Final Project
Jose Miguel Botero C, Karen Sofía Guerrero B, Juan José Gutierrez L, Juan Felipe Peña O, Marcela Mejía M
2022-06-17
DATASET: beauty
pacman::p_load(tidyverse,Lock5Data,ISLR,nycflights13,wooldridge,expss,hablar,rio,rstatix,ggpubr, ROCit, modelsummary)
wooldridge::beauty## wage lwage belavg abvavg exper looks union goodhlth black female
## 1 5.73 1.74571550 0 1 30 4 0 1 0 1
## 2 4.28 1.45395303 0 0 28 3 0 1 0 1
## 3 7.96 2.07442904 0 1 35 4 0 1 0 1
## 4 11.57 2.44841552 0 0 38 3 0 1 0 0
## 5 11.42 2.43536615 0 0 27 3 0 1 0 0
## 6 3.91 1.36353743 0 0 20 3 0 0 0 1
## 7 8.76 2.17019582 0 0 12 3 0 1 0 0
## 8 7.69 2.03992081 0 1 5 4 1 1 0 0
## 9 5.00 1.60943794 0 0 5 3 0 1 0 1
## 10 3.89 1.35840917 0 0 12 3 0 1 0 1
## 11 3.45 1.23837423 0 1 3 4 0 1 0 1
## 12 4.03 1.39376640 0 1 6 4 0 1 0 0
## 13 5.14 1.63705301 1 0 19 2 0 1 0 0
## 14 3.00 1.09861231 0 0 8 3 0 1 0 0
## 15 7.99 2.07819080 0 1 12 4 0 1 0 0
## 16 6.01 1.79342484 0 1 17 4 0 1 0 0
## 17 5.16 1.64093649 0 0 7 3 0 1 0 0
## 18 11.54 2.44581914 0 1 12 4 0 1 0 0
## 19 10.44 2.34564447 0 1 10 4 0 1 0 1
## 20 7.69 2.03992081 0 0 7 3 0 1 0 1
## 21 7.69 2.03992081 0 1 7 4 1 1 0 0
## 22 6.79 1.91545093 0 0 19 3 0 1 0 0
## 23 6.87 1.92716408 0 0 33 3 0 1 0 0
## 24 17.03 2.83497643 0 0 32 3 0 1 0 0
## 25 10.05 2.30757260 0 1 12 4 0 1 0 0
## 26 15.81 2.76064277 0 1 24 4 0 1 0 0
## 27 14.84 2.69732618 0 1 29 5 0 1 0 0
## 28 19.08 2.94864058 0 1 17 5 0 1 0 0
## 29 8.35 2.12226152 1 0 41 1 0 0 0 0
## 30 9.62 2.26384425 0 0 40 3 0 0 0 0
## 31 5.96 1.78507054 0 0 10 3 0 1 0 0
## 32 5.73 1.74571550 0 0 43 3 0 0 0 0
## 33 6.73 1.90657520 0 1 41 4 0 1 0 0
## 34 8.17 2.10046887 1 0 18 2 0 1 0 0
## 35 12.39 2.51688981 0 1 28 4 0 1 0 0
## 36 9.62 2.26384425 1 0 37 2 0 1 0 0
## 37 4.83 1.57484651 0 1 20 4 0 1 1 0
## 38 7.69 2.03992081 1 0 10 2 1 1 0 0
## 39 12.50 2.52572870 0 0 31 3 0 1 0 0
## 40 2.82 1.03673685 0 0 18 3 0 1 0 0
## 41 12.31 2.51041198 0 0 9 3 1 1 0 0
## 42 13.22 2.58173084 0 0 42 3 0 1 0 0
## 43 16.83 2.82316303 0 1 14 4 0 1 0 0
## 44 11.54 2.44581914 0 0 27 3 0 1 0 0
## 45 4.95 1.59938753 0 0 20 3 0 1 1 1
## 46 7.21 1.97546899 0 0 14 3 0 1 0 0
## 47 11.58 2.44927955 0 0 4 3 0 1 0 0
## 48 15.38 2.73306799 0 0 13 3 0 1 0 0
## 49 6.10 1.80828881 0 0 5 3 0 1 0 0
## 50 7.93 2.07065296 0 0 32 3 0 1 0 0
## 51 9.16 2.21484613 0 1 19 4 1 1 0 1
## 52 6.91 1.93296957 0 1 14 4 0 1 0 1
## 53 20.18 3.00469208 0 0 28 3 0 1 0 0
## 54 9.93 2.29556060 0 0 31 3 1 1 0 0
## 55 7.79 2.05284095 0 1 18 4 0 1 0 0
## 56 5.13 1.63510573 0 0 23 3 0 1 0 0
## 57 10.99 2.39698577 0 1 14 4 0 1 0 0
## 58 6.56 1.88099062 1 0 17 2 0 1 0 0
## 59 10.10 2.31253552 0 0 16 3 0 1 0 0
## 60 10.74 2.37397504 0 0 19 3 0 1 0 0
## 61 16.35 2.79422784 0 0 26 3 0 1 0 0
## 62 4.62 1.53039467 0 1 20 4 0 1 0 1
## 63 7.49 2.01356888 0 0 18 3 0 1 0 0
## 64 19.23 2.95647144 0 0 35 3 0 1 0 0
## 65 4.77 1.56234634 0 1 9 4 0 1 0 1
## 66 7.14 1.96571279 1 0 20 2 0 1 0 1
## 67 14.42 2.66861606 0 1 7 4 0 1 0 0
## 68 9.28 2.22786140 0 1 40 4 0 1 0 0
## 69 29.98 3.40053058 0 0 12 3 0 1 0 0
## 70 32.79 3.49012351 0 1 33 4 0 1 0 0
## 71 9.62 2.26384425 0 0 16 3 0 1 0 0
## 72 8.36 2.12345839 0 0 15 3 0 1 0 1
## 73 21.63 3.07408118 0 0 11 3 0 1 0 0
## 74 15.90 2.76631904 0 1 24 4 0 1 0 0
## 75 5.77 1.75267208 1 0 6 2 0 1 0 0
## 76 20.99 3.04404616 1 0 40 2 0 1 0 0
## 77 23.32 3.14931130 0 1 15 5 0 1 0 0
## 78 4.97 1.60341978 0 1 9 4 0 1 0 0
## 79 6.89 1.93007112 1 0 16 2 0 1 0 0
## 80 5.73 1.74571550 1 0 12 2 0 1 0 1
## 81 5.19 1.64673376 0 0 8 3 0 1 0 0
## 82 7.79 2.05284095 0 0 25 3 1 1 0 1
## 83 8.79 2.17361474 0 0 24 3 1 1 0 1
## 84 9.32 2.23216271 0 1 25 4 1 1 0 1
## 85 8.33 2.11986351 0 1 7 4 0 1 0 1
## 86 10.12 2.31451368 0 0 40 3 0 1 1 1
## 87 1.98 0.68309683 0 0 29 3 0 0 0 1
## 88 8.24 2.10900021 0 1 13 4 0 1 0 1
## 89 5.05 1.61938822 0 1 5 4 0 1 0 1
## 90 6.55 1.87946510 0 0 10 3 0 1 0 1
## 91 4.47 1.49738836 0 0 25 3 1 1 0 0
## 92 7.96 2.07442904 0 0 10 3 0 1 0 1
## 93 6.25 1.83258152 1 0 20 2 0 1 0 0
## 94 4.13 1.41827738 0 0 7 3 0 1 0 0
## 95 6.73 1.90657520 1 0 25 2 0 1 0 1
## 96 7.69 2.03992081 0 0 8 3 0 1 0 1
## 97 4.35 1.47017586 1 0 8 2 0 1 0 1
## 98 7.69 2.03992081 0 0 28 3 0 1 0 0
## 99 4.81 1.57069707 1 0 37 2 0 1 0 0
## 100 5.24 1.65632141 0 0 31 3 0 1 0 0
## 101 2.14 0.76080590 0 1 24 4 0 1 0 0
## 102 4.49 1.50185263 0 1 33 4 0 1 0 0
## 103 3.61 1.28370774 0 1 8 4 0 1 0 0
## 104 5.45 1.69561553 0 0 33 3 0 1 0 0
## 105 5.00 1.60943794 0 0 30 3 0 1 0 0
## 106 6.73 1.90657520 0 0 23 3 0 1 0 0
## 107 10.05 2.30757260 0 0 24 3 0 1 0 0
## 108 6.54 1.87793720 0 1 4 4 0 1 0 0
## 109 9.20 2.21920347 0 0 40 3 0 1 0 1
## 110 4.62 1.53039467 0 0 1 3 0 1 0 1
## 111 3.37 1.21491265 0 0 36 3 0 1 1 1
## 112 5.54 1.71199453 0 1 7 4 0 1 0 1
## 113 6.11 1.80992675 0 1 7 5 0 1 0 1
## 114 4.99 1.60743582 0 0 14 3 0 1 0 1
## 115 5.88 1.77155674 0 0 7 3 0 1 0 1
## 116 5.66 1.73342383 0 1 11 4 1 1 0 1
## 117 4.00 1.38629436 0 0 6 3 0 1 0 0
## 118 6.87 1.92716408 0 0 23 3 0 1 0 1
## 119 4.37 1.47476304 1 0 19 2 0 1 0 1
## 120 5.77 1.75267208 0 0 30 3 0 1 0 0
## 121 5.77 1.75267208 0 1 21 4 0 1 0 0
## 122 12.31 2.51041198 0 1 9 4 0 1 0 0
## 123 19.08 2.94864058 0 0 29 3 1 1 0 0
## 124 7.55 2.02154756 0 0 9 3 0 1 0 1
## 125 5.90 1.77495241 0 1 18 4 0 1 0 1
## 126 6.37 1.85159945 0 0 26 3 0 1 0 1
## 127 6.15 1.81645215 0 1 21 4 1 1 0 0
## 128 1.92 0.65232515 1 0 12 2 1 1 0 1
## 129 3.27 1.18479002 0 0 11 3 0 1 0 0
## 130 2.18 0.77932489 0 1 18 4 0 1 0 0
## 131 5.62 1.72633159 0 0 22 3 0 1 0 0
## 132 9.29 2.22893858 0 1 38 4 0 1 0 1
## 133 6.41 1.85785925 0 0 10 3 1 1 0 1
## 134 7.46 2.00955534 0 0 22 3 1 1 0 1
## 135 6.59 1.88555336 1 0 16 2 1 1 0 1
## 136 6.73 1.90657520 1 0 20 2 1 1 0 1
## 137 5.61 1.72455072 0 0 14 3 1 1 0 1
## 138 3.65 1.29472721 0 1 14 4 0 1 0 1
## 139 13.01 2.56571841 0 1 15 4 1 1 0 0
## 140 6.97 1.94161522 0 0 18 3 1 1 0 0
## 141 7.69 2.03992081 0 0 21 3 1 1 0 1
## 142 6.73 1.90657520 