Assignment 2
Jose Miguel Botero C, Karen Sofía Guerrero B, Juan José Gutierrez L, Juan Felipe Peña O, Marcela Mejía M
2022-06-07
DATASET: CEOSAL2
pacman::p_load(tidyverse,Lock5Data,ISLR,nycflights13,wooldridge,expss,hablar,rio,ggplot2)
wooldridge::ceosal2## salary age college grad comten ceoten sales profits mktval lsalary
## 1 1161 49 1 1 9 2 6200 966 23200 7.057037
## 2 600 43 1 1 10 10 283 48 1100 6.396930
## 3 379 51 1 1 9 3 169 40 1100 5.937536
## 4 651 55 1 0 22 22 1100 -54 1000 6.478509
## 5 497 44 1 1 8 6 351 28 387 6.208590
## 6 1067 64 1 1 7 7 19000 614 3900 6.972606
## 7 945 59 1 0 35 10 536 24 623 6.851185
## 8 1261 63 1 1 32 8 4800 191 2100 7.139660
## 9 503 47 1 1 4 4 610 7 454 6.220590
## 10 1094 64 1 1 39 5 2900 230 3900 6.997596
## 11 601 54 1 1 26 7 1200 34 533 6.398595
## 12 355 66 1 0 39 8 560 8 477 5.872118
## 13 1200 72 1 0 37 37 796 35 678 7.090077
## 14 697 51 1 0 25 1 8200 234 5700 6.546785
## 15 1041 63 1 1 21 11 4300 91 1400 6.947937
## 16 245 44 1 1 7 7 135 24 558 5.501258
## 17 817 68 1 0 38 4 1300 55 847 6.705639
## 18 1675 71 0 0 31 12 674 115 1200 7.423568
## 19 971 72 1 1 33 24 1400 69 609 6.878326
## 20 609 58 1 0 36 1 1100 69 880 6.411819
## 21 470 60 1 1 20 6 2300 210 2200 6.152733
## 22 867 59 1 0 36 14 884 81 1500 6.765039
## 23 752 54 1 0 32 4 1600 193 3200 6.622736
## 24 246 51 1 0 8 8 78 13 458 5.505332
## 25 825 56 1 1 4 4 10700 295 5900 6.715384
## 26 358 50 1 1 23 4 99 25 2300 5.880533
## 27 1162 58 1 0 24 6 3800 226 1800 7.057898
## 28 270 43 1 0 15 2 150 28 713 5.598422
## 29 829 56 1 0 14 8 2200 184 1500 6.720220
## 30 300 77 0 0 45 26 6900 483 4700 5.703783
## 31 1627 62 1 1 13 4 8300 596 9100 7.394493
## 32 1237 63 1 1 37 9 4600 108 6200 7.120444
## 33 540 61 1 1 37 1 5200 549 5600 6.291569
## 34 1798 66 1 1 21 14 24300 338 12500 7.494430
## 35 474 40 1 0 18 1 2700 117 2000 6.161207
## 36 1336 60 1 1 21 13 4500 562 4300 7.197435
## 37 541 51 1 0 30 4 1400 82 1200 6.293419
## 38 129 66 1 1 4 4 59 28 412 4.859812
## 39 1700 54 1 1 21 5 6800 1200 20400 7.438384
## 40 1750 66 1 1 31 24 16200 1400 17900 7.467371
## 41 624 61 1 1 21 13 1100 109 934 6.436151
## 42 791 66 1 0 14 8 2300 -60 487 6.673298
## 43 1487 51 1 0 3 3 22200 182 2800 7.304516
## 44 2021 56 1 0 34 3 51300 2700 42900 7.611348
## 45 1550 47 1 1 19 3 1100 120 4900 7.346010
## 46 401 64 1 0 44 8 571 57 670 5.993961
## 47 1295 62 1 0 8 8 10700 1300 16400 7.166266
## 48 449 56 1 0 31 1 661 37 538 6.107023
## 49 456 56 1 1 9 3 381 34 6700 6.122493
## 50 1142 53 1 1 30 1 28000 1900 26300 7.040536
## 51 577 64 1 0 26 2 3000 287 5700 6.357842
## 52 600 56 1 1 18 7 11700 -40 4000 6.396930
## 53 649 44 1 1 4 4 336 17 475 6.475433
## 54 822 60 1 0 22 20 896 77 752 6.711740
## 55 1080 52 1 0 18 5 388 55 1600 6.984716
## 56 1738 54 0 0 34 12 10700 842 15400 7.460490
## 57 581 54 1 0 19 19 408 23 403 6.364751
## 58 912 54 1 0 9 9 2600 239 2400 6.815640
## 59 650 69 1 0 37 13 261 40 817 6.476973
## 60 2199 52 1 1 8 8 5600 475 6300 7.695758
## 61 609 53 1 1 15 15 567 34 498 6.411819
## 62 1946 73 1 0 25 21 7800 484 8000 7.573531
## 63 552 52 1 0 30 1 2800 308 3500 6.313548
## 64 481 59 1 1 26 4 611 90 667 6.175867
## 65 526 45 1 0 8 7 2400 106 2000 6.265301
## 66 471 60 1 0 3 2 160 7 425 6.154858
## 67 630 56 1 0 29 1 1700 -55 420 6.445720
## 68 622 57 1 0 35 4 2500 143 1200 6.432940
## 69 999 52 1 0 28 17 159 21 398 6.906755
## 70 585 60 1 1 36 10 1700 33 449 6.371612
## 71 1107 57 1 0 17 6 2200 149 1100 7.009409
## 72 1099 59 1 0 34 10 8600 182 1800 7.002156
## 73 425 86 1 1 13 13 36 11 644 6.052089
## 74 2792 40 1 0 11 11 534 35 888 7.934514
## 75 350 54 1 0 31 4 1000 46 812 5.857933
## 76 363 58 1 1 36 6 717 80 880 5.894403
## 77 2265 63 1 1 35 6 18000 1700 18800 7.725330
## 78 377 45 1 0 7 5 238 57 1200 5.932245
## 79 879 63 1 1 21 9 1700 212 4900 6.778785
