.637640
## 175 airbus 85.68192 56 97.34540 96.17500 19.239272
## 176 airbus 110.58282 62 99.69191 97.49079 24.088743
## 177 airbus 150.60053 49 111.23656 109.85430 42.832497
## 178 airbus 239.99222 68 104.19586 104.96261 27.317381
## 179 airbus 140.45311 75 120.41895 118.48470 31.263446
## 180 airbus 123.07246 62 106.07949 105.31303 32.780367
## 181 airbus 130.85088 63 97.37950 96.72959 22.499126
## 182 airbus 186.09732 53 98.43812 98.56170 48.847023
## 183 airbus 162.71456 54 98.18733 96.51504 29.625193
## 184 airbus 154.23623 65 102.87894 100.66931 24.347516
## 185 airbus 156.01336 72 96.41315 96.12697 20.472816
## 186 airbus 122.33507 56 99.55913 98.87575 35.561255
## 187 airbus 124.69702 51 109.78499 110.56965 10.099991
## 188 airbus 182.64550 69 105.88725 107.52804 35.286189
## 189 airbus 161.23109 62 100.63019 100.87746 20.288655
## 190 airbus 164.83945 62 93.39940 95.57055 25.826906
## 191 airbus 127.30610 60 99.68960 99.45920 28.970864
## 192 airbus 157.57858 64 101.37497 101.35383 21.405236
## 193 airbus 130.31912 58 110.76702 112.53178 50.765799
## 194 airbus 175.51443 49 125.21230 125.13855 22.524779
## 195 airbus 141.88878 60 103.86693 105.55937 38.127302
## 196 airbus 147.71753 67 114.84661 113.17086 58.227800
## 197 airbus 220.05713 61 120.55794 118.28818 15.665658
## 198 airbus 98.50031 66 123.31053 124.39077 22.327176
## 199 airbus 264.59337 55 102.74650 102.41247 38.534021
## 200 airbus 149.36036 67 98.02641 99.42169 40.993053
## pitch distance
## 1 4.043515 3369.836
## 2 4.117432 2987.804
## 3 5.216802 6533.048
## 4 3.818276 2128.708
## 5 4.636185 2304.858
## 6 3.245098 3213.985
## 7 3.701449 4524.279
## 8 3.594936 2182.221
## 9 3.987471 2586.665
## 10 3.305888 2313.336
## 11 4.646266 4217.129
## 12 3.894352 4240.094
## 13 4.415128 2259.844
## 14 4.160108 1987.817
## 15 3.732112 2526.475
## 16 5.055818 2771.131
## 17 3.899869 2830.438
## 18 3.213703 4807.880
## 19 4.757853 2342.773
## 20 3.850436 2157.419
## 21 4.139951 5381.959
## 22 4.636507 1995.366
## 23 4.511726 3164.468
## 24 3.201149 2183.798
## 25 4.407987 1838.568
## 26 4.380465 2149.557
## 27 3.785795 2351.071
## 28 3.645135 2233.049
## 29 3.873845 3405.351
## 30 4.227166 2556.595
## 31 3.987957 3188.268
## 32 4.625702 3545.253
## 33 3.509450 2850.706
## 34 3.674610 1780.110
## 35 5.310678 3062.065
## 36 4.036322 2924.841
## 37 4.419372 2047.697
## 38 4.115288 2206.675
## 39 4.234651 1960.400
## 40 4.504393 3479.133
## 41 4.648428 5147.410
## 42 5.256831 2022.732
## 43 5.138243 3668.888
## 44 4.103694 2187.235
## 45 4.762115 3184.326
## 46 4.552148 2217.831
## 47 4.679503 3011.380
## 48 4.675239 2792.971
## 49 3.937906 4017.785
## 50 4.169404 6309.946
## 51 4.233058 3785.654
## 52 3.985369 2498.817
## 53 4.725850 3435.392
## 54 4.658435 2020.273
## 55 4.155737 2191.327
## 56 4.285029 2110.291
## 57 4.548028 3097.065
## 58 4.842149 3203.319
## 59 2.993151 5031.386
## 60 4.061951 4483.860
## 61 4.227419 3158.878
## 62 5.005951 2272.097
## 63 4.723400 1880.295
## 64 4.377685 2405.908
## 65 3.872559 2689.160
## 66 4.488083 2164.522
## 67 4.106572 4279.567
## 68 5.051623 2930.209
## 69 3.461763 1852.001
## 70 3.360349 2849.457
## 71 4.250258 2652.802
## 72 3.692308 2482.656
## 73 4.553410 2051.219
## 74 3.548427 3447.362
## 75 4.934241 4292.168
## 76 4.234070 2339.384
## 77 4.300254 2101.768
## 78 3.125823 2191.100
## 79 4.152119 2135.786
## 80 3.276835 3913.229
## 81 3.302747 2560.761
## 82 3.454210 3152.597
## 83 4.114118 2196.473
## 84 4.552326 3133.348
## 85 4.334137 3873.096
## 86 4.058218 