0 0 15 3 0 1 0 0
## 143 5.13 1.63510573 0 0 10 3 1 1 0 1
## 144 2.14 0.76080590 0 1 6 4 0 1 0 1
## 145 4.53 1.51072204 1 0 8 2 1 1 0 1
## 146 9.10 2.20827436 1 0 18 2 0 1 0 1
## 147 9.78 2.28033948 0 0 15 3 1 1 0 1
## 148 3.63 1.28923273 0 1 11 4 0 1 0 1
## 149 7.21 1.97546899 0 0 20 3 1 0 1 1
## 150 10.62 2.36273909 0 0 18 3 1 1 0 1
## 151 4.62 1.53039467 0 0 10 3 1 1 0 1
## 152 6.54 1.87793720 0 0 26 3 1 1 0 1
## 153 12.12 2.49485707 0 0 35 3 1 0 0 1
## 154 7.21 1.97546899 0 1 30 4 1 1 0 1
## 155 6.25 1.83258152 0 1 6 4 1 0 0 1
## 156 3.85 1.34807312 0 0 13 3 1 1 0 1
## 157 3.03 1.10856259 0 0 10 3 0 0 0 1
## 158 2.01 0.69813472 0 0 16 3 0 1 0 1
## 159 3.90 1.36097658 0 1 5 4 1 1 0 1
## 160 6.35 1.84845483 0 0 11 3 1 1 0 1
## 161 2.00 0.69314718 0 1 6 4 0 1 0 1
## 162 7.69 2.03992081 0 0 22 3 1 1 0 0
## 163 2.63 0.96698391 0 0 4 3 0 1 0 1
## 164 5.00 1.60943794 0 1 15 4 1 1 0 1
## 165 6.54 1.87793720 0 0 7 3 0 1 0 1
## 166 5.19 1.64673376 0 0 10 3 0 1 0 1
## 167 9.75 2.27726722 0 0 8 3 1 1 0 1
## 168 2.81 1.03318441 0 0 14 3 0 1 1 1
## 169 4.62 1.53039467 0 0 11 3 1 1 0 0
## 170 7.33 1.99197555 0 0 34 3 1 1 0 0
## 171 7.69 2.03992081 0 1 12 4 1 1 0 0
## 172 3.26 1.18172717 0 0 10 3 1 1 0 0
## 173 5.77 1.75267208 1 0 11 2 0 1 0 1
## 174 5.59 1.72097933 0 1 8 4 1 1 0 0
## 175 5.13 1.63510573 0 0 28 3 0 1 1 0
## 176 14.74 2.69056487 1 0 24 2 1 1 0 0
## 177 3.67 1.30019164 0 0 8 3 1 1 0 1
## 178 9.73 2.27521396 0 1 13 4 0 1 0 0
## 179 3.46 1.24126863 0 0 4 3 1 1 0 0
## 180 6.37 1.85159945 0 1 43 4 1 1 0 0
## 181 6.58 1.88403475 0 0 30 3 0 1 0 1
## 182 10.15 2.31747365 0 0 16 3 1 1 1 0
## 183 9.23 2.22245908 0 0 19 3 1 1 0 0
## 184 6.75 1.90954256 0 0 21 3 0 0 0 0
## 185 10.22 2.32434654 0 0 26 3 0 1 0 1
## 186 4.76 1.56024766 0 0 8 3 1 1 0 0
## 187 1.76 0.56531382 0 0 24 3 0 1 0 1
## 188 12.73 2.54396129 0 1 15 4 1 1 0 0
## 189 5.85 1.76644170 0 0 26 3 1 1 0 0
## 190 3.17 1.15373158 0 0 6 3 0 0 0 0
## 191 5.99 1.79009140 1 0 19 2 0 1 0 0
## 192 4.62 1.53039467 0 0 4 3 0 1 0 0
## 193 1.80 0.58778661 0 1 23 4 0 1 0 0
## 194 5.95 1.78339124 0 1 25 4 0 1 0 0
## 195 7.46 2.00955534 0 0 18 3 0 1 0 0
## 196 8.70 2.16332293 1 0 27 2 0 1 0 0
## 197 3.75 1.32175589 0 0 16 3 0 1 0 0
## 198 7.69 2.03992081 0 0 20 3 0 1 0 0
## 199 5.24 1.65632141 0 0 12 3 0 0 0 0
## 200 10.10 2.31253552 0 1 35 4 0 1 0 0
## 201 3.75 1.32175589 1 0 36 1 0 1 0 0
## 202 5.79 1.75613225 0 0 35 3 0 1 0 0
## 203 5.06 1.62136650 0 0 5 3 0 1 0 0
## 204 7.96 2.07442904 0 1 15 4 1 1 0 0
## 205 12.82 2.55100632 0 0 44 3 1 1 0 0
## 206 8.46 2.13534927 0 0 28 3 0 1 0 0
## 207 3.76 1.32441890 0 1 2 4 1 1 0 1
## 208 2.86 1.05082154 0 0 43 3 0 1 0 0
## 209 11.81 2.46894670 0 0 30 3 1 1 0 0
## 210 7.69 2.03992081 0 0 26 3 0 1 0 0
## 211 7.21 1.97546899 0 0 40 3 1 0 0 0
## 212 2.88 1.05779028 0 1 7 4 0 1 1 1
## 213 5.73 1.74571550 0 0 10 3 1 1 0 1
## 214 4.49 1.50185263 1 0 37 2 0 1 0 0
## 215 9.57 2.25863314 0 1 27 4 1 1 0 0
## 216 3.61 1.28370774 0 0 5 3 0 1 0 0
## 217 7.77 2.05027008 1 0 14 2 0 1 0 0
## 218 9.23 2.22245908 1 0 16 2 0 1 0 0
## 219 5.58 1.71918881 0 0 4 3 0 1 0 0
## 220 5.34 1.67522573 0 0 39 3 0 1 0 0
## 221 5.17 1.64287269 0 0 32 3 1 1 0 0
## 222 6.41 1.85785925 0 1 16 4 0 1 0 0
## 223 7.40 2.00148010 0 1 10 4 0 1 0 1
## 224 6.75 1.90954256 0 1 15 4 0 1 0 0
## 225 8.46 2.13534927 0 1 10 4 0 1 0 0
## 226 4.61 1.52822793 0 0 10 3 0 1 0 1
## 227 8.61 2.15292430 0 0 12 3 0 0 0 1
## 228 5.24 1.65632141 0 1 4 4 0 1 0 0
## 229 15.38 2.73306799 0 0 12 3 0 1 0 0
## 230 2.37 0.86288989 0 1 15 4 0 1 0 1
## 231 6.72 1.90508807 0 1 37 4 0 1 0 1
## 232 9.40 2.24070954 0 1 23 4 0 1 0 0
## 233 7.84 2.05923891 0 0 15 3 0 1 0 0
## 234 15.00 2.70805025 0 1 10 4 0 1 0 0
## 235 4.70 1.54756248 0 1 14 4 0 1 0 1
## 236 11.28 2.42303133 0 0 17 3 0 1 0 0
## 237 11.97 2.48240352 0 0 24 3 0 1 0 0
## 238 13.99 2.63834286 0 1 13 4 0 1 0 0
## 239 3.00 1.09861231 0 0 5 3 0 1 0 1
## 240 3.41 1.22671235 0 0 20 3 0 1 0 1
## 241 9.62 2.26384425 0 1 29 4 0 1 0 0
## 242 6.73 1.90657520 0 1 10 4 0 1 0 0
## 243 9.62 2.26384425 0 0 36 3 0 1 0 0
## 244 5.19 1.64673376 0 0 15 3 0 1 0 1
## 245 4.62 1.53039467 0 0 26 3 0 1 0 1
## 246 5.03 1.61541998 0 1 13 4 0 1 0 1
## 247 9.84 2.28645563 0 0 26 3 0 1 0 0
## 248 5.29 1.66581821 0 0 9 3 1 1 0 0
## 249 10.99 2.39698577 1 0 40 1 0 1 0 0
## 250 7.08 1.95727384 0 0 28 3 1 0 0 0
## 251 8.17 2.10046887 1 0 12 2 0 1 0 0
## 252 5.88 1.77155674 0 1 25 4 0 1 0 1
## 253 7.92 2.06939125 0 1 18 4 0 1 0 1
## 254 4.44 1.49065435 0 0 9 3 0 1 0 0
## 255 6.64 1.89311194 0 0 9 3 1 1 0 0
## 256 15.80 2.76001000 0 1 30 4 0 0 0 0
## 257 9.23 2.22245908 0 0 40 3 0 1 0 1
## 258 12.82 2.55100632 0 0 36 3 0 1 0 0
## 259 14.18 2.65183258 0 1 30 4 0 1 0 0
## 260 11.54 2.44581914 0 0 27 3 1 1 0 0
## 261 9.40 2.24070954 0 0 22 3 1 1 0 0
## 262 10.39 2.34084392 0 0 40 3 1 1 0 0
## 263 5.54 1.71199453 0 0 40 3 0 1 1 0
## 264 7.86 2.06178665 0 0 21 3 0 1 0 0
## 265 8.17 2.10046887 0 0 36 3 1 0 0 0
## 266 4.05 1.39871693 0 1 16 4 0 1 0 1
## 267 7.81 2.05540490 0 0 36 3 1 0 0 0
## 268 2.56 0.94000721 0 0 18 3 0 1 0 0
## 269 12.98 2.56340957 0 1 11 4 0 1 0 0
## 270 41.67 3.72978139 0 1 16 4 0 0 0 0
## 271 4.81 1.57069707 0 1 12 4 0 1 0 1
## 272 1.32 0.27763179 0 0 2 3 1 1 0 0
## 273 3.71 1.31103194 0 0 4 3 0 1 0 0
## 274 4.81 1.57069707 0 0 21 3 1 1 0 0
## 275 4.68 1.54329813 0 0 4 3 0 1 0 0
## 276 8.21 2.10535288 1 0 32 2 1 1 0 1
## 277 4.03 1.39376640 0 0 7 3 0 1 0 1
## 278 4.81 1.57069707 0 0 5 3 0 0 0 0
## 279 15.63 2.74919224 0 0 23 3 0 1 0 0
## 280 7.69 2.03992081 0 0 23 3 1 1 0 0
## 281 7.17 1.96990561 0 0 14 3 0 1 0 0
## 282 8.08 2.08939195 0 1 13 4 0 1 0 0
## 283 9.03 2.20055223 0 0 28 3 0 1 0 0
## 284 6.75 1.90954256 0 1 35 4 0 1 0 0
## 285 8.44 2.13298225 0 1 16 4 0 1 0 0
## 286 6.00 1.79175949 0 1 12 4 0 1 0 0
## 287 6.15 1.81645215 0 0 7 3 0 1 0 0
## 288 4.10 1.41098690 0 0 25 3 0 1 0 0
## 289 14.17 2.65112710 0 0 22 3 1 1 0 0
## 290 6.41 