## 80 720 49 1 0 12 12 672 23 1400 6.579251
## 81 950 63 1 0 27 14 2600 6 1500 6.856462
## 82 1143 67 0 0 23 3 1800 56 918 7.041412
## 83 1064 58 1 0 27 3 3500 195 2600 6.969790
## 84 1253 60 1 1 36 5 2600 142 3700 7.133296
## 85 462 58 1 1 23 0 1400 50 769 6.135565
## 86 174 69 1 0 13 13 29 6 390 5.159055
## 87 474 63 1 0 41 4 2200 175 2600 6.161207
## 88 1248 48 1 1 21 7 3500 423 7300 7.129298
## 89 1101 62 1 1 32 3 954 96 1200 7.003974
## 90 348 43 1 1 12 10 586 79 1400 5.852202
## 91 650 55 1 1 28 5 5700 -438 817 6.476973
## 92 875 58 1 1 32 10 5300 308 2200 6.774224
## 93 1600 61 1 0 4 1 12300 877 9400 7.377759
## 94 1500 55 1 1 30 4 7900 665 4800 7.313221
## 95 323 39 1 1 15 3 637 63 517 5.777652
## 96 459 59 1 0 33 3 785 40 1400 6.129050
## 97 925 56 1 1 26 12 3300 67 2200 6.829794
## 98 375 46 1 1 4 4 599 20 501 5.926926
## 99 447 53 1 0 4 1 143 16 527 6.102559
## 100 1340 55 1 0 13 10 1400 131 2900 7.200425
## 101 1749 57 1 1 26 11 8100 40 10000 7.466799
## 102 491 43 1 1 21 2 561 54 521 6.196444
## 103 5299 64 1 0 42 13 2400 119 1500 8.575274
## 104 431 58 1 1 33 3 815 36 550 6.066108
## 105 729 50 1 1 15 3 2000 182 2600 6.591674
## 106 1284 54 1 1 32 3 12300 1300 19600 7.157735
## 107 1373 57 1 0 36 8 14300 1600 23600 7.224753
## 108 989 40 1 0 18 5 439 30 582 6.896694
## 109 515 52 1 1 27 1 1100 51 889 6.244167
## 110 1301 50 1 1 19 15 1800 130 1600 7.170888
## 111 834 58 1 0 35 1 4400 63 890 6.726233
## 112 849 46 1 1 24 24 538 36 473 6.744059
## 113 100 61 1 1 26 26 2700 394 10100 4.605170
## 114 679 62 1 0 40 6 4900 -463 1400 6.520621
## 115 567 56 1 0 31 10 597 65 1700 6.340359
## 116 559 54 1 0 22 2 2100 13 686 6.326149
## 117 704 52 1 1 6 6 50 8 903 6.556778
## 118 308 45 1 1 14 14 210 39 1900 5.730100
## 119 1392 48 1 1 6 6 4800 51 1100 7.238497
## 120 389 55 1 0 29 4 478 38 420 5.963579
## 121 790 69 1 0 45 37 1200 140 3200 6.672033
## 122 396 80 1 0 58 28 513 53 963 5.981414
## 123 398 54 1 0 4 4 633 69 1800 5.986452
## 124 707 46 1 1 6 1 130 26 1200 6.561031
## 125 984 60 1 0 7 4 1500 135 1700 6.891626
## 126 410 55 1 0 36 20 501 34 590 6.016157
## 127 1095 60 1 1 33 5 27600 1400 17100 6.998509
## 128 694 61 1 1 35 19 4200 75 1000 6.542472
## 129 834 61 1 1 32 0 7600 364 5300 6.726233
## 130 1630 39 1 1 8 8 227 27 822 7.396335
## 131 493 55 1 1 4 1 1300 80 834 6.200509
## 132 625 57 0 0 36 9 1400 87 979 6.437752
## 133 483 52 1 1 18 14 1000 35 548 6.180017
## 134 733 60 1 0 8 8 347 18 778 6.597146
## 135 2102 67 1 1 41 20 10300 1700 45400 7.650645
## 136 853 58 1 0 34 34 818 33 411 6.748760
## 137 345 54 1 1 33 0 994 56 781 5.843544
## 138 800 57 1 1 12 9 1800 32 479 6.684612
## 139 764 55 1 1 31 16 1100 145 2100 6.638568
## 140 806 59 1 1 3 3 3000 257 3900 6.692084
## 141 310 40 1 0 18 1 2400 60 1300 5.736572
## 142 1119 61 1 0 34 9 2500 71 1200 7.020191
## 143 1287 59 1 1 4 3 4700 222 2700 7.160069
## 144 1170 57 1 1 9 3 1900 208 5600 7.064759
## 145 880 62 1 0 36 12 5300 229 4000 6.779922
## 146 1091 33 1 0 9 9 181 36 1300 6.994850
## 147 1100 65 1 0 18 6 563 -271 544 7.003066
## 148 650 53 1 1 5 4 1482 40 557 6.476973
## 149 607 38 1 1 7 3 231 38 599 6.408529
## 150 1133 63 1 0 9 9 870 37 686 7.032624
## 151 393 58 1 1 36 6 285 40 956 5.973810
## 152 605 53 1 0 16 4 422 30 505 6.405229
## 153 1444 59 1 1 2 2 6204 401 10700 7.275172
## 154 1033 62 1 1 30 1 5400 478 7300 6.940222
## 155 1142 50 1 1 11 3 2600 166 2200 7.040536
## 156 537 46 1 1 20 20 1200 17 669 6.285998
## 157 693 42 1 0 17 12 1400 206 3000 6.541030
## 158 439 57 1 1 26 12 2600 280 3800 6.084499
## 159 358 64 1 0 43 11 584 45 423 5.880533
## 160 1276 64 1 0 41 17 635 52 1300 7.151485
## 161 873 41 1 1 2 2 149 21 567 6.771935
## 162 537 57 1 1 35 1 11400 210 4800 6.285998
## 163 713 57 1 1 12 2 1600 55 1300 6.569481
## 164 1350 68 1 1 5 5 3300 92 2100 7.207860
## 