Practica 6 ARegresion lineal de autos
Roque Alvarez
26/2/2020
librerias
library(readr)
# install.packages("corrplot") # Nuevo
library(corrplot) # Para correlación## corrplot 0.84 loadedlibrary(caret) # Para dividir conjunto de datos## Loading required package: lattice## Loading required package: ggplot2# install.packages("MASS") # NUEVO
library(MASS)Los datos
datos <- read.csv("D:/Escuela/Clases/8vo-semestre/Analisis-inteligente-de-datos/RStudio/datos/auto-mpg.csv")
datos## No mpg cylinders displacement horsepower weight acceleration model_year
## 1 1 28.0 4 140.0 90 2264 15.5 71
## 2 2 19.0 3 70.0 97 2330 13.5 72
## 3 3 36.0 4 107.0 75 2205 14.5 82
## 4 4 28.0 4 97.0 92 2288 17.0 72
## 5 5 21.0 6 199.0 90 2648 15.0 70
## 6 6 23.0 4 115.0 95 2694 15.0 75
## 7 7 15.5 8 304.0 120 3962 13.9 76
## 8 8 32.9 4 119.0 100 2615 14.8 81
## 9 9 16.0 6 250.0 105 3897 18.5 75
## 10 10 13.0 8 318.0 150 3755 14.0 76
## 11 11 12.0 8 429.0 198 4952 11.5 73
## 12 12 30.7 6 145.0 76 3160 19.6 81
## 13 13 13.0 8 302.0 130 3870 15.0 76
## 14 14 27.9 4 156.0 105 2800 14.4 80
## 15 15 13.0 8 400.0 190 4422 12.5 72
## 16 16 23.8 4 151.0 85 2855 17.6 78
## 17 17 29.0 4 90.0 70 1937 14.2 76
## 18 18 14.0 8 400.0 175 4385 12.0 72
## 19 19 14.0 8 302.0 140 4638 16.0 74
## 20 20 29.0 4 135.0 84 2525 16.0 82
## 21 21 20.5 6 225.0 100 3430 17.2 78
## 22 22 26.6 4 151.0 84 2635 16.4 81
## 23 23 20.0 4 140.0 90 2408 19.5 72
## 24 24 20.0 4 114.0 91 2582 14.0 73
## 25 25 26.4 4 140.0 88 2870 18.1 80
## 26 26 16.0 8 400.0 170 4668 11.5 75
## 27 27 40.8 4 85.0 65 2110 19.2 80
## 28 28 15.0 8 383.0 170 3563 10.0 70
## 29 29 18.0 6 199.0 97 2774 15.5 70
## 30 30 35.0 4 72.0 69 1613 18.0 71
## 31 31 26.5 4 140.0 72 2565 13.6 76
## 32 32 13.0 8 307.0 130 4098 14.0 72
## 33 33 25.8 4 156.0 92 2620 14.4 81
## 34 34 39.1 4 79.0 58 1755 16.9 81
## 35 35 25.0 4 104.0 95 2375 17.5 70
## 36 36 14.0 8 351.0 153 4154 13.5 71
## 37 37 19.4 6 232.0 90 3210 17.2 78
## 38 38 30.0 4 97.0 67 1985 16.4 77
## 39 39 32.0 4 135.0 84 2295 11.6 82
## 40 40 26.0 4 97.0 46 1950 21.0 73
## 41 41 20.6 6 225.0 110 3360 16.6 79
## 42 42 17.5 8 305.0 140 4215 13.0 76
## 43 43 18.0 6 250.0 88 3139 14.5 71
## 44 44 14.0 8 318.0 150 4237 14.5 73
## 45 45 27.0 4 97.0 88 2130 14.5 70
## 46 46 25.1 4 140.0 88 2720 15.4 78
## 47 47 14.0 8 351.0 148 4657 13.5 75
## 48 48 19.1 6 225.0 90 3381 18.7 80
## 49 49 17.0 8 304.0 150 3672 11.5 72
## 50 50 23.5 6 173.0 110 2725 12.6 81
## 51 51 21.5 4 121.0 110 2600 12.8 77
## 52 52 19.0 6 250.0 88 3302 15.5 71
## 53 53 22.0 4 121.0 76 2511 18.0 72
## 54 54 19.4 8 318.0 140 3735 13.2 78
## 55 55 20.0 8 262.0 110 3221 13.5 75
## 56 56 32.0 4 144.0 96 2665 13.9 82
## 57 57 30.9 4 105.0 75 2230 14.5 78
## 58 58 29.0 4 98.0 83 2219 16.5 74
## 59 59 14.0 8 454.0 220 4354 9.0 70
## 60 60 14.0 