1.85785925 0 0 22 3 0 1 0 0
## 291 31.09 3.43688631 0 0 32 3 0 0 0 0
## 292 10.29 2.33117247 0 1 35 4 0 1 0 0
## 293 5.06 1.62136650 0 0 28 3 0 1 0 0
## 294 6.37 1.85159945 0 0 18 3 0 1 0 0
## 295 2.98 1.09192336 0 0 11 3 0 1 0 1
## 296 13.28 2.58625913 0 0 40 3 0 1 0 0
## 297 4.20 1.43508446 1 0 10 2 0 1 0 0
## 298 8.92 2.18829584 0 0 40 3 0 1 0 0
## 299 2.63 0.96698391 0 0 7 3 0 1 0 1
## 300 26.24 3.26728487 0 0 24 3 0 1 0 0
## 301 7.54 2.02022219 0 1 36 4 0 1 0 0
## 302 8.65 2.15755939 0 0 34 3 0 1 0 0
## 303 20.99 3.04404616 0 0 44 3 0 1 0 0
## 304 10.26 2.32825279 0 0 44 3 0 1 0 0
## 305 8.55 2.14593124 0 0 30 3 0 1 0 0
## 306 2.08 0.73236787 0 1 23 4 0 1 0 0
## 307 3.00 1.09861231 0 0 5 3 0 0 0 0
## 308 6.01 1.79342484 0 1 25 4 0 1 0 0
## 309 10.05 2.30757260 0 1 14 4 0 1 0 0
## 310 5.73 1.74571550 0 0 5 3 0 1 0 0
## 311 5.93 1.78002417 0 0 20 3 0 1 0 0
## 312 4.49 1.50185263 1 0 1 2 0 1 0 0
## 313 5.66 1.73342383 0 1 13 4 0 1 0 0
## 314 10.19 2.32140684 0 1 15 4 0 1 0 0
## 315 20.99 3.04404616 0 0 35 3 0 1 0 0
## 316 9.05 2.20276475 0 1 20 4 0 1 0 0
## 317 3.92 1.36609173 0 1 12 5 0 1 0 0
## 318 4.28 1.45395303 0 1 7 4 0 1 0 1
## 319 2.75 1.01160097 0 0 7 3 0 1 0 1
## 320 2.88 1.05779028 0 1 32 4 0 1 0 0
## 321 6.41 1.85785925 0 0 13 3 0 1 0 0
## 322 7.69 2.03992081 0 0 15 3 0 1 0 0
## 323 3.75 1.32175589 0 0 33 3 0 0 0 1
## 324 6.41 1.85785925 0 0 48 3 0 1 0 0
## 325 11.54 2.44581914 0 1 38 4 0 1 0 0
## 326 11.40 2.43361330 0 0 28 3 0 1 0 0
## 327 2.20 0.78845739 0 1 18 4 0 1 0 1
## 328 1.65 0.50077528 1 0 24 1 0 1 0 1
## 329 6.59 1.88555336 0 0 27 3 0 1 0 0
## 330 11.06 2.40333509 0 0 24 3 0 1 0 0
## 331 1.63 0.48858002 0 0 16 3 0 1 0 0
## 332 3.58 1.27536273 0 0 44 3 0 1 0 1
## 333 12.31 2.51041198 0 1 14 4 0 1 0 0
## 334 6.66 1.89611948 0 0 16 3 0 1 0 0
## 335 10.53 2.35422826 0 0 18 3 0 1 0 1
## 336 1.92 0.65232515 1 0 18 2 0 1 0 1
## 337 19.23 2.95647144 0 1 15 4 0 1 0 0
## 338 7.21 1.97546899 0 1 41 4 0 1 0 0
## 339 25.64 3.24415350 0 0 28 3 0 1 0 0
## 340 3.85 1.34807312 0 0 24 3 0 1 0 0
## 341 10.99 2.39698577 0 0 13 3 0 1 0 0
## 342 10.02 2.30458307 0 0 22 3 0 1 0 0
## 343 5.31 1.66959178 0 1 18 4 0 1 0 0
## 344 7.19 1.97269118 0 0 40 3 0 1 0 0
## 345 3.82 1.34025037 0 1 17 4 0 1 0 0
## 346 9.62 2.26384425 0 0 17 3 0 1 0 0
## 347 9.62 2.26384425 0 1 20 4 0 1 0 0
## 348 3.85 1.34807312 0 1 30 4 0 1 0 0
## 349 7.50 2.01490307 0 0 30 3 0 1 0 0
## 350 6.92 1.93441582 0 1 22 4 0 1 0 0
## 351 7.69 2.03992081 0 1 7 4 0 1 0 0
## 352 9.23 2.22245908 0 0 23 3 0 1 0 0
## 353 1.27 0.23901689 0 0 8 3 0 1 0 1
## 354 4.82 1.57277393 0 1 31 4 0 1 0 0
## 355 6.75 1.90954256 0 0 30 3 0 1 0 0
## 356 8.55 2.14593124 1 0 26 2 0 1 0 0
## 357 6.41 1.85785925 0 1 24 4 1 1 0 0
## 358 7.44 2.00687075 0 0 12 3 1 1 0 0
## 359 1.32 0.27763179 0 0 37 3 0 1 0 1
## 360 4.81 1.57069707 0 1 16 4 0 1 0 1
## 361 14.35 2.66374993 0 0 23 3 0 1 0 0
## 362 2.40 0.87546879 1 0 9 2 0 1 0 0
## 363 3.08 1.12492955 1 0 14 2 0 1 0 0
## 364 10.66 2.36649847 0 1 8 5 0 1 0 0
## 365 7.69 2.03992081 1 0 41 2 0 1 0 0
## 366 2.54 0.93216407 0 1 5 4 0 1 0 0
## 367 6.38 1.85316813 0 0 30 3 0 1 0 0
## 368 4.81 1.57069707 0 1 12 4 0 1 0 0
## 369 3.82 1.34025037 0 0 24 3 0 1 0 1
## 370 7.40 2.00148010 0 0 19 3 0 1 0 0
## 371 7.69 2.03992081 0 1 20 4 0 1 0 0
## 372 8.24 2.10900021 1 0 23 2 0 1 0 1
## 373 2.63 0.96698391 0 0 3 3 0 1 0 0
## 374 5.31 1.66959178 0 1 20 4 0 1 0 0
## 375 3.00 1.09861231 1 0 45 2 0 1 0 0
## 376 5.70 1.74046612 0 0 31 3 0 1 0 0
## 377 17.49 2.86162925 0 1 23 4 0 1 0 0
## 378 3.89 1.35840917 0 0 7 3 0 1 0 0
## 379 16.14 2.78130054 0 0 23 3 1 1 0 0
## 380 18.32 2.90799332 0 0 25 3 0 1 0 0
## 381 10.42 2.34372711 0 0 28 3 1 0 0 0
## 382 24.73 3.20801711 0 0 36 3 0 1 0 0
## 383 7.61 2.02946329 0 0 14 3 0 1 0 0
## 384 10.00 2.30258512 0 1 41 4 0 1 0 0
## 385 1.68 0.51879376 0 0 15 3 0 1 0 0
## 386 5.10 1.62924051 0 1 34 4 0 0 1 0
## 387 7.72 2.04381442 0 0 20 3 0 1 0 0
## 388 13.46 2.59972239 0 1 20 4 0 1 0 0
## 389 17.48 2.86105728 0 1 35 4 0 1 0 0
## 390 1.92 0.65232515 0 0 25 3 0 1 1 0
## 391 5.09 1.62727785 0 1 38 4 0 1 0 0
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## 1216 1 0 0 0 1 100 12
## 1217 0 0 0 1 1 784 13
## 1218 1 0 0 0 1 4 10
## 1219 0 0 0 1 1 2025 12
## 1220 0 0 1 0 1 81 10
## 1221 1 0 0 0 1 169 12
## 1222 0 1 1 0 1 1600 13
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## 1224 0 0 0 0 1 225 12
## 1225 1 0 0 0 1 144 10
## 1226 0 0 0 0 1 400 17
## 1227 1 0 0 1 1 64 10
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## 1230 1 0 0 0 0 49 14
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## 1241 0 1 0 1 1 144 12
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## 1244 1 0 0 0 0 81 13
## 1245 1 0 1 0 0 225 13
## 1246 1 0 0 1 0 1156 10
## 1247 1 0 0 1 0 64 14
## 1248 0 0 0 1 0 9 17
## 1249 0 0 0 1 0 361 16
## 1250 1 0 1 0 0 100 13
## 1251 1 0 0 0 0 169 10
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## 1253 1 1 1 0 0 441 16
## 1254 0 0 0 0 1 100 5
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## 1258 0 0 0 1 1 1225 12
## 1259 1 0 1 0 1 225 10
## 1260 0 1 0 1 1 576 16b1 <- beauty- Construct a five-number summary table for variables – wage, lwage, exper,educ after grouping the data using the factor variables “looks”, “married” and “female”. Comment on the results
pacman::p_load(tidyverse, lock5Data, ISLR, nycflights13, wooldridge, expss, hablar, rio)## Installing package into 'C:/Users/Marcela Mejia M/AppData/Local/R/win-library/4.2'
## (as 'lib' is unspecified)## Warning: package 'lock5Data' is not available for this version of R
##
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages## Warning: Perhaps you meant 'Lock5Data' ?## Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/4.2:
## no fue posible abrir la URL 'http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/4.2/PACKAGES'## Warning: 'BiocManager' not available. Could not check Bioconductor.