3741.068
## 87 3.940869 2104.090
## 88 4.470942 2444.529
## 89 4.117407 1977.117
## 90 4.807402 2785.855
## 91 3.653297 2740.802
## 92 4.161058 2503.700
## 93 3.690854 2370.007
## 94 4.809622 3645.611
## 95 4.132198 2270.054
## 96 5.154699 5058.470
## 97 3.861643 3698.126
## 98 4.766635 3439.725
## 99 4.396186 2578.238
## 100 4.777534 2718.430
## 101 4.495264 3723.055
## 102 3.133959 4129.042
## 103 4.860966 2637.532
## 104 3.946045 2001.054
## 105 5.118423 2073.155
## 106 5.117117 2238.360
## 107 4.060051 2984.024
## 108 3.640565 4455.648
## 109 4.729996 2072.907
## 110 4.737542 2652.956
## 111 4.704166 2774.454
## 112 4.335353 2144.585
## 113 4.254654 2174.699
## 114 4.638566 2478.609
## 115 4.070322 2186.622
## 116 4.162337 2808.315
## 117 4.822683 2573.053
## 118 3.577309 2628.536
## 119 4.110664 5343.201
## 120 3.867476 4427.671
## 121 4.001038 2148.108
## 122 4.334558 4736.605
## 123 4.297502 2554.833
## 124 3.967524 2008.221
## 125 3.516298 2060.169
## 126 3.902655 2488.998
## 127 3.904121 2404.743
## 128 3.823555 2017.601
## 129 3.660194 4896.295
## 130 3.952123 2837.081
## 131 4.211726 2376.801
## 132 3.532272 2167.758
## 133 3.399819 2542.336
## 134 4.173322 2162.927
## 135 3.503461 2169.673
## 136 3.167978 2123.147
## 137 3.613914 2330.988
## 138 4.491129 2268.783
## 139 4.314092 2080.220
## 140 4.215413 2781.726
## 141 3.180071 2059.538
## 142 3.203514 2770.430
## 143 4.333835 2167.273
## 144 4.565551 1763.771
## 145 3.908674 1955.303
## 146 3.527174 2369.084
## 147 4.942973 2524.568
## 148 3.879809 2189.996
## 149 3.459215 2048.494
## 150 3.172457 2340.583
## 151 4.483192 2454.203
## 152 3.800255 3541.578
## 153 3.626330 2762.476
## 154 4.035050 3177.189
## 155 3.840267 2116.081
## 156 4.753384 3313.067
## 157 3.431559 1899.708
## 158 2.826184 2919.579
## 159 3.158703 1882.941
## 160 3.523655 3469.147
## 161 3.514983 3311.837
## 162 3.631032 3296.365
## 163 3.262348 2810.696
## 164 4.393525 2481.258
## 165 3.355367 2435.825
## 166 3.844892 2093.997
## 167 4.318246 1885.746
## 168 4.393900 2640.379
## 169 4.371072 3891.472
## 170 4.159192 2461.988
## 171 2.701924 4795.636
## 172 3.417140 1740.902
## 173 4.030304 2808.991
## 174 4.754211 3559.758
## 175 3.319058 1790.239
## 176 3.872403 1783.813
## 177 3.919742 3156.526
## 178 3.837824 2606.250
## 179 2.796731 3470.584
## 180 3.362904 2493.198
## 181 3.810677 2092.856
## 182 3.312405 2299.952
## 183 3.053515 1975.111
## 184 2.935757 2119.314
## 185 4.304832 1816.594
## 186 3.894868 2283.195
## 187 4.465461 3054.412
## 188 3.389987 2948.810
## 189 2.861640 1990.853
## 190 3.103442 1868.165
## 191 4.107103 2032.774
## 192 3.239965 2028.968
## 193 3.384856 3437.657
## 194 4.365772 4254.933
## 195 4.239918 2711.464
## 196 3.672759 3665.804
## 197 4.111265 3499.734
## 198 4.276710 4295.901
## 199 4.169718 2623.651
## 200 4.726821 2440.381Removing abnormal values from the dataset
nrow(subset_dataset)## [1] 200subset_dataset = filter(subset_dataset,duration > 40)
nrow(subset_dataset)## [1] 197subset_dataset = filter(subset_dataset,speed_ground > 30 & speed_ground <140)
nrow(subset_dataset)## [1] 196subset_dataset = filter(subset_dataset,speed_air>30 & speed_air <140)
nrow(subset_dataset)## [1] 196subset_dataset = filter(subset_dataset,height >= 6)
nrow(subset_dataset)## [1] 196subset_dataset = filter(subset_dataset,distance < 6000)
nrow(subset_dataset)## [1] 195To check further for any negative values
sum(subset_dataset<0) ## Warning in Ops.factor(left, right): '<' not meaningful for factors## [1] NAsubset_dataset## ï..aircraft duration no_pasg speed_ground speed_air height