165 1268 47 1 0 20 4 1100 47 2500 7.145196
## 166 465 64 1 1 31 3 2400 326 2400 6.142037
## 167 693 46 1 1 7 3 2200 44 533 6.541030
## 168 369 49 1 1 4 1 65 -132 1200 5.910797
## 169 381 54 1 0 30 2 2700 386 4500 5.942800
## 170 467 49 1 1 13 0 513 49 534 6.146329
## 171 559 57 1 1 34 16 605 56 653 6.326149
## 172 218 57 1 1 33 5 504 41 421 5.384495
## 173 264 63 1 0 42 3 334 43 480 5.575949
## 174 185 58 1 0 39 1 766 49 560 5.220356
## 175 387 71 1 1 32 13 432 28 477 5.958425
## 176 2220 63 1 1 18 18 277 -80 540 7.705263
## 177 445 69 1 0 23 0 249 31 828 6.098074
## lsales lmktval comtensq ceotensq profmarg
## 1 8.732305 10.051908 81 4 15.5806456
## 2 5.645447 7.003066 100 100 16.9611301
## 3 5.129899 7.003066 81 9 23.6686382
## 4 7.003066 6.907755 484 484 -4.9090910
## 5 5.860786 5.958425 64 36 7.9772081
## 6 9.852194 8.268732 49 49 3.2315791
## 7 6.284134 6.434546 1225 100 4.4776120
## 8 8.476371 7.649693 1024 64 3.9791667
## 9 6.413459 6.118097 16 16 1.1475409
## 10 7.972466 8.268732 1521 25 7.9310346
## 11 7.090077 6.278522 676 49 2.8333333
## 12 6.327937 6.167517 1521 64 1.4285715
## 13 6.679599 6.519147 1369 1369 4.3969851
## 14 9.011889 8.648221 625 1 2.8536584
## 15 8.366370 7.244227 441 121 2.1162791
## 16 4.905275 6.324359 49 49 17.7777786
## 17 7.170120 6.741701 1444 16 4.2307692
## 18 6.513230 7.090077 961 144 17.0623150
## 19 7.244227 6.411819 1089 576 4.9285712
## 20 7.003066 6.779922 1296 1 6.2727275
## 21 7.740664 7.696213 400 36 9.1304350
## 22 6.784457 7.313221 1296 196 9.1628962
## 23 7.377759 8.070906 1024 16 12.0625000
## 24 4.356709 6.126869 64 64 16.6666660
## 25 9.277999 8.682708 16 16 2.7570093
## 26 4.595120 7.740664 529 16 25.2525253
## 27 8.242756 7.495542 576 36 5.9473686
## 28 5.010635 6.569481 225 4 18.6666660
## 29 7.696213 7.313221 196 64 8.3636360
## 30 8.839276 8.455317 2025 676 7.0000000
## 31 9.024011 9.116030 169 16 7.1807227
## 32 8.433811 8.732305 1369 81 2.3478260
## 33 8.556414 8.630522 1369 1 10.5576925
## 34 10.098231 9.433484 441 196 1.3909465
## 35 7.901007 7.600903 324 1 4.3333335
## 36 8.411833 8.366370 441 169 12.4888887
## 37 7.244227 7.090077 900 16 5.8571429
## 38 4.077538 6.021023 16 16 47.4576263
## 39 8.824677 9.923290 441 25 17.6470585
## 40 9.692766 9.792556 961 576 8.6419754
## 41 7.003066 6.839477 441 169 9.9090910
## 42 7.740664 6.188264 196 64 -2.6086957
## 43 10.007848 7.937375 9 9 0.8198198
## 44 10.845446 10.666627 1156 9 5.2631578
## 45 7.003066 8.496990 361 9 10.9090910
## 46 6.347389 6.507277 1936 64 9.9824867
## 47 9.277999 9.705036 64 64 12.1495323
## 48 6.493754 6.287858 961 1 5.5975795
## 49 5.942800 8.809863 81 9 8.9238844
## 50 10.239960 10.177324 900 1 6.7857141
## 51 8.006368 8.648221 676 4 9.5666666
## 52 9.367344 8.294049 324 49 -0.3418804
## 53 5.817111 6.163315 16 16 5.0595236
## 54 6.797940 6.622736 484 400 8.5937500
## 55 5.961005 7.377759 324 25 14.1752577
## 56 9.277999 9.642123 1156 144 7.8691587
## 57 6.011267 5.998937 361 361 5.6372547
## 58 7.863267 7.783224 81 81 9.1923075
## 59 5.564520 6.705639 1369 169 15.3256702
## 60 8.630522 8.748305 64 64 8.4821424
## 61 6.340359 6.210600 225 225 5.9964728
## 62 8.961879 8.987197 625 441 6.2051282
## 63 7.937375 8.160519 900 1 11.0000000
## 64 6.415097 6.502790 676 16 14.7299509
## 65 7.783224 7.600903 64 49 4.4166665
## 66 5.075174 6.052089 9 4 4.3750000
## 67 7.438384 6.040255 841 1 -3.2352941
## 68 7.824046 7.090077 1225 16 5.7199998
## 69 5.068904 5.986452 784 289 13.2075472
## 70 7.438384 6.107023 1296 100 1.9411764
## 71 7.696213 7.003066 289 36 6.7727275
## 72 9.059518 7.495542 1156 100 2.1162791
## 73 3.583519 6.467699 169 169 30.5555553
## 74 6.280396 6.788972 121 121 6.5543070
## 75 6.907755 6.699501 961 16 4.5999999
## 76 6.575076 6.779922 1296 36 11.1576014
## 77 9.798127 9.841612 1225 36 9.4444447
## 78 5.472270 