##
## Please use `install.packages('BiocManager')` and then retry.## Warning in p_install(package, character.only = TRUE, ...):## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'lock5Data'## Warning in pacman::p_load(tidyverse, lock5Data, ISLR, nycflights13, wooldridge, : Failed to install/load:
## lock5DataBe <- wooldridge:: beauty
Be## wage lwage belavg abvavg exper looks union goodhlth black female
## 1 5.73 1.74571550 0 1 30 4 0 1 0 1
## 2 4.28 1.45395303 0 0 28 3 0 1 0 1
## 3 7.96 2.07442904 0 1 35 4 0 1 0 1
## 4 11.57 2.44841552 0 0 38 3 0 1 0 0
## 5 11.42 2.43536615 0 0 27 3 0 1 0 0
## 6 3.91 1.36353743 0 0 20 3 0 0 0 1
## 7 8.76 2.17019582 0 0 12 3 0 1 0 0
## 8 7.69 2.03992081 0 1 5 4 1 1 0 0
## 9 5.00 1.60943794 0 0 5 3 0 1 0 1
## 10 3.89 1.35840917 0 0 12 3 0 1 0 1
## 11 3.45 1.23837423 0 1 3 4 0 1 0 1
## 12 4.03 1.39376640 0 1 6 4 0 1 0 0
## 13 5.14 1.63705301 1 0 19 2 0 1 0 0
## 14 3.00 1.09861231 0 0 8 3 0 1 0 0
## 15 7.99 2.07819080 0 1 12 4 0 1 0 0
## 16 6.01 1.79342484 0 1 17 4 0 1 0 0
## 17 5.16 1.64093649 0 0 7 3 0 1 0 0
## 18 11.54 2.44581914 0 1 12 4 0 1 0 0
## 19 10.44 2.34564447 0 1 10 4 0 1 0 1
## 20 7.69 2.03992081 0 0 7 3 0 1 0 1
## 21 7.69 2.03992081 0 1 7 4 1 1 0 0
## 22 6.79 1.91545093 0 0 19 3 0 1 0 0
## 23 6.87 1.92716408 0 0 33 3 0 1 0 0
## 24 17.03 2.83497643 0 0 32 3 0 1 0 0
## 25 10.05 2.30757260 0 1 12 4 0 1 0 0
## 26 15.81 2.76064277 0 1 24 4 0 1 0 0
## 27 14.84 2.69732618 0 1 29 5 0 1 0 0
## 28 19.08 2.94864058 0 1 17 5 0 1 0 0
## 29 8.35 2.12226152 1 0 41 1 0 0 0 0
## 30 9.62 2.26384425 0 0 40 3 0 0 0 0
## 31 5.96 1.78507054 0 0 10 3 0 1 0 0
## 32 5.73 1.74571550 0 0 43 3 0 0 0 0
## 33 6.73 1.90657520 0 1 41 4 0 1 0 0
## 34 8.17 2.10046887 1 0 18 2 0 1 0 0
## 35 12.39 2.51688981 0 1 28 4 0 1 0 0
## 36 9.62 2.26384425 1 0 37 2 0 1 0 0
## 37 4.83 1.57484651 0 1 20 4 0 1 1 0
## 38 7.69 2.03992081 1 0 10 2 1 1 0 0
## 39 12.50 2.52572870 0 0 31 3 0 1 0 0
## 40 2.82 1.03673685 0 0 18 3 0 1 0 0
## 41 12.31 2.51041198 0 0 9 3 1 1 0 0
## 42 13.22 2.58173084 0 0 42 3 0 1 0 0
## 43 16.83 2.82316303 0 1 14 4 0 1 0 0
## 44 11.54 2.44581914 0 0 27 3 0 1 0 0
## 45 4.95 1.59938753 0 0 20 3 0 1 1 1
## 46 7.21 1.97546899 0 0 14 3 0 1 0 0
## 47 11.58 2.44927955 0 0 4 3 0 1 0 0
## 48 15.38 2.73306799 0 0 13 3 0 1 0 0
## 49 6.10 1.80828881 0 0 5 3 0 1 0 0
## 50 7.93 2.07065296 0 0 32 3 0 1 0 0
## 51 9.16 2.21484613 0 1 19 4 1 1 0 1
## 52 6.91 1.93296957 0 1 14 4 0 1 0 1
## 53 20.18 3.00469208 0 0 28 3 0 1 0 0
## 54 9.93 2.29556060 0 0 31 3 1 1 0 0
## 55 7.79 2.05284095 0 1 18 4 0 1 0 0
## 56 5.13 1.63510573 0 0 23 3 0 1 0 0
## 57 10.99 2.39698577 0 1 14 4 0 1 0 0
## 58 6.56 1.88099062 1 0 17 2 0 1 0 0
## 59 10.10 2.31253552 0 0 16 3 0 1 0 0
## 60 10.74 2.37397504 0 0 19 3 0 1 0 0
## 61 16.35 2.79422784 0 0 26 3 0 1 0 0
## 62 4.62 1.53039467 0 1 20 4 0 1 0 1
## 63 7.49 2.01356888 0 0 18 3 0 1 0 0
## 64 19.23 2.95647144 0 0 35 3 0 1 0 0
## 65 4.77 1.56234634 0 1 9 4 0 1 0 1
## 66 7.14 1.96571279 1 0 20 2 0 1 0 1
## 67 14.42 2.66861606 0 1 7 4 0 1 0 0
## 68 9.28 2.22786140 0 1 40 4 0 1 0 0
## 69 29.98 3.40053058 0 0 12 3 0 1 0 0
## 70 32.79 3.49012351 0 1 33 4 0 1 0 0
## 71 9.62 2.26384425 0 0 16 3 0 1 0 0
## 72 8.36 2.12345839 0 0 15 3 0 1 0 1
## 73 21.63 3.07408118 0 0 11 3 0 1 0 0
## 74 15.90 2.76631904 0 1 24 4 0 1 0 0
## 75 5.77 1.75267208 1 0 6 2 0 1 0 0
## 76 20.99 3.04404616 1 0 40 2 0 1 0 0
## 77 23.32 3.14931130 0 1 15 5 0 1 0 0
## 78 4.97 1.60341978 0 1 9 4 0 1 0 0
## 79 6.89 1.93007112 1 0 16 2 0 1 0 0
## 80 5.73 1.74571550 1 0 12 2 0 1 0 1
## 81 5.19 1.64673376 0 0 8 3 0 1 0 0
## 82 7.79 2.05284095 0 0 25 3 1 1 0 1
## 83 8.79 2.17361474 0 0 24 3 1 1 0 1
## 84 9.32 2.23216271 0 1 25 4 1 1 0 1
## 85 8.33 2.11986351 0 1 7 4 0 1 0 1
## 86 10.12 2.31451368 0 0 40 3 0 1 1 1
## 87 1.98 0.68309683 0 0 29 3 0 0 0 1
## 88 8.24 2.10900021 0 1 13 4 0 1 0 1
## 89 5.05 1.61938822 0 1 5 4 0 1 0 1
## 90 6.55 1.87946510 0 0 10 3 0 1 0 1
## 91 4.47 1.49738836 0 0 25 3 1 1 0 0
## 92 7.96 2.07442904 0 0 10 3 0 1 0 1
## 93 6.25 1.83258152 1 0 20 2 0 1 0 0
## 94 4.13 1.41827738 0 0 7 3 0 1 0 0
## 95 6.73 1.90657520 1 0 25 2 0 1 0 1
## 96 7.69 2.03992081 0 0 8 3 0 1 0 1
## 97 4.35 1.47017586 1 0 8 2 0 1 0 1
## 98 7.69 2.03992081 0 0 28 3 0 1 0 0
## 99 4.81 1.57069707 1 0 37 2 0 1 0 0
## 100 5.24 1.65632141 0 0 31 3 0 1 0 0
## 101 2.14 0.76080590 0 1 24 4 0 1 0 0
## 102 4.49 1.50185263 0 1 33 4 0 1 0 0
## 103 3.61 1.28370774 0 1 8 4 0 1 0 0
## 104 5.45 1.69561553 0 0 33 3 0 1 0 0
## 105 5.00 1.60943794 0 0 30 3 0 1 0 0
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## 108 6.54 1.87793720 0 1 4 4 0 1 0 0
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## 113 6.11 1.80992675 0 1 7 5 0 1 0 1
## 114 4.99 1.60743582 0 0 14 3 0 1 0 1
## 115 5.88 1.77155674 0 0 7 3 0 1 0 1
## 116 5.66 1.73342383 0 1 11 4 1 1 0 1
## 117 4.00 1.38629436 0 0 6 3 0 1 0 0
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## 119 4.37 1.47476304 1 0 19 2 0 1 0 1
## 120 5.77 1.75267208 0 0 30 3 0 1 0 0
## 121 5.77 1.75267208 0 1 21 4 0 1 0 0
## 122 12.31 2.51041198 0 1 9 4 0 1 0 0
## 123 19.08 2.94864058 0 0 29 3 1 1 0 0
## 124 7.55 2.02154756 0 0 9 3 0 1 0 1
## 125 5.90 1.77495241 0 1 18 4 0 1 0 1
## 126 6.37 1.85159945 0 0 26 3 0 1 0 1
## 127 6.15 1.81645215 0 1 21 4 1 1 0 0
## 128 1.92 0.65232515 1 0 12 2 1 1 0 1
## 129 3.27 1.18479002 0 0 11 3 0 1 0 0
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## 138 3.65 1.29472721 0 1 14 4 0 1 0 1
## 139 13.01 2.56571841 0 1 15 4 1 1 0 0
## 140 6.97 1.94161522 0 0 18 3 1 1 0 0
## 141 7.69 2.03992081 0 0 21 3 1 1 0 1
## 142 6.73 1.90657520 0 0 15 3 0 1 0 0
## 143 5.13 1.63510573 0 0 10 3 1 1 0 1
## 144 2.14 0.76080590 0 1 6 4 0 1 0 1
## 145 4.53 1.51072204 1 0 8 2 1 1 0 1
## 146 9.10 2.20827436 1 0 18 2 0 1 0 1
## 147 9.78 2.28033948 0 0 15 3 1 1 0 1
## 148 3.63 1.28923273 0 1 11 4 0 1 0 1
## 149 7.21 1.97546899 0 0 20 3 1 0 1 1
## 150 10.62 2.36273909 0 0 18 3 1 1 0 1
## 151 4.62 1.53039467 0 0 10 3 1 1 0 1
## 152 6.54 1.87793720 0 0 26 3 1 1 0 1
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## 154 7.21 1.97546899 0 1 30 4 1 1 0 1
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## 1259 1 0 1 0 1 225 10
## 1260 0 1 0 1 1 576 16Be %>% group_by(looks, married, female)## # A tibble: 1,260 × 17
## # Groups: looks, married, female [20]
## wage lwage belavg abvavg exper looks union goodhlth black female married
## <dbl> <dbl> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 5.73 1.75 0 1 30 4 0 1 0 1 1
## 2 4.28 1.45 0 0 28 3 0 1 0 1 1
## 3 7.96 2.07 0 1 35 4 0 1 0 1 0
## 4 11.6 2.45 0 0 38 3 0 1 0 0 1
## 5 11.4 2.44 0 0 27 3 0 1 0 0 1
## 6 3.91 1.36 0 0 20 3 0 0 0 1 1
## 7 8.76 2.17 0 0 12 3 0 1 0 0 1
## 8 7.69 2.04 0 1 5 4 1 1 0 0 0
## 9 5 1.61 0 0 5 3 0 1 0 1 0
## 10 3.89 1.36 0 0 12 3 0 1 0 1 0
## # … with 1,250 more rows, and 6 more variables: south <int>, bigcity <int>,
## # smllcity <int>, service <int>, expersq <int>, educ <int>Be %>% dplyr::summarise(
across(c("wage", "lwage", "exper", "educ"), Mean = mean_, Median = median_, SD = sd_, Min = min_, Max = max_)
)## wage lwage exper educ
## 1 5.73 1.74571550 30 14
## 2 4.28 1.45395303 28 12
## 3 7.96 2.07442904 35 10
## 4 11.57 2.44841552 38 16
## 5 11.42 2.43536615 27 16
## 6 3.91 1.36353743 20 12
## 7 8.76 2.17019582 12 16
## 8 7.69 2.03992081 5 16
## 9 5.00 1.60943794 5 16
## 10 3.89 1.35840917 12 12
## 11 3.45 1.23837423 3 12
## 12 4.03 1.39376640 6 16
## 13 5.14 1.63705301 19 17
## 14 3.00 1.09861231 8 16
## 15 7.99 2.07819080 12 16
## 16 6.01 1.79342484 17 16
## 17 5.16 1.64093649 7 17
## 18 11.54 2.44581914 12 17
## 19 10.44 2.34564447 10 17
## 20 7.69 2.03992081 7 16
## 21 7.69 2.03992081 7 17
## 22 6.79 1.91545093 19 14
## 23 6.87 1.92716408 33 12
## 24 17.03 2.83497643 32 13
## 25 10.05 2.30757260 12 13
## 26 15.81 2.76064277 24 17
## 27 14.84 2.69732618 29 13
## 28 19.08 2.94864058 17 17
## 29 8.35 2.12226152 41 16
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## 31 5.96 1.78507054 10 14
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## 975 3.41 1.22671235 4 14