## 1 boeing 98.47909 53 107.91568 109.32838 27.418924
## 2 boeing 125.73330 69 101.65559 102.85141 27.804716
## 3 boeing 72.28963 54 93.39176 92.86956 32.223489
## 4 boeing 187.59955 58 94.03641 96.19646 33.661226
## 5 boeing 233.80250 69 104.80843 103.86846 43.882732
## 6 boeing 163.90650 55 119.38046 120.44471 38.558536
## 7 boeing 126.54029 70 94.78123 91.14207 39.476299
## 8 boeing 112.90010 53 98.18041 99.13583 28.152991
## 9 boeing 86.82891 62 91.71454 92.87485 28.773729
## 10 boeing 140.23631 65 118.74200 119.40215 19.856192
## 11 boeing 130.46356 52 116.71343 117.65650 36.195527
## 12 boeing 93.52863 61 90.33190 90.11101 42.408190
## 13 boeing 202.54628 65 93.61520 95.32244 18.586453
## 14 boeing 194.67245 63 95.73291 97.18505 47.586622
## 15 boeing 79.99931 64 100.17254 100.26067 48.602825
## 16 boeing 121.10832 63 101.04739 101.72214 40.038554
## 17 boeing 116.98454 67 122.75656 123.88257 30.216568
## 18 boeing 154.06174 63 94.63081 93.93528 41.423375
## 19 boeing 107.12014 60 93.89934 93.18624 26.470877
## 20 boeing 161.89247 72 129.26492 128.41773 33.948999
## 21 boeing 130.48885 68 94.37662 92.15346 19.574381
## 22 boeing 130.42435 60 111.88831 110.20458 10.255225
## 23 boeing 83.51501 44 95.06873 96.08420 17.376660
## 24 boeing 180.14354 65 91.00027 91.58605 17.641254
## 25 boeing 218.02088 61 98.20617 96.03340 24.531204
## 26 boeing 133.55693 72 94.27133 92.87774 33.046424
## 27 boeing 156.80569 47 95.32258 94.21516 30.270100
## 28 boeing 205.87361 62 113.99631 110.65992 34.443425
## 29 boeing 125.22795 58 99.16859 96.92801 36.694347
## 30 boeing 153.78655 58 109.53851 109.38230 12.990661
## 31 boeing 108.34193 58 108.13311 108.50090 42.952396
## 32 boeing 163.08537 61 106.67585 104.44716 18.847638
## 33 boeing 151.87842 57 91.50821 90.50332 17.087228
## 34 boeing 131.74744 61 105.06368 107.44169 25.443920
## 35 boeing 124.78661 68 106.12024 104.13468 42.895160
## 36 boeing 99.15687 59 93.25356 93.47108 25.763979
## 37 boeing 150.30400 60 95.82178 96.55238 27.403567
## 38 boeing 95.29773 52 92.76937 91.07250 35.463228
## 39 boeing 150.39249 46 112.10344 111.43507 19.715772
## 40 boeing 209.10635 58 124.56986 125.98691 40.101122
## 41 boeing 65.69551 64 92.64536 94.73526 23.529869
## 42 boeing 127.99133 59 114.29273 113.86394 25.468139
## 43 boeing 100.97740 57 91.22413 90.00286 45.024392
## 44 boeing 78.45566 70 107.98596 109.45813 19.703121
## 45 boeing 217.92742 59 96.37331 97.10336 29.911516
## 46 boeing 114.76272 49 107.13152 107.51990 25.200025
## 47 boeing 244.15893 54 102.86234 104.43771 27.615830
## 48 boeing 113.36296 56 113.96403 113.74609 44.735463
## 49 boeing 197.17730 58 113.88914 113.44548 33.455376
## 50 boeing 120.82453 57 102.05412 101.33561 20.834550
## 51 boeing 147.75443 46 110.14590 110.60412 25.817053
## 52 boeing 287.00252 53 91.39993 93.34317 26.329235
## 53 boeing 139.41387 61 93.27329 93.49604 21.128504
## 54 boeing 211.08978 54 89.95089 91.11934 33.658158
## 55 boeing 154.99002 66 108.99642 108.36091 15.538766
## 56 boeing 128.93811 79 106.93389 108.42651 30.457709
## 57 boeing 197.54635 68 126.66918 127.96414 23.764231
## 58 boeing 232.79386 56 123.95687 122.18668 26.367547
## 59 boeing 154.19447 66 107.05909 108.17747 27.407951
## 60 boeing 95.46378 57 90.82324 92.91770 40.490463
## 61 boeing 104.86600 59 90.20736 90.79071 28.275040
## 62 boeing 107.33618 64 99.02079 97.33032 32.890789
## 63 boeing 182.20102 61 104.29513 103.09644 32.659534
## 64 boeing 190.94950 53 