8 304.0 150 4257 15.5 74
## 61 61 38.0 4 105.0 63 2125 14.7 82
## 62 62 24.0 4 119.0 97 2545 17.0 75
## 63 63 14.0 8 455.0 225 4425 10.0 70
## 64 64 14.0 8 318.0 150 4457 13.5 74
## 65 65 16.5 8 350.0 180 4380 12.1 76
## 66 66 31.0 4 91.0 68 1970 17.6 82
## 67 67 19.9 8 260.0 110 3365 15.5 78
## 68 68 12.0 8 350.0 160 4456 13.5 72
## 69 69 16.0 6 250.0 100 3781 17.0 74
## 70 70 17.0 8 305.0 130 3840 15.4 79
## 71 71 33.5 4 85.0 70 1945 16.8 77
## 72 72 15.0 6 250.0 72 3158 19.5 75
## 73 73 19.0 6 250.0 100 3282 15.0 71
## 74 74 31.3 4 120.0 75 2542 17.5 80
## 75 75 18.0 6 250.0 105 3459 16.0 75
## 76 76 13.0 8 351.0 158 4363 13.0 73
## 77 77 20.5 6 200.0 95 3155 18.2 78
## 78 78 21.0 4 122.0 86 2226 16.5 72
## 79 79 14.0 8 318.0 150 4077 14.0 72
## 80 80 15.5 8 400.0 190 4325 12.2 77
## 81 81 32.0 4 83.0 61 2003 19.0 74
## 82 82 33.8 4 97.0 67 2145 18.0 80
## 83 83 36.1 4 98.0 66 1800 14.4 78
## 84 84 22.0 4 121.0 98 2945 14.5 75
## 85 85 21.0 6 200.0 101 2875 17.0 74
## 86 86 17.6 8 302.0 129 3725 13.4 79
## 87 87 40.9 4 85.0 78 1835 17.3 80
## 88 88 15.5 8 318.0 145 4140 13.7 77
## 89 89 26.0 4 97.0 46 1835 20.5 70
## 90 90 24.0 4 113.0 95 2278 15.5 72
## 91 91 15.0 8 302.0 130 4295 14.9 77
## 92 92 13.0 8 360.0 170 4654 13.0 73
## 93 93 36.0 4 135.0 84 2370 13.0 82
## 94 94 37.2 4 86.0 65 2019 16.4 80
## 95 95 43.4 4 90.0 48 2335 23.7 80
## 96 96 25.0 4 98.0 78 2046 19.0 71
## 97 97 31.5 4 89.0 71 1990 14.9 78
## 98 98 15.0 8 429.0 198 4341 10.0 70
## 99 99 27.0 4 151.0 90 2950 17.3 82
## 100 100 11.0 8 429.0 208 4633 11.0 72
## 101 101 26.0 4 91.0 70 1955 20.5 71
## 102 102 26.0 4 96.0 69 2189 18.0 72
## 103 103 26.0 4 98.0 90 2265 15.5 73
## 104 104 12.0 8 383.0 180 4955 11.5 71
## 105 105 24.5 4 98.0 60 2164 22.1 76
## 106 106 26.0 4 98.0 79 2255 17.7 76
## 107 107 34.5 4 100.0 78 2320 15.8 81
## 108 108 15.0 8 350.0 165 3693 11.5 70
## 109 109 27.5 4 134.0 95 2560 14.2 78
## 110 110 18.0 6 225.0 105 3121 16.5 73
## 111 111 32.3 4 97.0 67 2065 17.8 81
## 112 112 18.0 8 318.0 150 3436 11.0 70
## 113 113 10.0 8 360.0 215 4615 14.0 70
## 114 114 23.6 4 140.0 78 2905 14.3 80
## 115 115 22.4 6 231.0 110 3415 15.8 81
## 116 116 37.0 4 119.0 92 2434 15.0 80
## 117 117 21.5 6 231.0 115 3245 15.4 79
## 118 118 33.0 4 91.0 53 1795 17.5 75
## 119 119 27.0 4 97.0 88 2100 16.5 72
## 120 120 25.0 4 116.0 81 2220 16.9 76
## 121 121 23.0 4 120.0 88 2957 17.0 75
## 122 122 14.0 8 351.0 153 4129 13.0 72
## 123 123 26.6 8 350.0 105 3725 19.0 81
## 124 124 18.0 6 171.0 97 2984 14.5 75
## 125 125 14.0 8 455.0 225 3086 10.0 70
## 126 126 14.0 8 302.0 137 4042 14.5 73
## 127 127 34.0 4 112.0 88 2395 18.0 82
## 128 128 29.5 4 97.0 71 1825 12.2 76
## 129 129 16.9 8 350.0 155 4360 14.9 79
## 130 130 21.0 4 120.0 87 2979 19.5 72
## 131 131 26.0 4 121.0 113 2234 12.5 70