7.090077 49 25 23.9495792
## 79 7.438384 8.496990 441 81 12.4705887
## 80 6.510258 7.244227 144 144 3.4226191
## 81 7.863267 7.313221 729 196 0.2307692
## 82 7.495542 6.822197 529 9 3.1111112
## 83 8.160519 7.863267 729 9 5.5714288
## 84 7.863267 8.216088 1296 25 5.4615383
## 85 7.244227 6.645091 529 0 3.5714285
## 86 3.367296 5.966147 169 169 20.6896553
## 87 7.696213 7.863267 1681 16 7.9545455
## 88 8.160519 8.895630 441 49 12.0857143
## 89 6.860664 7.090077 1024 9 10.0628929
## 90 6.373320 7.244227 144 100 13.4812288
## 91 8.648221 6.705639 784 25 -7.6842103
## 92 8.575462 7.696213 1024 100 5.8113208
## 93 9.417355 9.148465 16 1 7.1300812
## 94 8.974618 8.476371 900 16 8.4177217
## 95 6.456769 6.248043 225 9 9.8901100
## 96 6.665684 7.244227 1089 9 5.0955415
## 97 8.101678 7.696213 676 144 2.0303030
## 98 6.395262 6.216606 16 16 3.3388982
## 99 4.962845 6.267200 16 1 11.1888113
## 100 7.244227 7.972466 169 100 9.3571424
## 101 8.999619 9.210340 676 121 0.4938272
## 102 6.329721 6.255750 441 4 9.6256685
## 103 7.783224 7.313221 1764 169 4.9583335
## 104 6.703188 6.309918 1089 9 4.4171777
## 105 7.600903 7.863267 225 9 9.1000004
## 106 9.417355 9.883285 1024 9 10.5691061
## 107 9.568015 10.069002 1296 64 11.1888113
## 108 6.084499 6.366470 324 25 6.8337131
## 109 7.003066 6.790097 729 1 4.6363635
## 110 7.495542 7.377759 361 225 7.2222223
## 111 8.389359 6.791222 1225 1 1.4318181
## 112 6.287858 6.159095 576 576 6.6914496
## 113 7.901007 9.220291 676 676 14.5925922
## 114 8.496990 7.244227 1600 36 -9.4489794
## 115 6.391917 7.438384 961 100 10.8877726
## 116 7.649693 6.530878 484 4 0.6190476
## 117 3.912023 6.805723 36 36 16.0000000
## 118 5.347107 7.549609 196 196 18.5714283
## 119 8.476371 7.003066 36 36 1.0625000
## 120 6.169611 6.040255 841 16 7.9497910
## 121 7.090077 8.070906 2025 1369 11.6666670
## 122 6.240276 6.870053 3364 784 10.3313837
## 123 6.450470 7.495542 16 16 10.9004736
## 124 4.867535 7.090077 36 1 20.0000000
## 125 7.313221 7.438384 49 16 9.0000000
## 126 6.216606 6.380123 1296 400 6.7864270
## 127 10.225571 9.746834 1089 25 5.0724640
## 128 8.342840 6.907755 1225 361 1.7857143
## 129 8.935904 8.575462 1024 0 4.7894735
## 130 5.424950 6.711740 64 64 11.8942728
## 131 7.170120 6.726233 16 1 6.1538463
## 132 7.244227 6.886532 1296 81 6.2142859
## 133 6.907755 6.306275 324 196 3.5000000
## 134 5.849325 6.656726 64 64 5.1873198
## 135 9.239900 10.723268 1681 400 16.5048542
## 136 6.706862 6.018593 1156 1156 4.0342298
## 137 6.901737 6.660575 1089 0 5.6338029
## 138 7.495542 6.171700 144 81 1.7777778
## 139 7.003066 7.649693 961 256 13.1818180
## 140 8.006368 8.268732 9 9 8.5666666
## 141 7.783224 7.170120 324 1 2.5000000
## 142 7.824046 7.090077 1156 81 2.8399999
## 143 8.455317 7.901007 16 9 4.7234044
## 144 7.549609 8.630522 81 9 10.9473686
## 145 8.575462 8.294049 1296 144 4.3207545
## 146 5.198497 7.170120 81 81 19.8895035
## 147 6.333280 6.298949 324 36 -48.1349907
## 148 7.301148 6.322565 25 16 2.6990554
## 149 5.442418 6.395262 49 9 16.4502163
## 150 6.768493 6.530878 81 81 4.2528734
## 151 5.652489 6.862758 1296 36 14.0350876
## 152 6.045005 6.224558 256 16 7.1090050
## 153 8.732949 9.277999 4 4 6.4635720
## 154 8.594154 8.895630 900 1 8.8518515
## 155 7.863267 7.696213 121 9 6.3846154
## 156 7.090077 6.505784 400 400 1.4166666
## 157 7.244227 8.006368 289 144 14.7142859
## 158 7.863267 8.242756 676 144 10.7692308
## 159 6.369901 6.047372 1849 121 7.7054796
## 160 6.453625 7.170120 1681 289 8.1889763
## 161 5.003946 6.340359 4 4 14.0939598
## 162 9.341369 8.476371 1225 1 1.8421053
## 163 7.377759 7.170120 144 4 3.4375000
## 164 8.101678 7.649693 25 25 2.7878788
## 165 7.003066 7.824046 400 16 4.2727275
## 166 7.783224 7.783224 961 9 13.5833330
## 167 7.696213 6.278522 49 9 2.0000000
## 168 4.174387 7.090077 16 1 -203.0769196
## 169 7.901007 8.411833 900 4 14.2962961
## 170 6.240276 6.280396 169 0 9.5516567
## 171 6.405229 6.481577 1156 256 9.2561979
## 172 6.222576 6.042633 1089 25 8.1349211
## 173 5.811141 6.173786 1764 9 12.8742514
## 174 6.641182 6.327937 1521 1 6.3968668
## 175 6.068426 6.167517 1024 169 6.4814816
## 176 5.624018 6.291569 324 324 -28.8808670
## 177 5.517453 6.719013 529 0 12.4497995ceo <- ceosal2- Compare the salaries of graduates and non-graduates using a boxplot.
ceo$grad <- as.factor(ceo$grad)