## 976 6.92 1.93441582 3 12
## 977 3.75 1.32175589 12 12
## 978 7.96 2.07442904 5 12
## 979 3.75 1.32175589 6 8
## 980 4.28 1.45395303 7 12
## 981 2.31 0.83724749 35 5
## 982 3.80 1.33500111 15 12
## 983 4.09 1.40854502 5 12
## 984 3.85 1.34807312 12 12
## 985 3.75 1.32175589 23 10
## 986 3.00 1.09861231 10 12
## 987 3.96 1.37624407 1 16
## 988 8.55 2.14593124 20 12
## 989 3.75 1.32175589 14 10
## 990 6.00 1.79175949 1 8
## 991 4.81 1.57069707 6 12
## 992 2.50 0.91629076 26 12
## 993 4.32 1.46325541 18 10
## 994 1.27 0.23901689 4 12
## 995 5.77 1.75267208 13 12
## 996 4.20 1.43508446 33 10
## 997 13.74 2.62031126 28 12
## 998 2.56 0.94000721 4 16
## 999 5.13 1.63510573 42 13
## 1000 7.21 1.97546899 39 5
## 1001 4.24 1.44456327 7 13
## 1002 4.10 1.41098690 38 12
## 1003 8.50 2.14006615 19 12
## 1004 5.94 1.78170919 29 12
## 1005 10.54 2.35517764 35 10
## 1006 3.50 1.25276291 18 10
## 1007 5.95 1.78339124 8 13
## 1008 8.89 2.18492699 20 13
## 1009 5.73 1.74571550 34 5
## 1010 1.05 0.04879012 29 5
## 1011 3.85 1.34807312 8 13
## 1012 2.88 1.05779028 4 12
## 1013 8.44 2.13298225 15 12
## 1014 3.61 1.28370774 6 12
## 1015 5.77 1.75267208 20 12
## 1016 3.33 1.20297229 35 8
## 1017 7.96 2.07442904 44 5
## 1018 4.49 1.50185263 17 10
## 1019 2.38 0.86710054 3 13
## 1020 5.08 1.62531126 40 12
## 1021 5.31 1.66959178 18 12
## 1022 5.31 1.66959178 23 12
## 1023 6.54 1.87793720 10 10
## 1024 7.43 2.00552583 18 10
## 1025 5.29 1.66581821 42 12
## 1026 11.09 2.40604377 38 8
## 1027 3.33 1.20297229 5 13
## 1028 3.27 1.18479002 25 8
## 1029 3.85 1.34807312 26 8
## 1030 8.04 2.08442903 11 12
## 1031 4.62 1.53039467 16 10
## 1032 7.96 2.07442904 40 5
## 1033 4.85 1.57897866 7 12
## 1034 6.97 1.94161522 17 12
## 1035 4.81 1.57069707 44 12
## 1036 3.02 1.10525680 6 12
## 1037 3.98 1.38128185 8 12
## 1038 5.56 1.71559811 31 12
## 1039 7.32 1.99061036 26 12
## 1040 7.69 2.03992081 20 10
## 1041 6.29 1.83896101 22 13
## 1042 9.62 2.26384425 11 12
## 1043 7.63 2.03208780 8 13
## 1044 4.29 1.45628667 40 5
## 1045 25.64 3.24415350 13 10
## 1046 4.24 1.44456327 3 12
## 1047 3.87 1.35325444 10 12
## 1048 5.73 1.74571550 18 10
## 1049 3.50 1.25276291 5 12
## 1050 5.05 1.61938822 16 10
## 1051 7.96 2.07442904 10 12
## 1052 7.96 2.07442904 10 12
## 1053 3.41 1.22671235 3 12
## 1054 5.73 1.74571550 40 8
## 1055 8.89 2.18492699 35 12
## 1056 4.81 1.57069707 28 10
## 1057 4.81 1.57069707 16 14
## 1058 3.75 1.32175589 19 12
## 1059 13.41 2.59600067 11 10
## 1060 5.09 1.62727785 10 12
## 1061 2.88 1.05779028 4 12
## 1062 3.21 1.16627097 2 14
## 1063 4.55 1.51512730 5 12
## 1064 5.77 1.75267208 31 10
## 1065 8.85 2.18041754 28 14
## 1066 4.41 1.48387468 9 10
## 1067 4.27 1.45161378 12 13
## 1068 10.12 2.31451368 17 13
## 1069 5.29 1.66581821 37 13
## 1070 5.73 1.74571550 35 8
## 1071 7.63 2.03208780 35 12
## 1072 7.69 2.03992081 10 13
## 1073 6.08 1.80500472 42 12
## 1074 7.49 2.01356888 27 12
## 1075 6.37 1.85159945 5 13
## 1076 6.41 1.85785925 5 12
## 1077 1.98 0.68309683 3 12
## 1078 3.85 1.34807312 37 5
## 1079 7.05 1.95302761 14 12
## 1080 5.13 1.63510573 21 13
## 1081 6.35 1.84845483 7 10
## 1082 6.69 1.90061390 27 12
## 1083 4.62 1.53039467 1 13
## 1084 5.02 1.61342990 31 13
## 1085 4.23 1.44220197 42 5
## 1086 4.33 1.46556747 45 5
## 1087 6.54 1.87793720 34 10
## 1088 7.13 1.96431124 9 13
## 1089 3.85 1.34807312 3 10
## 1090 2.17 0.77472723 2 10
## 1091 5.29 1.66581821 36 10
## 1092 6.49 1.87026250 27 12
## 1093 10.19 2.32140684 39 12
## 1094 5.79 1.75613225 9 16
## 1095 3.82 1.34025037 47 12
## 1096 12.54 2.52892351 13 12
## 1097 7.69 2.03992081 28 10
## 1098 8.55 2.14593124 26 12
## 1099 5.92 1.77833641 34 12
## 1100 13.74 2.62031126 28 12
## 1101 2.12 0.75141603 17 14
## 1102 16.14 2.78130054 10 13
## 1103 1.32 0.27763179 21 10
## 1104 1.79 0.58221561 15 12
## 1105 2.32 0.84156716 15 13
## 1106 3.70 1.30833280 11 12
## 1107 6.75 1.90954256 37 10
## 1108 8.44 2.13298225 43 5
## 1109 26.24 3.26728487 37 12
## 1110 8.88 2.18380165 24 12
## 1111 2.50 0.91629076 17 10
## 1112 7.28 1.98513091 16 12
## 1113 7.84 2.05923891 36 12
## 1114 26.24 3.26728487 37 12
## 1115 6.33 1.84530020 38 8
## 1116 4.16 1.42551506 9 12
## 1117 3.21 1.16627097 36 5
## 1118 2.02 0.70309752 41 5
## 1119 9.23 2.22245908 10 12
## 1120 2.68 0.98581684 33 5
## 1121 8.65 2.15755939 5 14
## 1122 2.08 0.73236787 1 12
## 1123 6.25 1.83258152 47 5
## 1124 1.92 0.65232515 8 12
## 1125 3.08 1.12492955 6 10
## 1126 2.11 0.74668789 12 12
## 1127 3.46 1.24126863 26 5
## 1128 4.81 1.57069707 14 10
## 1129 3.37 1.21491265 13 5
## 1130 5.77 1.75267208 39 5
## 1131 3.34 1.20597076 10 13
## 1132 7.26 1.98237991 27 10
## 1133 3.73 1.31640828 5 13
## 1134 2.21 0.79299253 6 12
## 1135 2.92 1.07158363 32 8
## 1136 3.53 1.26129782 1 8
## 1137 1.80 0.58778661 8 12
## 1138 2.60 0.95551139 15 10
## 1139 1.17 0.15700372 1 12
## 1140 1.88 0.63127178 25 10
## 1141 2.88 1.05779028 15 10
## 1142 1.92 0.65232515 7 12
## 1143 1.88 0.63127178 1 12
## 1144 3.37 1.21491265 12 12
## 1145 1.82 0.59883654 3 10
## 1146 1.46 0.37843648 2 10
## 1147 2.26 0.81536484 3 10
## 1148 2.07 0.72754860 2 12
## 1149 3.54 1.26412666 16 13
## 1150 1.26 0.23111172 32 5
## 1151 2.26 0.81536484 20 10
## 1152 3.95 1.37371564 17 12
## 1153 2.65 0.97455966 15 5
## 1154 3.75 1.32175589 19 8
## 1155 2.26 0.81536484 37 5
## 1156 2.19 0.78390157 29 10
## 1157 2.19 0.78390157 5 13
## 1158 3.21 1.16627097 7 5
## 1159 2.47 0.90421814 23 5
## 1160 3.00 1.09861231 5 12
## 1161 3.75 1.32175589 29 8
## 1162 2.00 0.69314718 13 12
## 1163 2.26 0.81536484 10 12
## 1164 2.88 1.05779028 12 13
## 1165 4.69 1.54543257 7 12
## 1166 3.95 1.37371564 9 12
## 1167 6.54 1.87793720 4 12
## 1168 3.57 1.27256560 26 12
## 1169 5.31 1.66959178 15 17
## 1170 2.63 0.96698391 19 10
## 1171 4.33 1.46556747 22 16
## 1172 2.25 0.81093019 12 5
## 1173 2.75 1.01160097 11 10
## 1174 3.29 1.19088757 6 12
## 1175 5.77 1.75267208 13 14
## 1176 4.74 1.55603707 8 13
## 1177 4.12 1.41585314 7 13
## 1178 2.89 1.06125653 12 10
## 1179 6.96 1.94017947 11 13
## 1180 6.41 1.85785925 41 12
## 1181 4.28 1.45395303 7 12
## 1182 1.28 0.24686006 9 13
## 1183 2.56 0.94000721 40 10
## 1184 4.62 1.53039467 7 14
## 1185 1.98 0.68309683 9 12
## 1186 1.92 0.65232515 13 13
## 1187 1.80 0.58778661 33 13
## 1188 1.98 0.68309683 17 14
## 1189 2.00 0.69314718 3 13
## 1190 2.11 0.74668789 8 16
## 1191 2.11 0.74668789 3 10
## 1192 2.77 1.01884735 9 13
## 1193 3.46 1.24126863 30 10
## 1194 6.00 1.79175949 5 13
## 1195 4.81 1.57069707 13 12
## 1196 2.33 0.84586823 5 12
## 1197 3.57 1.27256560 9 16
## 1198 1.98 0.68309683 8 13
## 1199 5.13 1.63510573 20 10
## 1200 4.47 1.49738836 3 13
## 1201 2.07 0.72754860 5 12
## 1202 6.97 1.94161522 20 13
## 1203 1.98 0.68309683 2 12
## 1204 4.62 1.53039467 7 12
## 1205 3.21 1.16627097 5 13
## 1206 1.80 0.58778661 46 14
## 1207 4.62 1.53039467 10 12
## 1208 6.11 1.80992675 27 12
## 1209 5.73 1.74571550 39 16
## 1210 3.57 1.27256560 13 13
## 1211 1.58 0.45742488 27 12
## 1212 4.21 1.43746269 7 12
## 1213 1.75 0.55961579 18 12
## 1214 2.15 0.76546788 6 12
## 1215 1.02 0.01980261 11 13
## 1216 2.40 0.87546879 10 12
## 1217 2.83 1.04027665 28 13
## 1218 3.29 1.19088757 2 10
## 1219 1.98 0.68309683 45 12
## 1220 23.16 3.14242673 9 10
## 1221 5.49 1.70292819 13 12
## 1222 3.00 1.09861231 40 13
## 1223 3.29 1.19088757 10 12
## 1224 2.88 1.05779028 15 12
## 1225 2.63 0.96698391 12 10
## 1226 5.82 1.76130033 20 17
## 1227 1.09 0.08617773 8 10
## 1228 1.28 0.24686006 13 12
## 1229 6.25 1.83258152 3 12
## 1230 2.88 1.05779028 7 14
## 1231 7.69 2.03992081 18 12
## 1232 7.14 1.96571279 13 12
## 1233 6.93 1.93585980 36 12
## 1234 1.56 0.44468579 4 12
## 1235 1.98 0.68309683 8 10
## 1236 8.75 2.16905379 24 16
## 1237 4.81 1.57069707 31 12
## 1238 4.71 1.54968786 16 13
## 1239 4.87 1.58309388 10 13
## 1240 4.62 1.53039467 10 13
## 1241 2.40 0.87546879 12 12
## 1242 5.34 1.67522573 20 12
## 1243 4.21 1.43746269 13 12
## 1244 4.72 1.55180871 9 13
## 1245 12.82 2.55100632 15 13
## 1246 6.73 1.90657520 34 10
## 1247 5.39 1.68454540 8 14
## 1248 5.31 1.66959178 3 17
## 1249 10.58 2.35896540 19 16
## 1250 8.65 2.15755939 10 13
## 1251 4.17 1.42791605 13 10
## 1252 6.81 1.91839206 7 12
## 1253 9.62 2.26384425 21 16
## 1254 1.22 0.19885089 10 5
## 1255 1.79 0.58221561 20 8
## 1256 1.61 0.47623420 25 12
## 1257 1.68 0.51879376 4 12
## 1258 3.29 1.19088757 35 12
## 1259 2.31 0.83724749 15 10
## 1260 1.92 0.65232515 24 16rstatix::get_summary_stats(Be, type = "five_number") ## # A tibble: 17 × 7
## variable n min max q1 median q3
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 abvavg 1260 0 1 0 0 1