100.58975 98.33931 18.835041
## 65 boeing 272.03906 59 118.92272 120.39704 15.049349
## 66 boeing 206.16697 58 104.66394 104.70414 38.214350
## 67 boeing 130.21785 56 93.54933 90.76800 16.292894
## 68 boeing 222.28989 63 101.52331 101.11892 27.158311
## 69 boeing 159.85144 61 107.30146 105.91314 15.326207
## 70 boeing 260.03402 46 91.65670 93.05483 44.601461
## 71 boeing 169.84419 59 91.78849 91.27691 31.441321
## 72 boeing 189.66294 46 105.68599 107.65427 36.485676
## 73 boeing 277.17601 52 119.65394 120.19232 25.182763
## 74 boeing 205.92345 59 92.09829 92.16903 44.697092
## 75 boeing 107.73178 64 93.74599 94.40577 29.612945
## 76 boeing 202.68866 69 90.94389 90.53335 39.461285
## 77 boeing 96.94122 70 94.03049 94.52062 23.594477
## 78 boeing 164.23895 59 113.02952 113.17469 38.348270
## 79 boeing 149.22418 60 99.47419 101.09512 19.852904
## 80 boeing 142.13800 61 105.10045 103.35311 28.633774
## 81 boeing 175.29995 62 89.53340 90.47672 39.563714
## 82 boeing 127.22258 59 107.61448 107.87262 31.388204
## 83 boeing 124.48006 60 114.48072 114.69029 45.077666
## 84 boeing 109.45172 66 117.64059 112.26495 35.910036
## 85 boeing 233.42754 65 92.48929 94.52436 20.912577
## 86 boeing 169.83635 63 93.98511 96.26242 29.633599
## 87 boeing 106.51409 64 92.88591 92.64579 20.386141
## 88 boeing 79.70586 75 106.74612 106.73318 18.346202
## 89 boeing 152.57631 60 101.44035 101.69323 23.452540
## 90 boeing 174.60690 48 100.27343 98.71605 25.320302
## 91 boeing 155.00793 43 92.13091 93.67582 35.186989
## 92 boeing 136.95960 57 95.22782 94.20363 22.603042
## 93 boeing 154.52460 67 129.30718 127.59332 23.978497
## 94 boeing 89.35808 55 111.20255 110.68408 41.852413
## 95 boeing 195.28768 52 111.12793 109.82162 28.883346
## 96 boeing 75.01276 60 99.37869 99.94451 33.945667
## 97 boeing 204.95501 64 95.67754 99.11260 41.444766
## 98 boeing 167.61572 56 110.99011 109.75179 58.081790
## 99 boeing 166.10453 48 116.59249 118.01142 13.263240
## 100 boeing 102.41984 66 104.03291 102.84676 27.833615
## 101 boeing 79.27589 55 90.25381 90.36740 40.279043
## 102 boeing 197.87679 59 88.68758 90.73047 38.027849
## 103 boeing 176.25513 60 93.06721 94.97020 35.551604
## 104 boeing 126.04012 57 105.71922 105.10448 37.325700
## 105 boeing 99.19386 60 119.67749 123.04933 27.558025
## 106 boeing 72.50494 60 93.14475 91.58630 28.311008
## 107 boeing 163.60834 60 97.50272 99.43391 34.082267
## 108 boeing 128.88757 63 102.54307 104.62440 28.788569
## 109 boeing 87.73641 67 92.40024 92.42514 34.107757
## 110 boeing 224.74613 59 91.22207 92.75258 31.363682
## 111 boeing 121.93412 52 99.85309 100.66156 17.884148
## 112 boeing 193.72629 61 91.51151 93.20778 29.836885
## 113 boeing 92.45029 49 101.54350 101.84980 18.169394
## 114 boeing 106.10805 69 97.17487 98.63772 38.441153
## 115 boeing 63.32952 52 132.78468 132.91146 18.177030
## 116 boeing 99.68150 61 121.83714 120.95341 33.184597
## 117 boeing 212.29018 46 89.53371 90.62618 35.494743
## 118 boeing 153.83445 61 126.83928 126.11865 20.547834
## 119 airbus 132.46942 80 100.01055 100.89168 41.033011
## 120 airbus 93.95293 58 96.87869 98.08588 29.178095
## 121 airbus 99.14806 63 97.09691 96.91374 33.144246
## 122 airbus 112.87150 60 104.45540 103.67154 23.783587
## 123 airbus 148.49500 53 99.87452 98.72406 39.520426
## 124 airbus 217.12308 66 94.81426 97.63134 33.058366
## 125 airbus 131.73110 60 131.03518 131.33795 28.277966
## 126 airbus 160.39282 55 103.27582 105.18710 54.198540
## 127 airbus 122.73330 68 94.35155 97.56674 45.457866
## 128 airbus 123.30242 