## 132 132 33.0 4 105.0 74 2190 14.2 81
## 133 133 16.0 8 318.0 150 4190 13.0 76
## 134 134 15.0 8 350.0 145 4082 13.0 73
## 135 135 10.0 8 307.0 200 4376 15.0 70
## 136 136 28.0 4 120.0 79 2625 18.6 82
## 137 137 20.2 6 200.0 85 2965 15.8 78
## 138 138 29.8 4 134.0 90 2711 15.5 80
## 139 139 14.0 8 318.0 150 4096 13.0 71
## 140 140 11.0 8 350.0 180 3664 11.0 73
## 141 141 15.0 8 390.0 190 3850 8.5 70
## 142 142 38.0 4 91.0 67 1965 15.0 82
## 143 143 13.0 8 440.0 215 4735 11.0 73
## 144 144 41.5 4 98.0 76 2144 14.7 80
## 145 145 19.2 8 267.0 125 3605 15.0 79
## 146 146 34.1 4 91.0 68 1985 16.0 81
## 147 147 21.0 6 155.0 107 2472 14.0 73
## 148 148 36.0 4 105.0 74 1980 15.3 82
## 149 149 18.0 6 232.0 100 2789 15.0 73
## 150 150 44.0 4 97.0 52 2130 24.6 82
## 151 151 37.7 4 89.0 62 2050 17.3 81
## 152 152 16.0 8 400.0 230 4278 9.5 73
## 153 153 31.0 4 76.0 52 1649 16.5 74
## 154 154 25.5 4 122.0 96 2300 15.5 77
## 155 155 34.3 4 97.0 78 2188 15.8 80
## 156 156 26.0 4 108.0 93 2391 15.5 74
## 157 157 16.0 6 225.0 105 3439 15.5 71
## 158 158 13.0 8 318.0 150 3940 13.2 76
## 159 159 27.2 4 135.0 84 2490 15.7 81
## 160 160 23.0 4 140.0 78 2592 18.5 75
## 161 161 24.2 6 146.0 120 2930 13.8 81
## 162 162 30.0 4 146.0 67 3250 21.8 80
## 163 163 14.0 8 340.0 160 3609 8.0 70
## 164 164 13.0 8 360.0 175 3821 11.0 73
## 165 165 30.0 4 88.0 76 2065 14.5 71
## 166 166 17.5 8 305.0 145 3880 12.5 77
## 167 167 17.5 8 318.0 140 4080 13.7 78
## 168 168 29.0 4 68.0 49 1867 19.5 73
## 169 169 22.0 4 140.0 72 2408 19.0 71
## 170 170 15.0 8 304.0 150 3892 12.5 72
## 171 171 36.4 5 121.0 67 2950 19.9 80
## 172 172 15.0 8 318.0 150 3777 12.5 73
## 173 173 36.0 4 98.0 70 2125 17.3 82
## 174 174 21.0 6 231.0 110 3039 15.0 75
## 175 175 15.5 8 350.0 170 4165 11.4 77
## 176 176 36.1 4 91.0 60 1800 16.4 78
## 177 177 17.5 6 258.0 95 3193 17.8 76
## 178 178 22.0 6 232.0 112 2835 14.7 82
## 179 179 26.0 4 79.0 67 1963 15.5 74
## 180 180 18.0 4 121.0 112 2933 14.5 72
## 181 181 25.0 4 121.0 115 2671 13.5 75
## 182 182 23.0 6 198.0 95 2904 16.0 73
## 183 183 15.0 6 250.0 100 3336 17.0 74
## 184 184 22.0 6 250.0 105 3353 14.5 76
## 185 185 23.0 4 151.0 78 3035 20.5 82
## 186 186 12.0 8 350.0 180 4499 12.5 73
## 187 187 15.0 8 318.0 150 4135 13.5 72
## 188 188 21.6 4 121.0 115 2795 15.7 78
## 189 189 22.0 4 108.0 94 2379 16.5 73
## 190 190 9.0 8 304.0 193 4732 18.5 70
## 191 191 28.0 4 97.0 75 2155 16.4 76
## 192 192 22.0 4 122.0 86 2395 16.0 72
## 193 193 14.5 8 351.0 152 4215 12.8 76
## 194 194 20.0 4 97.0 88 2279 19.0 73
## 195 195 19.0 4 122.0 85 2310 18.5 73
## 196 196 22.3 4 140.0 88 2890 17.3 79
## 197 197 24.0 4 107.0 90 2430 14.5 70
## 198 198 12.0 8 400.0 167 4906 12.5 73
## 199 199 18.0 3 70.0 90 2124 13.5 73
## 200 200 29.5 4 98.0 68 2135 16.6 78
## 201 201 19.0 6 232.0 100 