## 2 belavg 1260 0 1 0 0 0
## 3 bigcity 1260 0 1 0 0 0
## 4 black 1260 0 1 0 0 0
## 5 educ 1260 5 17 12 12 13
## 6 exper 1260 0 48 8 15 27
## 7 expersq 1260 0 2304 64 225 729
## 8 female 1260 0 1 0 0 1
## 9 goodhlth 1260 0 1 1 1 1
## 10 looks 1260 1 5 3 3 4
## 11 lwage 1260 0.02 4.35 1.31 1.67 2.04
## 12 married 1260 0 1 0 1 1
## 13 service 1260 0 1 0 0 1
## 14 smllcity 1260 0 1 0 0 1
## 15 south 1260 0 1 0 0 0
## 16 union 1260 0 1 0 0 1
## 17 wage 1260 1.02 77.7 3.71 5.3 7.70summary(Be)## wage lwage belavg abvavg
## Min. : 1.020 Min. :0.0198 Min. :0.000 Min. :0.000
## 1st Qu.: 3.708 1st Qu.:1.3104 1st Qu.:0.000 1st Qu.:0.000
## Median : 5.300 Median :1.6677 Median :0.000 Median :0.000
## Mean : 6.307 Mean :1.6588 Mean :0.123 Mean :0.304
## 3rd Qu.: 7.695 3rd Qu.:2.0406 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :77.720 Max. :4.3531 Max. :1.000 Max. :1.000
## exper looks union goodhlth
## Min. : 0.00 Min. :1.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 8.00 1st Qu.:3.000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :15.00 Median :3.000 Median :0.0000 Median :1.0000
## Mean :18.21 Mean :3.186 Mean :0.2722 Mean :0.9333
## 3rd Qu.:27.00 3rd Qu.:4.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :48.00 Max. :5.000 Max. :1.0000 Max. :1.0000
## black female married south
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.000 Median :1.0000 Median :0.0000
## Mean :0.07381 Mean :0.346 Mean :0.6913 Mean :0.1746
## 3rd Qu.:0.00000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.000 Max. :1.0000 Max. :1.0000
## bigcity smllcity service expersq
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. : 0.0
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 64.0
## Median :0.000 Median :0.0000 Median :0.0000 Median : 225.0
## Mean :0.219 Mean :0.4667 Mean :0.2738 Mean : 474.5
## 3rd Qu.:0.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 729.0
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :2304.0
## educ
## Min. : 5.00
## 1st Qu.:12.00
## Median :12.00
## Mean :12.56
## 3rd Qu.:13.00
## Max. :17.00- Using a suitable plot, comment on whether there are differences in wages between individuals with different ratings on their physical appearance. Color the boxplot by the variable female. Interpret the results.
b3<- wooldridge::beauty %>% filter(female == 1)
b3 %>%
#ggplot(aes(looks, wage, colour=female )) + geom_bar() + geom_lines()
ggplot(aes(x =looks, y = wage, colour = female)) + geom_col(aes(fill = female))
- Estimate a linear regression model based on equation (1) for the entire sample. Interpret the results. How does the coefficient change after removing the two control variables educ and exper
pacman::p_load(tidyverse, car, wooldridge, jtools, hablar, broom,gvlma)
wooldridge::beauty## wage lwage belavg abvavg exper looks union goodhlth black female
## 1 5.73 1.74571550 0 1 30 4 0 1 0 1
## 2 4.28 1.45395303 0 0 28 3 0 1 0 1
## 3 7.96 2.07442904 0 1 35 4 0 1 0 1
## 4 11.57 2.44841552 0 0 38 3 0 1 0 0
## 5 11.42 2.43536615 0 0 27 3 0 1 0 0
## 6 3.91 1.36353743 0 0 20 3 0 0 0 1
## 7 8.76 2.17019582 0 0 12 3 0 1 0 0
## 8 7.69 2.03992081 0 1 5 4 1 1 0 0
## 9 5.00 1.60943794 0 0 5 3 0 1 0 1
## 10 3.89 1.35840917 0 0 12 3 0 1 0 1
## 11 3.45 1.23837423 0 1 3 4 0 1 0 1
## 12 4.03 1.39376640 0 1 6 4 0 1 0 0
## 13 5.14 1.63705301 1 0 19 2 0 1 0 0
## 14 3.00 1.09861231 0 0 8 3 0 1 0 0
## 15 7.99 2.07819080 0 1 12 4 0 1 0 0
## 16 6.01 1.79342484 0 1 17 4 0 1 0 0
## 17 5.16 1.64093649 0 0 7 3 0 1 0 0
## 18 11.54 2.44581914 0 1 12 4 0 1 0 0
## 19 10.44 2.34564447 0 1 10 4 0 1 0 1
## 20 7.69 2.03992081 0 0 7 3 0 1 0 1
## 21 7.69 2.03992081 0 1 7 4 1 1 0 0
## 22 6.79 1.91545093 0 0 19 3 0 1 0 0
## 23 6.87 1.92716408 0 0 33 3 0 1 0 0
## 24 17.03 2.83497643 0 0 32 3 0 1 0 0
## 25 10.05 2.30757260 0 1 12 4 0 1 0 0
## 26 15.81 2.76064277 0 1 24 4 0 1 0 0
## 27 14.84 2.69732618 0 1 29 5 0 1 0 0
## 28 19.08 2.94864058 0 1 17 5 0 1 0 0
## 29 8.35 2.12226152 1 0 41 1 0 0 0 0
## 30 9.62 2.26384425 0 0 40 3 0 0 0 0
## 31 5.96 1.78507054 0 0 10 3 0 1 0 0
## 32 5.73 1.74571550 0 0 43 3 0 0 0 0
## 33 6.73 1.90657520 0 1 41 4 0 1 0 0
## 34 8.17 2.10046887 1 0 18 2 0 1 0 0
## 35 12.39 2.51688981 0 1 28 4 0 1 0 0
## 36 9.62 2.26384425 1 0 37 2 0 1 0 0
## 37 4.83 1.57484651 0 1 20 4 0 1 1 0
## 38 7.69 2.03992081 1 0 10 2 1 1 0 0
## 39 12.50 2.52572870 0 0 31 3 0 1 0 0
## 40 2.82 1.03673685 0 0 18 3 0 1 0 0
## 41 12.31 2.51041198 0 0 9 3 1 1 0 0
## 42 13.22 2.58173084 0 0 42 3 0 1 0 0
## 43 16.83 2.82316303 0 1 14 4 0 1 0 0
## 44 11.54 2.44581914 0 0 27 3 0 1 0 0
## 45 4.95 1.59938753 0 0 20 3 0 1 1 1
## 46 7.21 1.97546899 0 0 14 3 0 1 0 0
## 47 11.58 2.44927955 0 0 4 3 0 1 0 0
## 48 15.38 2.73306799 0 0 13 3 0 1 0 0
## 49 6.10 1.80828881 0 0 5 3 0 1 0 0
## 50 7.93 2.07065296 0 0 32 3 0 1 0 0
## 51 9.16 2.21484613 0 1 19 4 1 1 0 1
## 52 6.91 1.93296957 0 1 14 4 0 1 0 1
## 53 20.18 3.00469208 0 0 28 3 0 1 0 0
## 54 9.93 2.29556060 0 0 31 3 1 1 0 0
## 55 7.79 2.05284095 0 1 18 4 0 1 0 0
## 56 5.13 1.63510573 0 0 23 3 0 1 0 0
## 57 10.99 2.39698577 0 1 14 4 0 1 0 0
## 58 6.56 1.88099062 1 0 17 2 0 1 0 0
## 59 10.10 2.31253552 0 0 16 3 0 1 0 0
## 60 10.74 2.37397504 0 0 19 3 0 1 0 0
## 61 16.35 2.79422784 0 0 26 3 0 1 0 0
## 62 4.62 1.53039467 0 1 20 4 0 1 0 1
## 63 7.49 2.01356888 0 0 18 3 0 1 0 0
## 64 19.23 2.95647144 0 0 35 3 0 1 0 0
## 65 4.77 1.56234634 0 1 9 4 0 1 0 1
## 66 7.14 1.96571279 1 0 20 2 0 1 0 1
## 67 14.42 2.66861606 0 1 7 4 0 1 0 0
## 68 9.28 2.22786140 0 1 40 4 0 1 0 0
## 69 29.98 3.40053058 0 0 12 3 0 1 0 0
## 70 32.79 3.49012351 0 1 33 4 0 1 0 0
## 71 9.62 2.26384425 0 0 16 3 0 1 0 0
## 72 8.36 2.12345839 0 0 15 3 0 1 0 1
## 73 21.63 3.07408118 0 0 11 3 0 1 0 0
## 74 15.90 2.76631904 0 1 24 4 0 1 0 0
## 75 5.77 1.75267208 1 0 6 2 0 1 0 0
## 76 20.99 3.04404616 1 0 40 2 0 1 0 0
## 77 23.32 3.14931130 0 1 15 5 0 1 0 0
## 78 4.97 1.60341978 0 1 9 4 0 1 0 0
## 79 6.89 1.93007112 1 0 16 2 0 1 0 0
## 80 5.73 1.74571550 1 0 12 2 0 1 0 1
## 81 5.19 1.64673376 0 0 8 3 0 1 0 0
## 82 7.79 2.05284095 0 0 25 3 1 1 0 1
## 83 8.79 2.17361474 0 0 24 3 1 1 0 1
## 84 9.32 2.23216271 0 1 25 4 1 1 0 1
## 85 8.33 2.11986351 0 1 7 4 0 1 0 1
## 86 10.12 2.31451368 0 0 40 3 0 1 1 1
## 87 1.98 0.68309683 0 0 29 3 0 0 0 1
## 88 8.24 2.10900021 0 1 13 4 0 1 0 1
## 89 5.05 1.61938822 0 1 5 4 0 1 0 1
## 90 6.55 1.87946510 0 0 10 3 0 1 0 1
## 91 4.47 1.49738836 0 0 25 3 1 1 0 0
## 92 7.96 2.07442904 0 0 10 3 0 1 0 1
## 93 6.25 1.83258152 1 0 20 2 0 1 0 0
## 94 4.13 1.41827738 0 0 7 3 0 1 0 0
## 95 6.73 1.90657520 1 0 25 2 0 1 0 1
## 96 7.69 2.03992081 0 0 8 3 0 1 0 1
## 97 4.35 1.47017586 1 0 8 2 0 1 0 1
## 98 7.69 2.03992081 0 0 28 3 0 1 0 0
## 99 4.81 1.57069707 1 0 37 2 0 1 0 0
## 100 5.24 1.65632141 0 0 31 3 0 1 0 0
## 101 2.14 0.76080590 0 1 24 4 0 1 0 0
## 102 4.49 1.50185263 0 1 33 4 0 1 0 0
## 103 3.61 1.28370774 0 1 8 4 0 1 0 0
## 104 5.45 1.69561553 0 0 33 3 0 1 0 0
## 105 5.00 1.60943794 0 0 30 3 0 1 0 0
## 106 6.73 1.90657520 0 0 23 3 0 1 0 0
## 107 10.05 2.30757260 0 0 24 3 0 1 0 0
## 108 6.54 1.87793720 0 1 4 4 0 1 0 0
## 109 9.20 2.21920347 0 0 40 3 0 1 0 1
## 110 4.62 1.53039467 0 0 1 3 0 1 0 1
## 111 3.37 1.21491265 0 0 36 3 0 1 1 1
## 112 5.54 1.71199453 0 1 7 4 0 1 0 1
## 113 6.11 1.80992675 0 1 7 5 0 1 0 1
## 114 4.99 1.60743582 0 0 14 3 0 1 0 1
## 115 5.88 1.77155674 0 0 7 3 0 1 0 1
## 116 5.66 1.73342383 0 1 11 4 1 1 0 1
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## 1232 1 0 0 1 0 169 12
## 1233 1 1 0 1 0 1296 12
## 1234 0 0 0 1 0 16 12
## 1235 0 0 0 1 1 64 10
## 1236 0 0 0 1 1 576 16
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## 1239 1 0 0 0 0 100 13
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## 1244 1 0 0 0 0 81 13
## 1245 1 0 1 0 0 225 13
## 1246 1 0 0 1 0 1156 10
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## 1253 1 1 1 0 0 441 16
## 1254 0 0 0 0 1 100 5
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## 1257 1 0 0 1 1 16 12
## 1258 0 0 0 1 1 1225 12
## 1259 1 0 1 0 1 225 10
## 1260 0 1 0 1 1 576 16b1=wooldridge::beauty
#Linear regression model
lm(lwage ~ looks + educ + exper, data = b1) %>% summary()##
## Call:
## lm(formula = lwage ~ looks + educ + exper, data = b1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.68796 -0.31365 0.00695 0.33698 2.78744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.203462 0.104171 1.953 0.05102 .