qplot(grad, lsalary, data =ceo, geom= "boxplot", colour=I("coral"))
#It can be seen, that on average, the salaries of graduates and non graduates seem to be the same, however, for non graduates, the minimum and maximum value is higher than that of graduates. There seems to be outliers, one over the highest value for non graduates, and the other under the lowest value of graduate's salaries. - Compare the salaries of college-educated CEOs and others.
ceo$college <- as.factor(ceo$college)
qplot(college, lsalary, data =ceo, geom= "boxplot", colour= I("magenta"))
#According to the boxplot, the median salary for non college graduates is higher than that of college graduates, however, the maximum value overall is in the salaries of the college graduates, as well as the minimum. The outliers in the data are part from the graduate group, which could influence on the mean.- Comment on the relationship between salary and age for graduates and others.
g <- ggplot(ceo, aes(age,lsalary))
g + geom_point() +
geom_smooth(method = "lm") +
facet_grid(. ~ grad) ## `geom_smooth()` using formula 'y ~ x'
#As it can be seen, there is no apparent relation between salary and age in non graduates, since the line is almost flat, however there seems to be a positive relation between salary and age in graduates (when age increases, salary increases).- Comment on the relationship between salary and tenure for graduates and others.
g <- ggplot(ceo, aes(ceoten,lsalary))
g + geom_point() +
geom_smooth(method = "lm") +
facet_grid(. ~ grad) ## `geom_smooth()` using formula 'y ~ x'
#For both of these groups, it could be said that there is a positive relation between salary and tenure, however, this relation seems to be stronger for non graduates, with a more notorious slope.DATASET: big9salary
wooldridge::big9salary## id year salary pubindx totpge assist assoc prof chair top20phd yearphd
## 1 101 92 NA 30.54 92.6670 0 0 1 0 0 73
## 2 101 95 NA 30.96 107.1700 0 0 1 0 0 73
## 3 101 99 107100 40.45 186.5000 0 0 1 0 0 73
## 4 102 92 79420 33.54 127.5000 0 0 1 0 0 76
## 5 102 95 88239 33.89 133.0000 0 0 1 0 0 76
## 6 102 99 100450 36.23 191.5000 0 0 1 0 0 76
## 7 103 92 87450 44.90 183.5000 0 0 1 0 1 61
## 8 103 95 96831 45.09 194.5000 0 0 1 0 1 61
## 9 103 99 108290 46.81 224.5000 0 0 1 0 1 61
## 10 104 92 NA 1.58 24.5000 1 0 0 0 1 91
## 11 104 95 NA 22.74 118.5000 NA NA NA 0 1 91
## 12 104 99 82000 46.21 232.5000 0 1 0 0 1 91
## 13 105 92 54585 2.83 61.0000 NA NA NA 0 1 72
## 14 105 95 59360 2.83 61.0000 NA NA NA 0 1 72
## 15 105 99 63610 2.83 80.0000 0 1 0 0 1 72
## 16 106 92 56785 25.90 145.5000 NA NA NA 0 0 76
## 17 106 95 60850 25.90 145.5000 NA NA NA 0 0 76
## 18 106 99 68440 25.90 145.5000 0 0 1 0 0 76
## 19 107 92 59925 27.12 66.8330 0 0 1 0 1 66
## 20 107 95 66093 27.12 66.8330 0 0 1 0 1 66
## 21 107 99 71170 28.29 89.8330 0 0 1 0 1 66
## 22 108 92 59305 5.87 69.8330 0 0 1 1 1 83
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## 465 11.971236 NA NA 3.111696e+02 0 2.87016892
## 466 10.290856 NA NA 0.000000e+00 1 NA
## 467 10.286605 NA NA 1.040400e+00 0 0.01980261
## 468 10.423531 NA NA 1.488400e+00 0 0.19885089
## 469 11.491620 22 484 1.999396e+02 0 2.64900780
## 470 11.532581 25 625 2.214144e+02 0 2.70001793
## 471 11.748535 29 841 2.214144e+02 0 2.70001793
## 472 NA NA NA 0.000000e+00 1 NA
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## 474 NA NA NA 0.000000e+00 1 NA