2901 16.0 74
## 202 202 25.4 6 168.0 116 2900 12.6 81
## 203 203 24.0 6 200.0 81 3012 17.6 76
## 204 204 26.0 4 97.0 75 2265 18.2 77
## 205 205 23.0 4 97.0 54 2254 23.5 72
## 206 206 13.0 8 400.0 150 4464 12.0 73
## 207 207 18.0 6 225.0 105 3613 16.5 74
## 208 208 27.2 4 119.0 97 2300 14.7 78
## 209 209 18.0 6 258.0 110 2962 13.5 71
## 210 210 24.0 4 116.0 75 2158 15.5 73
## 211 211 29.0 4 97.0 75 2171 16.0 75
## 212 212 26.0 4 156.0 92 2585 14.5 82
## 213 213 13.0 8 350.0 175 4100 13.0 73
## 214 214 31.0 4 71.0 65 1773 19.0 71
## 215 215 20.6 6 231.0 105 3380 15.8 78
## 216 216 33.5 4 98.0 83 2075 15.9 77
## 217 217 27.4 4 121.0 80 2670 15.0 79
## 218 218 30.0 4 111.0 80 2155 14.8 77
## 219 219 18.0 6 232.0 100 3288 15.5 71
## 220 220 24.0 4 134.0 96 2702 13.5 75
## 221 221 25.0 4 97.5 80 2126 17.0 72
## 222 222 12.0 8 455.0 225 4951 11.0 73
## 223 223 20.0 6 232.0 100 2914 16.0 75
## 224 224 13.0 8 350.0 150 4699 14.5 74
## 225 225 26.8 6 173.0 115 2700 12.9 79
## 226 226 15.0 8 318.0 150 3399 11.0 73
## 227 227 26.0 4 116.0 75 2246 14.0 74
## 228 228 31.0 4 112.0 85 2575 16.2 82
## 229 229 34.7 4 105.0 63 2215 14.9 81
## 230 230 30.5 4 97.0 78 2190 14.1 77
## 231 231 25.0 4 140.0 75 2542 17.0 74
## 232 232 21.0 4 140.0 72 2401 19.5 73
## 233 233 18.1 8 302.0 139 3205 11.2 78
## 234 234 32.4 4 107.0 72 2290 17.0 80
## 235 235 15.0 8 350.0 145 4440 14.0 75
## 236 236 24.0 4 113.0 95 2372 15.0 70
## 237 237 28.0 4 90.0 75 2125 14.5 74
## 238 238 18.6 6 225.0 110 3620 18.7 78
## 239 239 19.8 6 200.0 85 2990 18.2 79
## 240 240 13.0 8 400.0 175 5140 12.0 71
## 241 241 19.0 4 121.0 112 2868 15.5 73
## 242 242 25.0 4 113.0 95 2228 14.0 71
## 243 243 23.0 4 140.0 83 2639 17.0 75
## 244 244 27.0 4 97.0 88 2130 14.5 71
## 245 245 25.0 4 140.0 92 2572 14.9 76
## 246 246 35.7 4 98.0 80 1915 14.4 79
## 247 247 20.0 6 198.0 95 3102 16.5 74
## 248 248 16.5 8 351.0 138 3955 13.2 79
## 249 249 34.0 4 108.0 70 2245 16.9 82
## 250 250 20.0 4 130.0 102 3150 15.7 76
## 251 251 23.7 3 70.0 100 2420 12.5 80
## 252 252 18.2 8 318.0 135 3830 15.2 79
## 253 253 11.0 8 400.0 150 4997 14.0 73
## 254 254 16.0 8 318.0 150 4498 14.5 75
## 255 255 24.5 4 151.0 88 2740 16.0 77
## 256 256 33.7 4 107.0 75 2210 14.4 81
## 257 257 37.3 4 91.0 69 2130 14.7 79
## 258 258 16.2 6 163.0 133 3410 15.8 78
## 259 259 31.0 4 79.0 67 2000 16.0 74
## 260 260 27.2 4 141.0 71 3190 24.8 79
## 261 261 32.0 4 85.0 70 1990 17.0 76
## 262 262 31.6 4 120.0 74 2635 18.3 81
## 263 263 43.1 4 90.0 48 1985 21.5 78
## 264 264 30.0 4 135.0 84 2385 12.9 81
## 265 265 32.4 4 108.0 75 2350 16.8 81
## 266 266 27.0 4 101.0 83 2202 15.3 76
## 267 267 17.0 8 302.0 140 3449 10.5 70
## 268 268 29.0 4 85.0 52 2035 22.2 76
## 269 269 46.6 4 86.0 65 2110 17.9 80
## 270 270 20.5 6 231.0 105 3425 16.9 77
## 271 271 34.2 4 105.0 