41 97.56820 96.97844 38.409193
## 129 airbus 45.50278 58 107.28767 105.68512 21.833564
## 130 airbus 113.37605 52 98.89157 100.52806 33.573007
## 131 airbus 247.49599 66 100.75477 100.83679 19.028711
## 132 airbus 193.12008 59 96.19656 98.11607 42.307732
## 133 airbus 249.89360 70 99.06971 98.87142 36.195843
## 134 airbus 108.72207 64 99.81738 100.30990 16.215281
## 135 airbus 145.17027 67 109.61058 107.31454 27.894764
## 136 airbus 223.95233 56 99.32504 99.39736 36.782683
## 137 airbus 123.18315 63 106.92923 106.76025 26.927860
## 138 airbus 77.46403 66 103.31639 98.12993 20.116604
## 139 airbus 184.61819 65 95.61697 95.01136 23.278395
## 140 airbus 168.79553 47 92.90759 95.76298 23.784897
## 141 airbus 140.41824 61 100.08904 100.51143 35.489405
## 142 airbus 157.16729 48 101.83850 103.63497 23.011622
## 143 airbus 55.91944 71 98.67330 98.93870 40.214749
## 144 airbus 190.79171 58 93.67228 95.09882 33.630979
## 145 airbus 207.39498 73 104.70011 107.78666 9.697216
## 146 airbus 151.67842 68 100.64167 102.11015 32.977374
## 147 airbus 214.72721 65 114.64797 114.16252 35.640282
## 148 airbus 199.33501 63 106.32542 105.11007 42.972776
## 149 airbus 199.91969 48 109.30116 109.55663 33.066514
## 150 airbus 139.31381 44 99.59684 99.16027 35.187030
## 151 airbus 84.37515 49 112.18972 112.19018 30.202559
## 152 airbus 177.25965 63 98.59362 97.48393 22.126151
## 153 airbus 192.58242 60 110.40812 110.03857 39.817281
## 154 airbus 173.77141 65 94.74180 95.28745 38.731918
## 155 airbus 158.53503 62 118.51898 117.65423 25.785069
## 156 airbus 145.77939 52 111.67392 111.33236 36.042803
## 157 airbus 194.68890 61 113.81884 113.57055 33.466774
## 158 airbus 157.97521 56 109.18165 108.23199 21.249105
## 159 airbus 201.18798 54 99.01740 98.22192 39.319647
## 160 airbus 121.91996 65 108.14498 106.70400 21.444463
## 161 airbus 95.84732 62 96.92270 99.83695 21.418855
## 162 airbus 149.33131 62 98.72752 98.14793 16.097139
## 163 airbus 191.42502 66 98.26399 99.96063 45.148967
## 164 airbus 140.67120 48 120.45476 118.67260 30.351507
## 165 airbus 220.30837 57 101.13020 101.80643 32.952280
## 166 airbus 137.58573 66 126.24430 127.93711 35.175701
## 167 airbus 71.56955 65 95.78227 96.31288 27.686330
## 168 airbus 170.11618 54 112.82932 108.21624 35.479682
## 169 airbus 163.67501 55 113.02073 114.87786 28.637640
## 170 airbus 85.68192 56 97.34540 96.17500 19.239272
## 171 airbus 110.58282 62 99.69191 97.49079 24.088743
## 172 airbus 150.60053 49 111.23656 109.85430 42.832497
## 173 airbus 239.99222 68 104.19586 104.96261 27.317381
## 174 airbus 140.45311 75 120.41895 118.48470 31.263446
## 175 airbus 123.07246 62 106.07949 105.31303 32.780367
## 176 airbus 130.85088 63 97.37950 96.72959 22.499126
## 177 airbus 186.09732 53 98.43812 98.56170 48.847023
## 178 airbus 162.71456 54 98.18733 96.51504 29.625193
## 179 airbus 154.23623 65 102.87894 100.66931 24.347516
## 180 airbus 156.01336 72 96.41315 96.12697 20.472816
## 181 airbus 122.33507 56 99.55913 98.87575 35.561255
## 182 airbus 124.69702 51 109.78499 110.56965 10.099991
## 183 airbus 182.64550 69 105.88725 107.52804 35.286189
## 184 airbus 161.23109 62 100.63019 100.87746 20.288655
## 185 airbus 164.83945 62 93.39940 95.57055 25.826906
## 186 airbus 127.30610 60 99.68960 99.45920 28.970864
## 187 airbus 157.57858 64 101.37497 101.35383 21.405236
## 188 airbus 130.31912 58 110.76702 112.53178 50.765799
## 189 airbus 175.51443 49 125.21230 125.13855 22.524779
## 190 airbus 141.88878 60 103.86693 105.55937 38.127302
## 191 airbus 147.71753 67 