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## 476 10.889752 10 100 5.475600e+02 0 3.15273595
## 477 11.288419 14 196 8.450649e+02 0 3.36970663
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## 499 NA 0 0 0.000000e+00 1 NA
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## 506 11.314474 36 1296 2.390116e+02 0 2.73825598
## 507 NA 40 1600 2.390116e+02 0 2.73825598
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## 510 11.442503 NA NA 3.515625e+02 0 2.93119383
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## 518 11.350453 25 625 2.131600e+02 0 2.68102145
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## 583 10.736397 0 0 0.000000e+00 1 NA
## 584 10.885566 3 9 6.230015e+02 0 3.21727443
## 585 11.321765 7 49 7.089640e+03 0 4.43319511
## 586 10.866662 5 25 8.154090e+03 0 4.50313759
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## 590 11.714952 21 441 1.375225e+04 0 4.76447916
## 591 11.875302 25 625 1.482793e+04 0 4.80213404
## 592 NA 0 0 0.000000e+00 1 NA
## 593 10.858999 0 0 0.000000e+00 1 NA
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## 595 10.714417 0 0 0.000000e+00 1 NA
## 596 10.871896 3 9 0.000000e+00 1 NA
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## 598 11.272127 18 324 1.906196e+03 0 3.77643228
## 599 11.409784 21 441 2.961536e+03 0 3.99673176
## 600 12.009571 25 625 3.061409e+03 0 4.01331520
## 601 11.346870 19 361 1.096934e+03 0 3.50013733
## 602 11.569305 22 484 1.096934e+03 0 3.50013733
## 603 11.763684 26 676 1.096934e+03 0 3.50013733
## 604 11.418614 21 441 6.510876e+03 0 4.39061451
## 605 11.589887 24 576 7.428716e+03 0 4.45655441
## 606 NA 28 784 7.844645e+03 0 4.48379326
## 607 11.407565 16 256 1.097675e+04 0 4.65176725
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## 612 11.920388 34 1156 1.574774e+04 0 4.83222628
## 613 NA 0 0 0.000000e+00 1 NA
## 614 10.823771 2 4 1.512900e+00 0 0.20701419
## 615 11.029228 6 36 2.079360e+01 0 1.51732266
## 616 11.198215 19 361 2.022301e+03 0 3.80599570
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## 620 11.407565 31 961 1.713481e+02 0 2.57184863
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## 651 11.532728 28 784 1.308269e+03 0 3.58822989
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## 663 11.517913 16 256 5.422850e+03 0 4.29918814
## 664 NA 0 0 5.184000e-01 0 -0.32850403
## 665 10.858999 0 0 5.184000e-01 0 -0.32850403
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## 684 11.584315 26 676 6.864122e+03 0 4.41703176
## 685 NA 21 441 1.830338e+04 0 4.90742064
## 686 11.989160 24 576 2.219206e+04 0 5.00374508
## 687 11.717498 28 784 2.466784e+04 0 5.05662775
## 688 11.617826 42 1764 0.000000e+00 1 NA
## 689 11.672609 45 2025 0.000000e+00 1 NA
## 690 NA 49 2401 0.000000e+00 1 NA
## 691 11.608853 15 225 1.886777e+04 0 4.92260504
## 692 11.688264 18 324 1.886777e+04 0 4.92260504
## 693 11.771822 22 484 3.196586e+04 0 5.18621206
## 694 10.928004 NA NA 0.000000e+00 1 NA
## 695 10.959731 NA NA 0.000000e+00 1 NA
## 696 NA NA NA 0.000000e+00 1 NA
## 697 NA 9 81 6.525408e+03 0 4.39172935
## 698 11.505981 12 144 8.177585e+03 0 4.50457621
## 699 11.623169 16 256 1.694163e+04 0 4.86876440
## 700 NA 6 36 1.322777e+03 0 3.59374428
## 701 11.307745 9 81 3.161813e+03 0 4.02945042
## 702 11.512925 13 169 3.640916e+03 0 4.09999514
## 703 10.869197 NA NA 0.000000e+00 1 NA
## 704 10.900897 NA NA 0.000000e+00 1 NA
## 705 NA NA NA 0.000000e+00 1 NA
## 706 NA NA NA 0.000000e+00 1 NA
## 707 NA NA NA 0.000000e+00 1 NA
## 708 NA NA NA 0.000000e+00 1 NA
## 709 NA NA NA 0.000000e+00 1 NA
## 710 NA NA NA 0.000000e+00 1 NA
## 711 NA NA NA 0.000000e+00 1 NA
## 712 11.627700 NA NA 0.000000e+00 1 NA
## 713 11.679990 NA NA 0.000000e+00 1 NA
## 714 NA NA NA 