70 2200 13.2 79
## 272 272 32.0 4 71.0 65 1836 21.0 74
## 273 273 18.5 6 250.0 98 3525 19.0 77
## 274 274 17.7 6 231.0 165 3445 13.4 78
## 275 275 25.0 4 90.0 71 2223 16.5 75
## 276 276 20.2 6 200.0 88 3060 17.1 81
## 277 277 17.0 6 231.0 110 3907 21.0 75
## 278 278 28.0 4 98.0 80 2164 15.0 72
## 279 279 28.0 4 112.0 88 2605 19.6 82
## 280 280 28.0 4 107.0 86 2464 15.5 76
## 281 281 18.0 8 307.0 130 3504 12.0 70
## 282 282 17.5 6 250.0 110 3520 16.4 77
## 283 283 13.0 8 350.0 165 4274 12.0 72
## 284 284 15.0 6 258.0 110 3730 19.0 75
## 285 285 15.0 8 400.0 150 3761 9.5 70
## 286 286 27.0 4 140.0 86 2790 15.6 82
## 287 287 20.2 6 232.0 90 3265 18.2 79
## 288 288 24.0 4 140.0 92 2865 16.4 82
## 289 289 23.0 4 122.0 86 2220 14.0 71
## 290 290 25.4 5 183.0 77 3530 20.1 79
## 291 291 11.0 8 318.0 210 4382 13.5 70
## 292 292 15.0 6 250.0 72 3432 21.0 75
## 293 293 27.0 4 112.0 88 2640 18.6 82
## 294 294 20.0 6 156.0 122 2807 13.5 73
## 295 295 29.9 4 98.0 65 2380 20.7 81
## 296 296 36.0 4 120.0 88 2160 14.5 82
## 297 297 23.2 4 156.0 105 2745 16.7 78
## 298 298 37.0 4 91.0 68 2025 18.2 82
## 299 299 24.0 4 120.0 97 2489 15.0 74
## 300 300 38.0 6 262.0 85 3015 17.0 82
## 301 301 35.0 4 122.0 88 2500 15.1 80
## 302 302 29.8 4 89.0 62 1845 15.3 80
## 303 303 26.0 4 97.0 78 2300 14.5 74
## 304 304 27.0 4 97.0 60 1834 19.0 71
## 305 305 17.6 6 225.0 85 3465 16.6 81
## 306 306 13.0 8 350.0 145 3988 13.0 73
## 307 307 24.0 4 121.0 110 2660 14.0 73
## 308 308 22.0 6 225.0 100 3233 15.4 76
## 309 309 26.0 4 122.0 80 2451 16.5 74
## 310 310 18.5 6 250.0 110 3645 16.2 76
## 311 311 16.0 8 400.0 180 4220 11.1 77
## 312 312 19.0 6 156.0 108 2930 15.5 76
## 313 313 31.0 4 119.0 82 2720 19.4 82
## 314 314 28.0 4 116.0 90 2123 14.0 71
## 315 315 18.0 6 250.0 78 3574 21.0 76
## 316 316 20.3 5 131.0 103 2830 15.9 78
## 317 317 34.1 4 86.0 65 1975 15.2 79
## 318 318 31.0 4 79.0 67 1950 19.0 74
## 319 319 36.0 4 79.0 58 1825 18.6 77
## 320 320 15.5 8 351.0 142 4054 14.3 79
## 321 321 13.0 8 350.0 145 4055 12.0 76
## 322 322 27.0 4 151.0 90 2735 18.0 82
## 323 323 19.0 4 120.0 88 3270 21.9 76
## 324 324 18.0 6 250.0 88 3021 16.5 73
## 325 325 18.0 6 232.0 100 2945 16.0 73
## 326 326 32.2 4 108.0 75 2265 15.2 80
## 327 327 31.8 4 85.0 65 2020 19.2 79
## 328 328 17.0 8 260.0 110 4060 19.0 77
## 329 329 33.5 4 151.0 90 2556 13.2 79
## 330 330 25.5 4 140.0 89 2755 15.8 77
## 331 331 30.0 4 79.0 70 2074 19.5 71
## 332 332 33.0 4 91.0 53 1795 17.4 76
## 333 333 21.0 6 200.0 85 2587 16.0 70
## 334 334 19.0 6 225.0 100 3630 17.7 77
## 335 335 44.3 4 90.0 48 2085 21.7 80
## 336 336 22.0 6 146.0 97 2815 14.5 77
## 337 337 19.0 6 232.0 90 3211 17.0 75
## 338 338 24.0 4 90.0 75 2108 15.5 74
## 339 339 28.1 4 141.0 80 3230 20.4 81
## 340 340 14.0 8 350.0 165 4209 12.0 71
## 341 341 19.2 6 