114.84661 113.17086 58.227800
## 192 airbus 220.05713 61 120.55794 118.28818 15.665658
## 193 airbus 98.50031 66 123.31053 124.39077 22.327176
## 194 airbus 264.59337 55 102.74650 102.41247 38.534021
## 195 airbus 149.36036 67 98.02641 99.42169 40.993053
## pitch distance
## 1 4.043515 3369.836
## 2 4.117432 2987.804
## 3 3.818276 2128.708
## 4 4.636185 2304.858
## 5 3.245098 3213.985
## 6 3.701449 4524.279
## 7 3.594936 2182.221
## 8 3.987471 2586.665
## 9 3.305888 2313.336
## 10 4.646266 4217.129
## 11 3.894352 4240.094
## 12 4.415128 2259.844
## 13 4.160108 1987.817
## 14 3.732112 2526.475
## 15 5.055818 2771.131
## 16 3.899869 2830.438
## 17 3.213703 4807.880
## 18 4.757853 2342.773
## 19 3.850436 2157.419
## 20 4.139951 5381.959
## 21 4.636507 1995.366
## 22 4.511726 3164.468
## 23 3.201149 2183.798
## 24 4.407987 1838.568
## 25 4.380465 2149.557
## 26 3.785795 2351.071
## 27 3.645135 2233.049
## 28 3.873845 3405.351
## 29 4.227166 2556.595
## 30 3.987957 3188.268
## 31 4.625702 3545.253
## 32 3.509450 2850.706
## 33 3.674610 1780.110
## 34 5.310678 3062.065
## 35 4.036322 2924.841
## 36 4.419372 2047.697
## 37 4.115288 2206.675
## 38 4.234651 1960.400
## 39 4.504393 3479.133
## 40 4.648428 5147.410
## 41 5.256831 2022.732
## 42 5.138243 3668.888
## 43 4.103694 2187.235
## 44 4.762115 3184.326
## 45 4.552148 2217.831
## 46 4.679503 3011.380
## 47 4.675239 2792.971
## 48 3.937906 4017.785
## 49 4.233058 3785.654
## 50 3.985369 2498.817
## 51 4.725850 3435.392
## 52 4.658435 2020.273
## 53 4.155737 2191.327
## 54 4.285029 2110.291
## 55 4.548028 3097.065
## 56 4.842149 3203.319
## 57 2.993151 5031.386
## 58 4.061951 4483.860
## 59 4.227419 3158.878
## 60 5.005951 2272.097
## 61 4.723400 1880.295
## 62 4.377685 2405.908
## 63 3.872559 2689.160
## 64 4.488083 2164.522
## 65 4.106572 4279.567
## 66 5.051623 2930.209
## 67 3.461763 1852.001
## 68 3.360349 2849.457
## 69 4.250258 2652.802
## 70 3.692308 2482.656
## 71 4.553410 2051.219
## 72 3.548427 3447.362
## 73 4.934241 4292.168
## 74 4.234070 2339.384
## 75 4.300254 2101.768
## 76 3.125823 2191.100
## 77 4.152119 2135.786
## 78 3.276835 3913.229
## 79 3.302747 2560.761
## 80 3.454210 3152.597
## 81 4.114118 2196.473
## 82 4.552326 3133.348
## 83 4.334137 3873.096
## 84 4.058218 3741.068
## 85 3.940869 2104.090
## 86 4.470942 2444.529
## 87 4.117407 1977.117
## 88 4.807402 2785.855
## 89 3.653297 2740.802
## 90 4.161058 2503.700
## 91 3.690854 2370.007
## 92 4.132198 2270.054
## 93 5.154699 5058.470
## 94 3.861643 3698.126
## 95 4.766635 3439.725
## 96 4.396186 2578.238
## 97 4.777534 2718.430
## 98 4.495264 3723.055
## 99 3.133959 4129.042
## 100 4.860966 2637.532
## 101 3.946045 2001.054
## 102 5.118423 2073.155
## 103 5.117117 2238.360
## 104 4.060051 2984.024
## 105 3.640565 4455.648
## 106 4.729996 2072.907
## 107 4.737542 2652.956
## 108 4.704166 2774.454
## 109 4.335353 2144.585
## 110 4.254654 2174.699
## 111 4.638566 2478.609
## 112 4.070322 2186.622
## 113 4.822683 2573.053
## 114 3.577309 2628.536
## 115 4.110664 5343.201
## 116 3.867476 4427.671
## 117 4.001038 2148.108
## 118 4.334558 4736.605
## 119 4.297502 2554.833
## 120 3.967524 2008.221
## 121 3.516298 2060.169
## 122 3.902655 2488.998
## 123 3.904121 2404.743
## 124 3.823555 2017.601
## 125 3.660194 4896.295
## 126 3.952123 2837.081
## 127 4.211726 2376.801
## 128 3.532272 2167.758
## 129 3.399819 2542.336
## 130 3.503461 2169.673
## 131 3.167978 2123.147
## 132 3.613914 2330.988
## 133 4.491129 2268.783
## 134 4.314092 2080.220
## 135 4.215413 