0.000000e+00 1 NA
## 715 11.359961 34 1156 0.000000e+00 1 NA
## 716 11.539557 37 1369 0.000000e+00 1 NA
## 717 NA 41 1681 0.000000e+00 1 NA
## 718 11.612128 29 841 7.209708e+03 0 4.44159174
## 719 11.703983 32 1024 7.509956e+03 0 4.46199226
## 720 11.769852 36 1296 7.509956e+03 0 4.46199226
## 721 NA NA NA 0.000000e+00 1 NA
## 722 NA NA NA 0.000000e+00 1 NA
## 723 NA NA NA 0.000000e+00 1 NA
## 724 11.381678 31 961 1.453897e+03 0 3.64100146
## 725 11.459672 34 1156 1.453897e+03 0 3.64100146
## 726 11.502168 38 1444 1.453897e+03 0 3.64100146
## 727 11.156851 6 36 1.637011e+03 0 3.70031381
## 728 11.221195 9 81 6.840944e+03 0 4.41534042
## 729 11.608235 13 169 1.270805e+04 0 4.72499561
## 730 NA NA NA 0.000000e+00 1 NA
## 731 NA NA NA 0.000000e+00 1 NA
## 732 NA NA NA 0.000000e+00 1 NA
## 733 11.350407 19 361 8.114407e+03 0 4.50069809
## 734 11.534188 22 484 1.062961e+04 0 4.63569927
## 735 11.583943 26 676 1.062961e+04 0 4.63569927
## 736 10.810798 NA NA 2.248656e+03 0 3.85904408
## 737 11.126012 NA NA 2.248656e+03 0 3.85904408
## 738 11.363903 NA NA 3.093584e+03 0 4.01854277
## 739 NA 0 0 4.000000e-02 0 -1.60943794
## 740 10.833681 2 4 4.000000e-02 0 -1.60943794
## 741 10.876159 6 36 7.139584e+02 0 3.28541231
## 742 10.673758 NA NA 0.000000e+00 1 NA
## 743 10.842283 NA NA 0.000000e+00 1 NA
## 744 NA NA NA 0.000000e+00 1 NA
## 745 11.576295 NA NA 4.294181e+03 0 4.18250799
## 746 11.679575 NA NA 9.517953e+03 0 4.58046770
## 747 11.707093 NA NA 1.679616e+04 0 4.86445284
## 748 NA 6 36 4.826081e+03 0 4.24089479
## 749 11.274046 9 81 9.162318e+03 0 4.56142712
## 750 11.667576 13 169 1.146827e+04 0 4.67366934
## 751 11.087222 32 1024 8.643600e+00 0 1.07840955
## 752 11.121423 35 1225 8.643600e+00 0 1.07840955
## 753 11.129907 39 1521 8.643600e+00 0 1.07840955
## 754 11.236948 24 576 1.829273e+03 0 3.75583696
## 755 11.333237 27 729 1.829273e+03 0 3.75583696
## 756 11.442546 31 961 1.829273e+03 0 3.75583696
## 757 11.261241 NA NA 0.000000e+00 1 NA
## 758 11.589887 NA NA 0.000000e+00 1 NA
## 759 NA NA NA 0.000000e+00 1 NA
## 760 11.329495 14 196 1.551270e+04 0 4.82470703
## 761 11.553030 17 289 3.570588e+04 0 5.24153519
## 762 11.720744 21 441 6.195121e+04 0 5.51705122
## 763 10.774990 4 16 1.592010e+01 0 1.38379121
## 764 10.932196 7 49 1.498464e+03 0 3.65609789
## 765 11.561716 11 121 3.970260e+03 0 4.14329338
## 766 NA 7 49 5.641512e+03 0 4.31895351
## 767 11.319474 10 100 6.062180e+03 0 4.35491228
## 768 11.750957 14 196 1.624605e+04 0 4.84780264
## 769 10.722342 NA NA 0.000000e+00 1 NA
## 770 10.966162 NA NA 0.000000e+00 1 NA
## 771 NA NA NA 0.000000e+00 1 NA
## 772 10.941340 NA NA 1.687401e+02 0 2.56417990
## 773 11.277912 NA NA 2.076481e+02 0 2.66792250
## 774 11.434964 NA NA 2.122849e+02 0 2.67896461
## 775 10.770062 6 36 1.796912e+03 0 3.74691248
## 776 11.029261 9 81 2.089404e+03 0 3.82231712
## 777 11.407565 13 169 2.128900e+03 0 3.83168030
## 778 11.363521 9 81 7.029145e+03 0 4.42891026
## 779 11.501945 12 144 1.008618e+04 0 4.60946083
## 780 11.729850 16 256 1.473310e+04 0 4.79892588
## 781 11.417031 19 361 5.286744e+03 0 4.28647900
## 782 11.609054 22 484 5.396372e+03 0 4.29674101
## 783 11.715025 26 676 5.649026e+03 0 4.31961918
## 784 10.731056 1 1 4.796100e+00 0 0.78390157
## 785 10.856033 3 9 6.577209e+01 0 2.09309793
## 786 11.150950 6 36 3.534302e+03 0 4.08513546b1=big9salary1.Comment on the relationship between years of experience and salary.
qplot(salary,exper,data=b1,geom=c("point","smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 158 rows containing non-finite values (stat_smooth).## Warning: Removed 158 rows containing missing values (geom_point).
ggplot(b1,aes(exper,salary))+
geom_point()+
geom_smooth(method="loess",se=FALSE)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 158 rows containing non-finite values (stat_smooth).