231.0 105 3535 19.2 78
## 342 342 32.0 4 91.0 67 1965 15.7 82
## 343 343 17.0 6 250.0 100 3329 15.5 71
## 344 344 18.5 8 360.0 150 3940 13.0 79
## 345 345 13.0 8 400.0 170 4746 12.0 71
## 346 346 28.4 4 151.0 90 2670 16.0 79
## 347 347 19.2 8 305.0 145 3425 13.2 78
## 348 348 29.0 4 90.0 70 1937 14.0 75
## 349 349 31.5 4 98.0 68 2045 18.5 77
## 350 350 23.9 8 260.0 90 3420 22.2 79
## 351 351 13.0 8 302.0 129 3169 12.0 75
## 352 352 34.4 4 98.0 65 2045 16.2 81
## 353 353 28.8 6 173.0 115 2595 11.3 79
## 354 354 23.0 8 350.0 125 3900 17.4 79
## 355 355 38.0 4 91.0 67 1995 16.2 82
## 356 356 20.0 6 225.0 100 3651 17.7 76
## 357 357 16.0 8 351.0 149 4335 14.5 77
## 358 358 16.0 8 304.0 150 3433 12.0 70
## 359 359 25.0 4 110.0 87 2672 17.5 70
## 360 360 30.0 4 98.0 68 2155 16.5 78
## 361 361 32.8 4 78.0 52 1985 19.4 78
## 362 362 20.2 8 302.0 139 3570 12.8 78
## 363 363 18.1 6 258.0 120 3410 15.1 78
## 364 364 32.7 6 168.0 132 2910 11.4 80
## 365 365 21.5 3 80.0 110 2720 13.5 77
## 366 366 22.0 6 198.0 95 2833 15.5 70
## 367 367 24.3 4 151.0 90 3003 20.1 80
## 368 368 14.0 8 440.0 215 4312 8.5 70
## 369 369 37.0 4 85.0 65 1975 19.4 81
## 370 370 25.0 6 181.0 110 2945 16.4 82
## 371 371 28.0 4 151.0 90 2678 16.5 80
## 372 372 31.9 4 89.0 71 1925 14.0 79
## 373 373 16.0 6 250.0 100 3278 18.0 73
## 374 374 19.0 6 225.0 95 3264 16.0 75
## 375 375 39.4 4 85.0 70 2070 18.6 78
## 376 376 29.0 4 97.0 78 1940 14.5 77
## 377 377 17.0 6 163.0 125 3140 13.6 78
## 378 378 13.0 8 302.0 140 4294 16.0 72
## 379 379 23.0 4 120.0 97 2506 14.5 72
## 380 380 22.5 6 232.0 90 3085 17.6 76
## 381 381 20.8 6 200.0 85 3070 16.7 78
## 382 382 16.0 6 258.0 110 3632 18.0 74
## 383 383 39.0 4 86.0 64 1875 16.4 81
## 384 384 21.1 4 134.0 95 2515 14.8 78
## 385 385 18.0 6 225.0 95 3785 19.0 75
## 386 386 35.1 4 81.0 60 1760 16.1 81
## 387 387 14.0 8 304.0 150 3672 11.5 73
## 388 388 44.6 4 91.0 67 1850 13.8 80
## 389 389 16.0 8 302.0 140 4141 14.0 74
## 390 390 14.0 8 400.0 175 4464 11.5 71
## 391 391 32.1 4 98.0 70 2120 15.5 80
## 392 392 23.9 4 119.0 97 2405 14.9 78
## 393 393 13.0 8 350.0 155 4502 13.5 72
## 394 394 16.5 6 168.0 120 3820 16.7 76
## 395 395 34.5 4 105.0 70 2150 14.9 79
## 396 396 38.1 4 89.0 60 1968 18.8 80
## 397 397 30.5 4 98.0 63 2051 17.0 77
## 398 398 19.0 6 232.0 100 2634 13.0 71
## car_name
## 1 chevrolet vega 2300
## 2 mazda rx2 coupe
## 3 honda accord
## 4 datsun 510 (sw)
## 5 amc gremlin
## 6 audi 100ls
## 7 amc matador
## 8 datsun 200sx
## 9 chevroelt chevelle malibu
## 10 dodge d100
## 11 mercury marquis brougham
## 12 volvo diesel
## 13 ford f108
## 14 dodge colt
## 15 chrysler newport royal
## 16 oldsmobile starfire sx
## 17 vw rabbit
## 18 pontiac catalina
## 19 ford gran torino (sw)
## 20 dodge aries se
## 21 plymouth volare
## 22 buick skylark
## 23 