2781.726
## 136 3.180071 2059.538
## 137 3.203514 2770.430
## 138 4.333835 2167.273
## 139 4.565551 1763.771
## 140 3.908674 1955.303
## 141 3.527174 2369.084
## 142 4.942973 2524.568
## 143 3.879809 2189.996
## 144 3.459215 2048.494
## 145 3.172457 2340.583
## 146 4.483192 2454.203
## 147 3.800255 3541.578
## 148 3.626330 2762.476
## 149 4.035050 3177.189
## 150 3.840267 2116.081
## 151 4.753384 3313.067
## 152 3.431559 1899.708
## 153 2.826184 2919.579
## 154 3.158703 1882.941
## 155 3.523655 3469.147
## 156 3.514983 3311.837
## 157 3.631032 3296.365
## 158 3.262348 2810.696
## 159 4.393525 2481.258
## 160 3.355367 2435.825
## 161 3.844892 2093.997
## 162 4.318246 1885.746
## 163 4.393900 2640.379
## 164 4.371072 3891.472
## 165 4.159192 2461.988
## 166 2.701924 4795.636
## 167 3.417140 1740.902
## 168 4.030304 2808.991
## 169 4.754211 3559.758
## 170 3.319058 1790.239
## 171 3.872403 1783.813
## 172 3.919742 3156.526
## 173 3.837824 2606.250
## 174 2.796731 3470.584
## 175 3.362904 2493.198
## 176 3.810677 2092.856
## 177 3.312405 2299.952
## 178 3.053515 1975.111
## 179 2.935757 2119.314
## 180 4.304832 1816.594
## 181 3.894868 2283.195
## 182 4.465461 3054.412
## 183 3.389987 2948.810
## 184 2.861640 1990.853
## 185 3.103442 1868.165
## 186 4.107103 2032.774
## 187 3.239965 2028.968
## 188 3.384856 3437.657
## 189 4.365772 4254.933
## 190 4.239918 2711.464
## 191 3.672759 3665.804
## 192 4.111265 3499.734
## 193 4.276710 4295.901
## 194 4.169718 2623.651
## 195 4.726821 2440.381Copying the subset to the old dataset
Dataset1 = subset_dataset
nrow(Dataset1)## [1] 195Observations:
There were around 600 missing values for speed_air
3 abnormal values for duration.
1 abnormal value for speed_ground.
1 abnormal value for height.
Chapter 3 : Data Visualization Using R
Plotting Distance against given predictor variables
- Distance vs Duration
ggplot(data = Dataset1, mapping = aes(x = duration, y = distance)) + geom_point()
- Distance vs no_pasg
ggplot(data = Dataset1, mapping = aes(x = no_pasg, y = distance)) + geom_point()
- Distance vs speed_ground
ggplot(data = Dataset1, mapping = aes(x = speed_ground, y = distance)) + geom_point()
- Distance vs speed_air
ggplot(data = Dataset1, mapping = aes(x = speed_air, y = distance)) + geom_point()
- Distance vs height
ggplot(data = Dataset1, mapping = aes(x = height, y = distance)) + geom_point()
- Distance vs pitch
ggplot(data = Dataset1, mapping = aes(x = pitch, y = distance)) + geom_point()
Resetting the column name of “i..aircraft” to “aircraft”
colnames(Dataset1)[1] <- "aircraft"- Distance vs aircraft
ggplot(data = Dataset1, mapping = aes(x = aircraft , y = distance)) + geom_point()
Plotting Correlation Matrix
correlation <- cor(Dataset1[,2:8])
round(correlation, 2)## duration no_pasg speed_ground speed_air height pitch distance
## duration 1.00 -0.07 0.02 0.04 0.07 -0.06 0.05
## no_pasg -0.07 1.00 0.00 0.00 -0.01 -0.04 -0.03
## speed_ground 0.02 0.00 1.00 0.99 -0.10 -0.06 0.93
## speed_air 0.04 0.00 0.99 1.00 -0.09 -0.05 0.94
## height 0.07 -0.01 -0.10 -0.09 1.00 -0.03 0.06
## pitch -0.06 -0.04 -0.06 -0.05 -0.03 1.00 0.03
## distance 0.05 -0.03 0.93 0.94 0.06 0.03 1.00Observations:
We can see a strong correlation between distance and speed_ground(0.93), distance and speed_air(0.94), speed_air and speed_ground(0.99)
Chapter 4: Model Fitting
creating a linear model
Model=lm(distance~aircraft+duration+no_pasg+speed_ground+speed_air+height+pitch,data = Dataset1)Getting Model Summary
summary(Model)##