## Removed 158 rows containing missing values (geom_point).
#According to the ggplot, the more years of experience, the higher the salary, however, with approximately 35 years of experience onwards, a decrease in the salary amount starts to be seen.2.Comment on the relationship between years of experience and salary for males and females.
f1=big9salary %>%
filter(female==1) %>%
dplyr::select(salary,exper)
m1=big9salary %>%
filter(female==0) %>%
dplyr::select(salary,exper)
qplot(exper,salary,data=f1)## Warning: Removed 7 rows containing missing values (geom_point).
qplot(exper,salary,data=m1)## Warning: Removed 121 rows containing missing values (geom_point).
qplot(exper,salary,data=f1,geom=c("point","smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Removed 7 rows containing missing values (geom_point).
qplot(exper,salary,data=m1,geom=c("point","smooth")) ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 121 rows containing non-finite values (stat_smooth).## Warning: Removed 121 rows containing missing values (geom_point).
#The relationship between years of experience and salary for women shows a directly proportional increase, while the relationship between years of experience and salary for men also shows an increase; however, the increase is not as significant as for women, and after certain years of experience, the amount decreases.3.Compare the relationship between years of experience and salary for people having Ph.D. from top 20 universities with others
p1=big9salary %>%
filter(top20phd==1) %>%
dplyr::select(salary,exper)
n1=big9salary %>%
filter(top20phd==0) %>%
dplyr::select(salary,exper)
qplot(exper,salary,data=p1,geom=c("point","smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 58 rows containing non-finite values (stat_smooth).## Warning: Removed 58 rows containing missing values (geom_point).
qplot(exper,salary,data=n1,geom=c("point","smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 21 rows containing non-finite values (stat_smooth).## Warning: Removed 21 rows containing missing values (geom_point).
#For professors from the top 20 universities, the relationship between years of experience and salary increases proportionally. On the other hand, for professors from other universities, the ggplot shows that the relationship between experience and salary increases proportionally up to 30 years of experience, thereafter the relationship decreases or stagnates- Is the relationship between salary and publication index different for full professors and others?
r1=big9salary %>%
filter(prof==1) %>%
dplyr::select(salary,pubindx)
o1=big9salary %>%
filter(assist== 1|assoc== 1| chair==1)%>%
dplyr::select(salary,pubindx)
qplot(pubindx,salary,data=r1,geom=c("point","smooth"))## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 27 rows containing non-finite values (stat_smooth).## Warning: Removed 27 rows containing missing values (geom_point).
qplot(pubindx,salary,data=o1,geom=c("point","smooth")) ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
#The relationship of salary with the publications index does not really differ much between professors and other positions in the database, with the ggplot it is evident that the salary of professors increases with respect to the increase in the index, but the same happens with the other positions, but not in such an outstanding way. - Does a female full professor or a male full professor get a higher average salary?
q1 <- wooldridge::big9salary
boxplot(q1$salary~q1$female,xlab= "Gender", ylab = "Salary", col="Blue", lty=1, outpch=20, outbg="Yellow", wwhisklty=2 )
#On average, male professors win more than female ones.DATASET: hprice1
hp<-wooldridge::hprice1- On average do colonial or non-colonial houses have a higher price?
hp %>% dplyr::summarise(
colonial_price_mean = mean_if(1,colonial,data = price)
)## colonial_price_mean
## 1 302.9189hp %>% dplyr::summarise(
noncolonial_price = mean_if(0,colonial, data = price)
)## noncolonial_price
## 1 272.3704# On average, the colonial houses have a higher price- On average do colonial or non-colonial houses have a higher price per square foot?
hp %>% dplyr::mutate(psf=price/sqrft) %>%
filter(colonial==1)%>%
select(psf)%>%
summarise(mean=mean(psf))## mean
## 1 0.1488651hp %>% dplyr::mutate(psf=price/sqrft) %>%
filter(colonial==0)%>%
select(psf)%>%
summarise(mean=mean(psf))## mean
## 1 0.1422862#On average, the colonial houses have a higher price per square foot.- Does the price per square foot increase with the number of bedrooms?
ab <- wooldridge::hprice1 %>%
dplyr::mutate(Psf=price/sqrft)
boxplot(ab$Psf~ab$bdrms,xlab= "# Bedrooms", ylab = "Price per sqrft", col="Blue", lty=1, outpch=20, outbg="Yellow", wwhisklty=2 )
#The price per square foot does not increases if the number of bedrooms increases.- Is the difference between the actual price and assessed value higher for colonial houses relative to non-colonial?
#Add a column with the difference of price and assess value for each house
hp <- hp %>% dplyr::mutate(diference = hp$price - hp$assess)
#split the dataset by non-colonial (0) and colonial (1)
hp1 <- split(hp, hp$colonial)
noncolonial <- hp1$`0`
yescolonial <- hp1$`1`
#find the mean of the difference between actual price and assessed value (for colonial and non-colonoial)
x1 <- mean(noncolonial$diference)
x2 <- mean(yescolonial$diference)
x1## [1] -31.55185x2## [1] -18.0467#for both types of houses, the assessed value is higher than the price. For non colonial house, this difference is bigger- As the number of bedrooms increases, does the difference between the actual price and assessed price increase?
r1 <- cor.test(noncolonial$diference, noncolonial$bdrms)
r2 <- cor.test(yescolonial$diference, yescolonial$bdrms)
r1##
## Pearson's product-moment correlation
##
## data: noncolonial$diference and noncolonial$bdrms
## t = -0.045097, df = 25, p-value = 0.9644
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3877041 0.3722709
## sample estimates:
## cor
## -0.00901894r2##
## Pearson's product-moment correlation
##
## data: yescolonial$diference and yescolonial$bdrms
## t = 1.2658, df = 59, p-value = 0.2106
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.0930340 0.3981159
## sample estimates:
## cor
## 0.162596#for both colonial and noncolonial houses, the correlation between difference of price and assessed value and number of bedrooms is very weak, almost null