chevrolet vega
## 24 audi 100ls
## 25 ford fairmont
## 26 pontiac catalina
## 27 datsun 210
## 28 dodge challenger se
## 29 amc hornet
## 30 datsun 1200
## 31 ford pinto
## 32 chevrolet chevelle concours (sw)
## 33 dodge aries wagon (sw)
## 34 toyota starlet
## 35 saab 99e
## 36 ford galaxie 500
## 37 amc concord
## 38 subaru dl
## 39 dodge rampage
## 40 volkswagen super beetle
## 41 dodge aspen 6
## 42 chevrolet chevelle malibu classic
## 43 ford mustang
## 44 plymouth fury gran sedan
## 45 datsun pl510
## 46 ford fairmont (man)
## 47 ford ltd
## 48 dodge aspen
## 49 amc ambassador sst
## 50 chevrolet citation
## 51 bmw 320i
## 52 ford torino 500
## 53 volkswagen 411 (sw)
## 54 dodge diplomat
## 55 chevrolet monza 2+2
## 56 toyota celica gt
## 57 dodge omni
## 58 audi fox
## 59 chevrolet impala
## 60 amc matador (sw)
## 61 plymouth horizon miser
## 62 datsun 710
## 63 pontiac catalina
## 64 dodge coronet custom (sw)
## 65 cadillac seville
## 66 mazda glc custom
## 67 oldsmobile cutlass salon brougham
## 68 oldsmobile delta 88 royale
## 69 chevrolet chevelle malibu classic
## 70 chevrolet caprice classic
## 71 datsun f-10 hatchback
## 72 ford maverick
## 73 pontiac firebird
## 74 mazda 626
## 75 chevrolet nova
## 76 ford ltd
## 77 chevrolet malibu
## 78 ford pinto runabout
## 79 plymouth satellite custom (sw)
## 80 chrysler cordoba
## 81 datsun 710
## 82 subaru dl
## 83 ford fiesta
## 84 volvo 244dl
## 85 ford maverick
## 86 ford ltd landau
## 87 renault lecar deluxe
## 88 dodge monaco brougham
## 89 volkswagen 1131 deluxe sedan
## 90 toyota corona hardtop
## 91 mercury cougar brougham
## 92 plymouth custom suburb
## 93 dodge charger 2.2
## 94 datsun 310
## 95 vw dasher (diesel)
## 96 ford pinto
## 97 volkswagen scirocco
## 98 ford galaxie 500
## 99 chevrolet camaro
## 100 mercury marquis
## 101 plymouth cricket
## 102 renault 12 (sw)
## 103 fiat 124 sport coupe
## 104 dodge monaco (sw)
## 105 chevrolet woody
## 106 dodge colt
## 107 renault 18i
## 108 buick skylark 320
## 109 toyota corona
## 110 plymouth valiant
## 111 subaru
## 112 plymouth satellite
## 113 ford f250
## 114 ford mustang cobra
## 115 buick century
## 116 datsun 510 hatchback
## 117 pontiac lemans v6
## 118 honda civic cvcc
## 119 toyota corolla 1600 (sw)
## 120 opel 1900
## 121 peugeot 504
## 122 ford galaxie 500
## 123 oldsmobile cutlass ls
## 124 ford pinto
## 125 buick estate wagon (sw)
## 126 ford gran torino
## 127 chevrolet cavalier 2-door
## 128 volkswagen rabbit
## 129 buick estate wagon (sw)
## 130 peugeot 504 (sw)
## 131 bmw 2002
## 132 volkswagen jetta
## 133 dodge coronet brougham
## 134 chevrolet monte carlo s
## 135 chevy c20
## 136 ford ranger
## 137 ford fairmont (auto)
## 138 toyota corona liftback
## 139 plymouth fury iii
## 140 oldsmobile omega
## 141 amc ambassador