## Call:
## lm(formula = distance ~ aircraft + duration + no_pasg + speed_ground +
## speed_air + height + pitch, data = Dataset1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -287.36 -85.69 11.25 83.50 359.85
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6229.6000 161.7147 -38.522 <2e-16 ***
## aircraftboeing 437.9428 21.2621 20.597 <2e-16 ***
## duration 0.1276 0.2039 0.626 0.532
## no_pasg -1.9812 1.3780 -1.438 0.152
## speed_ground -3.5464 6.4160 -0.553 0.581
## speed_air 85.5469 6.5221 13.117 <2e-16 ***
## height 13.6756 1.0386 13.168 <2e-16 ***
## pitch -13.4897 18.6077 -0.725 0.469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 134.5 on 187 degrees of freedom
## Multiple R-squared: 0.9747, Adjusted R-squared: 0.9738
## F-statistic: 1030 on 7 and 187 DF, p-value: < 2.2e-16Observations:
From the above summary we can see that aircraft, speed_air and height show a significant value and hence are considered for modelling. Therefore, Landing Distance = -6229.6 + 437.9428 * aircraftboeing + 85.5469 * speed_air + 13..6756 * height This model covers 97.38 % of the data which is inferred from R-Squared value and it is considering only boieng aircraft.
Chapter 5 : Modelling for each Aircraft
Splitting the aircraft type into boeing and airbus and building the model.
boeingdata = filter(Dataset1,aircraft== 'boeing')
nrow(boeingdata)## [1] 118model_boeing<-lm(distance~duration+no_pasg+speed_ground+speed_air+height+pitch,data = boeingdata)
summary(model_boeing)##
## Call:
## lm(formula = distance ~ duration + no_pasg + speed_ground + speed_air +
## height + pitch, data = boeingdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -300.52 -81.44 -0.90 79.13 338.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5517.0309 192.2178 -28.702 < 2e-16 ***
## duration 0.1884 0.2430 0.775 0.440
## no_pasg -0.5819 1.7843 -0.326 0.745
## speed_ground -2.0530 8.1325 -0.252 0.801
## speed_air 83.8793 8.2148 10.211 < 2e-16 ***
## height 13.8645 1.3093 10.589 < 2e-16 ***
## pitch -97.7307 23.0082 -4.248 4.51e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.7 on 111 degrees of freedom
## Multiple R-squared: 0.98, Adjusted R-squared: 0.9789
## F-statistic: 905 on 6 and 111 DF, p-value: < 2.2e-16airbusdata = filter(Dataset1,aircraft== 'airbus')
nrow(airbusdata)## [1] 77model_airbus<-lm(distance~duration+no_pasg+speed_ground+speed_air+height+pitch,data = airbusdata)
summary(model_airbus)##
## Call:
## lm(formula = distance ~ duration + no_pasg + speed_ground + speed_air +
## height + pitch, data = airbusdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -190.80 -77.22 1.91 81.02 318.28
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.715e+03 2.440e+02 -27.519 < 2e-16 ***
## duration -1.269e-04 2.994e-01 0.000 1.000
## no_pasg -2.087e+00 1.820e+00 -1.146 0.256
## speed_ground -9.355e+00 8.573e+00 -1.091 0.279
## speed_air 9.172e+01 8.848e+00 10.367 8.75e-16 ***
## height 1.329e+01 1.398e+00 9.512 3.07e-14 ***
## pitch 1.147e+02 2.577e+01 4.449 3.17e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 116.1 on 70 degrees of freedom
## Multiple R-squared: 0.9751, Adjusted R-squared: 0.973
## F-statistic: 457 on 6 and 70 DF, p-value: < 2.2e-16Observation:
- Boeing data covers 97.89 % of the data.
- It shows speed_air, height and pitch as significant variables with p-value less than 0.05.
- Airbus data covers 97.3% of the data.
- Airbus also shows speed_air, height and pitch as significant variables with p-value less than 0.05
- Thus, the overall make of an aircraft affects the landing distance.