## looks 0.058226 0.022271 2.614 0.00904 **
## educ 0.073876 0.005844 12.642 < 2e-16 ***
## exper 0.018768 0.001282 14.638 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5302 on 1256 degrees of freedom
## Multiple R-squared: 0.2065, Adjusted R-squared: 0.2046
## F-statistic: 109 on 3 and 1256 DF, p-value: < 2.2e-16#Linear regression model without educ and exper
lm(lwage ~ looks,data = b1)%>% summary()##
## Call:
## lm(formula = lwage ~ looks, data = b1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.62953 -0.37853 0.01576 0.39058 2.65282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.49648 0.07961 18.798 <2e-16 ***
## looks 0.05095 0.02443 2.085 0.0372 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5937 on 1258 degrees of freedom
## Multiple R-squared: 0.003445, Adjusted R-squared: 0.002653
## F-statistic: 4.349 on 1 and 1258 DF, p-value: 0.03723- Estimate two separate regressions based on equation (1) for sub-samples containing only males and females.
wooldridge::beauty## wage lwage belavg abvavg exper looks union goodhlth black female
## 1 5.73 1.74571550 0 1 30 4 0 1 0 1
## 2 4.28 1.45395303 0 0 28 3 0 1 0 1
## 3 7.96 2.07442904 0 1 35 4 0 1 0 1
## 4 11.57 2.44841552 0 0 38 3 0 1 0 0
## 5 11.42 2.43536615 0 0 27 3 0 1 0 0
## 6 3.91 1.36353743 0 0 20 3 0 0 0 1
## 7 8.76 2.17019582 0 0 12 3 0 1 0 0
## 8 7.69 2.03992081 0 1 5 4 1 1 0 0
## 9 5.00 1.60943794 0 0 5 3 0 1 0 1
## 10 3.89 1.35840917 0 0 12 3 0 1 0 1
## 11 3.45 1.23837423 0 1 3 4 0 1 0 1
## 12 4.03 1.39376640 0 1 6 4 0 1 0 0
## 13 5.14 1.63705301 1 0 19 2 0 1 0 0
## 14 3.00 1.09861231 0 0 8 3 0 1 0 0
## 15 7.99 2.07819080 0 1 12 4 0 1 0 0
## 16 6.01 1.79342484 0 1 17 4 0 1 0 0
## 17 5.16 1.64093649 0 0 7 3 0 1 0 0
## 18 11.54 2.44581914 0 1 12 4 0 1 0 0
## 19 10.44 2.34564447 0 1 10 4 0 1 0 1
## 20 7.69 2.03992081 0 0 7 3 0 1 0 1
## 21 7.69 2.03992081 0 1 7 4 1 1 0 0
## 22 6.79 1.91545093 0 0 19 3 0 1 0 0
## 23 6.87 1.92716408 0 0 33 3 0 1 0 0
## 24 17.03 2.83497643 0 0 32 3 0 1 0 0
## 25 10.05 2.30757260 0 1 12 4 0 1 0 0
## 26 15.81 2.76064277 0 1 24 4 0 1 0 0
## 27 14.84 2.69732618 0 1 29 5 0 1 0 0
## 28 19.08 2.94864058 0 1 17 5 0 1 0 0
## 29 8.35 2.12226152 1 0 41 1 0 0 0 0
## 30 9.62 2.26384425 0 0 40 3 0 0 0 0
## 31 5.96 1.78507054 0 0 10 3 0 1 0 0
## 32 5.73 1.74571550 0 0 43 3 0 0 0 0
## 33 6.73 1.90657520 0 1 41 4 0 1 0 0
## 34 8.17 2.10046887 1 0 18 2 0 1 0 0
## 35 12.39 2.51688981 0 1 28 4 0 1 0 0
## 36 9.62 2.26384425 1 0 37 2 0 1 0 0
## 37 4.83 1.57484651 0 1 20 4 0 1 1 0
## 38 7.69 2.03992081 1 0 10 2 1 1 0 0
## 39 12.50 2.52572870 0 0 31 3 0 1 0 0
## 40 2.82 1.03673685 0 0 18 3 0 1 0 0
## 41 12.31 2.51041198 0 0 9 3 1 1 0 0
## 42 13.22 2.58173084 0 0 42 3 0 1 0 0
## 43 16.83 2.82316303 0 1 14 4 0 1 0 0
## 44 11.54 2.44581914 0 0 27 3 0 1 0 0
## 45 4.95 1.59938753 0 0 20 3 0 1 1 1
## 46 7.21 1.97546899 0 0 14 3 0 1 0 0
## 47 11.58 2.44927955 0 0 4 3 0 1 0 0
## 48 15.38 2.73306799 0 0 13 3 0 1 0 0
## 49 6.10 1.80828881 0 0 5 3 0 1 0 0
## 50 7.93 2.07065296 0 0 32 3 0 1 0 0
## 51 9.16 2.21484613 0 1 19 4 1 1 0 1
## 52 6.91 1.93296957 0 1 14 4 0 1 0 1
## 53 20.18 3.00469208 0 0 28 3 0 1 0 0
## 54 9.93 2.29556060 0 0 31 3 1 1 0 0
## 55 7.79 2.05284095 0 1 18 4 0 1 0 0
## 56 5.13 1.63510573 0 0 23 3 0 1 0 0
## 57 10.99 2.39698577 0 1 14 4 0 1 0 0
## 58 6.56 1.88099062 1 0 17 2 0 1 0 0
## 59 10.10 2.31253552 0 0 16 3 0 1 0 0
## 60 10.74 2.37397504 0 0 19 3 0 1 0 0
## 61 16.35 2.79422784 0 0 26 3 0 1 0 0
## 62 4.62 1.53039467 0 1 20 4 0 1 0 1
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## 1260 0 1 0 1 1 576 16b1=wooldridge::beauty
#female
f1=beauty %>%
filter(female==1) %>%
dplyr::select(lwage,looks,educ,exper)
#male
m1=beauty %>%
filter(female==0) %>%
dplyr::select(lwage,looks,educ,exper)
#linear regression model containing only females
lm(lwage ~ looks + educ + exper, data = f1) %>% summary()##
## Call:
## lm(formula = lwage ~ looks + educ + exper, data = f1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.26740 -0.31720 0.06164 0.28743 3.02860
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.084601 0.154026 -0.549 0.5831
## looks 0.059740 0.032021 1.866 0.0628 .
## educ 0.082247 0.009002 9.136 < 2e-16 ***
## exper 0.011216 0.002203 5.091 5.34e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4711 on 432 degrees of freedom
## Multiple R-squared: 0.1983, Adjusted R-squared: 0.1928
## F-statistic: 35.62 on 3 and 432 DF, p-value: < 2.2e-16#linear regression modelo containing only males
lm(lwage ~ looks + educ + exper, data = m1) %>% summary()##
## Call:
## lm(formula = lwage ~ looks + educ + exper, data = m1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.49331 -0.30394 -0.00906 0.30767 1.87797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.566104 0.121863 4.645 3.95e-06 ***
## looks 0.047679 0.026291 1.813 0.0701 .
## educ 0.065800 0.006654 9.889 < 2e-16 ***
## exper 0.014975 0.001457 10.280 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4918 on 820 degrees of freedom
## Multiple R-squared: 0.174, Adjusted R-squared: 0.171
## F-statistic: 57.59 on 3 and 820 DF, p-value: < 2.2e-16- Interact the variables “looks” and “service” and comment on whether the effect of physical appearance is different in service and other industries. Include educ and exper as control variables and use the entire sample for estimation. Plot the interaction model results.
Be <- wooldridge:: beauty
boxplot(Be$looks~Be$service ,xlab="service", ylab = "looks", col= "pink", lty= 1,outpch= 20, outbg="black", wwhisklty= 2)
cor.test(Be$looks,Be$service,method = "pearson" )##
## Pearson's product-moment correlation
##
## data: Be$looks and Be$service
## t = 0.36227, df = 1258, p-value = 0.7172
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04503725 0.06540193
## sample estimates:
## cor
## 0.01021349B1 <- split(Be, Be$service)
service.true <- B1$`1`
service.false <- B1$`0`
cor.test(service.true$looks, service.true$wage)##
## Pearson's product-moment correlation
##
## data: service.true$looks and service.true$wage
## t = 1.9018, df = 343, p-value = 0.05803
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.003473449 0.205522613
## sample estimates:
## cor
## 0.1021517cor.test(service.false$looks, service.false$wage)##
## Pearson's product-moment correlation
##
## data: service.false$looks and service.false$wage
## t = 0.93387, df = 913, p-value = 0.3506
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03398619 0.09551047
## sample estimates:
## cor
## 0.03089177plot(service.true$wage, service.true$looks)
plot(service.false$wage, service.false$looks)
- Partition the data set into high and low wage by coding observations above-median lwage as 1 and observations below-median lwage as zero. Estimate a logit model with the new factor variable as the dependent variable and educ, exper, and looks as the explanatory variables. Interpret the results
b1 <- b1 %>% dplyr::mutate(
wagemed = as.factor(ifelse((lwage>median(lwage)),1,0))
)
str(b1)## 'data.frame': 1260 obs. of 18 variables:
## $ wage : num 5.73 4.28 7.96 11.57 11.42 ...
## $ lwage : num 1.75 1.45 2.07 2.45 2.44 ...
## $ belavg : int 0 0 0 0 0 0 0 0 0 0 ...
## $ abvavg : int 1 0 1 0 0 0 0 1 0 0 ...
## $ exper : int 30 28 35 38 27 20 12 5 5 12 ...
## $ looks : int 4 3 4 3 3 3 3 4 3 3 ...
## $ union : int 0 0 0 0 0 0 0 1 0 0 ...
## $ goodhlth: int 1 1 1 1 1 0 1 1 1 1 ...
## $ black : int 0 0 0 0 0 0 0 0 0 0 ...
## $ female : int 1 1 1 0 0 1 0 0 1 1 ...
## $ married : int 1 1 0 1 1 1 1 0 0 0 ...
## $ south : int 0 1 0 0 0 0 0 0 0 0 ...
## $ bigcity : int 0 0 0 1 0 1 1 0 0 0 ...
## $ smllcity: int 1 1 1 0 1 0 0 1 0 1 ...
## $ service : int 1 0 0 1 0 0 0 0 0 0 ...
## $ expersq : int 900 784 1225 1444 729 400 144 25 25 144 ...
## $ educ : int 14 12 10 16 16 12 16 16 16 12 ...
## $ wagemed : Factor w/ 2 levels "0","1": 2 1 2 2 2 1 2 2 1 1 ...
## - attr(*, "time.stamp")= chr "25 Jun 2011 23:03"# Logistic regression
logit <- b1
logit$wagemed <- as.factor(logit$wagemed)
model_b1 <- glm(wagemed ~ educ + exper + looks, family = "binomial", data = logit)
summary(model_b1)##
## Call:
## glm(formula = wagemed ~ educ + exper + looks, family = "binomial",
## data = logit)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.21643 -0.94346 -0.02221 1.00077 2.27974
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.837786 0.472811 -10.232 <2e-16 ***
## educ 0.254958 0.026230 9.720 <2e-16 ***
## exper 0.071672 0.005915 12.118 <2e-16 ***
## looks 0.108322 0.092882 1.166 0.244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1746.7 on 1259 degrees of freedom
## Residual deviance: 1511.2 on 1256 degrees of freedom
## AIC: 1519.2
##
## Number of Fisher Scoring iterations: 4- Compare the returns from education and experience for individuals having below-average and above-average looks
data('beauty')
#below average experience
b1 <- beauty
b1 <- wooldridge::beauty %>% filter(belavg == 1)
view(b1)
boxplot(b1$exper~b1$belavg, xlab="person_below_average", ylab="experience", col="blue", lty=1, outpch=20, outbg="Purple", wwhisklty=2)
data('beauty')
#below average education
b1 <- beauty
b1 <- wooldridge::beauty %>% filter(belavg == 1)
view(b1)
boxplot(b1$educ~b1$belavg, xlab="person_below_average", ylab="education", col="blue", lty=1, outpch=20, outbg="Purple", wwhisklty=2)
data('beauty')
#above average education
b1 <- beauty
b1 <- wooldridge::beauty %>% filter(abvavg == 1)
view(b1)
boxplot(b1$educ~b1$abvavg, xlab="person_above_average", ylab="education", col="blue", lty=1, outpch=20, outbg="Purple", wwhisklty=2)
data('beauty')
#above average experience
b1 <- beauty
b1 <- wooldridge::beauty %>% filter(abvavg == 1)
view(b1)
boxplot(b1$exper~b1$abvavg, xlab="person_above_average", ylab="experience", col="blue", lty=1, outpch=20, outbg="Purple", wwhisklty=2)
- Compare the returns from education and experience for females having below-average and above-average looks. Additionally, compare the returns from education and experience for males with below-average looks and above-average looks. Compare the results with that of females and comment on the results.
#Female Edu, below
q1 <- wooldridge::beauty %>% filter(female == 1) %>%
filter(belavg == 1)
view(q1)
boxplot(q1$educ~q1$belavg, xlab="Women_below_average", ylab="Education", col="Pink", lty=1, outpch=20, outbg="Purple", wwhisklty=2)
#Female, Edu, above
q1 <- wooldridge::beauty %>% filter(female == 1) %>%
filter(abvavg == 1)
view(q1)
boxplot(q1$educ~q1$abvavg, xlab="Women_above_average", ylab="Education", col="Pink", lty=1, outpch=20, outbg="purple", wwhisklty=2)
#Female, Exp, below
q1 <- wooldridge::beauty %>% filter(female == 1) %>%
filter(belavg == 1)
view(q1)
boxplot(q1$exper~q1$belavg, xlab="Women_below_average", ylab="Experience", col="pink", lty=1, outpch=20, outbg="purple", wwhisklty=2)
#Female, Exp, above
q1 <- wooldridge::beauty %>% filter(female == 1) %>%
filter(abvavg == 1)
view(q1)
boxplot(q1$exper~q1$abvavg, xlab="Women_above_average", ylab="Experience", col="pink", lty=1, outpch=20, outbg="purple", wwhisklty=2)
#Male, Edu, below
q1 <- wooldridge::beauty %>% filter(female == 0) %>%
filter(belavg == 1)
view(q1)
boxplot(q1$educ~q1$belavg, xlab="Men_below_average", ylab="Education", col="blue", lty=1, outpch=20, outbg="dark blue", wwhisklty=2)
#Male, Edu, above
q1 <- wooldridge::beauty %>% filter(female == 0) %>%
filter(abvavg == 1)
view(q1)
boxplot(q1$educ~q1$abvavg, xlab="Men_above_average", ylab="Education", col="blue", lty=1, outpch=20, outbg="dark blue", wwhisklty=2)
#Male, Exp, below
q1 <- wooldridge::beauty %>% filter(female == 0) %>%
filter(belavg == 1)
view(q1)
boxplot(q1$exper~q1$belavg, xlab="Men_below_average", ylab="Experience", col="blue", lty=1, outpch=20, outbg="dark blue", wwhisklty=2)
#Male, Exp, above
q1 <- wooldridge::beauty %>% filter(female == 0) %>%
filter(abvavg == 1)
view(q1)
boxplot(q1$exper~q1$abvavg, xlab="Men_above_average", ylab="Experience", col="blue", lty=1, outpch=20, outbg="dark blue", wwhisklty=2)
- Construct a correlation matrix (pearson and spearman) with p-values with lwage, educ, exper, and looks variables
b2 <- beauty %>% select(lwage,educ,exper,looks)
rstatix::cor_test(b2, method= "pearson")## # A tibble: 16 × 8
## var1 var2 cor statistic p conf.low conf.high method
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 lwage lwage 1 Inf 0 1 1 Pearson
## 2 lwage educ 0.27 9.79 7.18e-22 0.214 0.317 Pearson
## 3 lwage exper 0.31 11.4 8 e-29 0.256 0.356 Pearson
## 4 lwage looks 0.059 2.09 3.72e- 2 0.00348 0.114 Pearson
## 5 educ lwage 0.27 9.79 7.18e-22 0.214 0.317 Pearson
## 6 educ educ 1 Inf 0 1 1 Pearson
## 7 educ exper -0.19 -6.72 2.72e-11 -0.239 -0.132 Pearson
## 8 educ looks 0.15 5.52 4.06e- 8 0.0995 0.207 Pearson
## 9 exper lwage 0.31 11.4 8 e-29 0.256 0.356 Pearson
## 10 exper educ -0.19 -6.72 2.72e-11 -0.239 -0.132 Pearson
## 11 exper exper 1 Inf 0 1 1 Pearson
## 12 exper looks -0.16 -5.57 3.18e- 8 -0.208 -0.101 Pearson
## 13 looks lwage 0.059 2.09 3.72e- 2 0.00348 0.114 Pearson
## 14 looks educ 0.15 5.52 4.06e- 8 0.0995 0.207 Pearson
## 15 looks exper -0.16 -5.57 3.18e- 8 -0.208 -0.101 Pearson
## 16 looks looks 1 Inf 0 1 1 Pearsonrstatix::cor_mat(b2, method= "pearson") %>%
cor_get_pval()## # A tibble: 4 × 5
## rowname lwage educ exper looks
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 lwage 0 7.18e-22 8 e-29 0.0372
## 2 educ 7.18e-22 0 2.72e-11 0.0000000406
## 3 exper 8 e-29 2.72e-11 0 0.0000000318
## 4 looks 3.72e- 2 4.06e- 8 3.18e- 8 0rstatix::cor_mat(b2, method= "pearson") %>%
pull_lower_triangle() %>%
cor_plot(label = TRUE) 
rstatix::cor_test(b2, method= "spearman")## # A tibble: 16 × 6
## var1 var2 cor statistic p method
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 lwage lwage 1 7.40e-8 0 Spearman
## 2 lwage educ 0.26 2.46e+8 2.51e-21 Spearman
## 3 lwage exper 0.36 2.13e+8 4.79e-40 Spearman
## 4 lwage looks 0.038 3.21e+8 1.75e- 1 Spearman
## 5 educ lwage 0.26 2.46e+8 2.51e-21 Spearman
## 6 educ educ 1 0 0 Spearman
## 7 educ exper -0.14 3.80e+8 7.86e- 7 Spearman
## 8 educ looks 0.14 2.86e+8 4.89e- 7 Spearman
## 9 exper lwage 0.36 2.13e+8 4.79e-40 Spearman
## 10 exper educ -0.14 3.80e+8 7.86e- 7 Spearman
## 11 exper exper 1 0 0 Spearman
## 12 exper looks -0.15 3.84e+8 5.1 e- 8 Spearman
## 13 looks lwage 0.038 3.21e+8 1.75e- 1 Spearman
## 14 looks educ 0.14 2.86e+8 4.89e- 7 Spearman
## 15 looks exper -0.15 3.84e+8 5.1 e- 8 Spearman
## 16 looks looks 1 3.70e-8 0 Spearmanrstatix::cor_mat(b2, method= "spearman") %>%
cor_get_pval()## # A tibble: 4 × 5
## rowname lwage educ exper looks
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 lwage 0 2.51e-21 4.79e-40 0.175
## 2 educ 2.51e-21 0 7.86e- 7 0.000000489
## 3 exper 4.79e-40 7.86e- 7 0 0.000000051
## 4 looks 1.75e- 1 4.89e- 7 5.1 e- 8 0rstatix::cor_mat(b2, method= "spearman") %>%
pull_lower_triangle() %>%
cor_plot(label = TRUE)