dpl
## 142 honda civic
## 143 chrysler new yorker brougham
## 144 vw rabbit
## 145 chevrolet malibu classic (sw)
## 146 mazda glc 4
## 147 mercury capri v6
## 148 volkswagen rabbit l
## 149 amc gremlin
## 150 vw pickup
## 151 toyota tercel
## 152 pontiac grand prix
## 153 toyota corona
## 154 plymouth arrow gs
## 155 audi 4000
## 156 subaru
## 157 plymouth satellite custom
## 158 plymouth volare premier v8
## 159 plymouth reliant
## 160 pontiac astro
## 161 datsun 810 maxima
## 162 mercedes-benz 240d
## 163 plymouth cuda 340
## 164 amc ambassador brougham
## 165 fiat 124b
## 166 chevrolet caprice classic
## 167 dodge magnum xe
## 168 fiat 128
## 169 chevrolet vega (sw)
## 170 amc matador (sw)
## 171 audi 5000s (diesel)
## 172 dodge coronet custom
## 173 mercury lynx l
## 174 buick skyhawk
## 175 chevrolet monte carlo landau
## 176 honda civic cvcc
## 177 amc pacer d/l
## 178 ford granada l
## 179 volkswagen dasher
## 180 volvo 145e (sw)
## 181 saab 99le
## 182 plymouth duster
## 183 chevrolet nova
## 184 chevrolet nova
## 185 amc concord dl
## 186 oldsmobile vista cruiser
## 187 plymouth fury iii
## 188 saab 99gle
## 189 datsun 610
## 190 hi 1200d
## 191 toyota corolla
## 192 ford pinto (sw)
## 193 ford gran torino
## 194 toyota carina
## 195 ford pinto
## 196 ford fairmont 4
## 197 audi 100 ls
## 198 ford country
## 199 maxda rx3
## 200 honda accord lx
## 201 amc hornet
## 202 toyota cressida
## 203 ford maverick
## 204 toyota corolla liftback
## 205 volkswagen type 3
## 206 chevrolet caprice classic
## 207 plymouth satellite sebring
## 208 datsun 510
## 209 amc hornet sportabout (sw)
## 210 opel manta
## 211 toyota corolla
## 212 chrysler lebaron medallion
## 213 buick century 350
## 214 toyota corolla 1200
## 215 buick century special
## 216 dodge colt m/m
## 217 amc spirit dl
## 218 buick opel isuzu deluxe
## 219 amc matador
## 220 toyota corona
## 221 dodge colt hardtop
## 222 buick electra 225 custom
## 223 amc gremlin
## 224 buick century luxus (sw)
## 225 oldsmobile omega brougham
## 226 dodge dart custom
## 227 fiat 124 tc
## 228 pontiac j2000 se hatchback
## 229 plymouth horizon 4
## 230 volkswagen dasher
## 231 chevrolet vega
## 232 chevrolet vega
## 233 ford futura
## 234 honda accord
## 235 chevrolet bel air
## 236 toyota corona mark ii
## 237 dodge colt
## 238 dodge aspen
## 239 mercury zephyr 6
## 240 pontiac safari (sw)
## 241 volvo 144ea
## 242 toyota corona
## 243 ford pinto
## 244 datsun pl510
## 245 capri ii
## 246 dodge colt hatchback custom
## 247 plymouth duster
## 248 mercury grand marquis
## 249 toyota corolla
## 250 volvo 245
## 251 mazda rx-7 gs
## 252 dodge st. regis
## 253 chevrolet impala
## 254 plymouth grand fury
## 255 pontiac sunbird coupe
## 256 honda prelude
## 257 fiat strada custom
## 258 peugeot 604sl
## 259 fiat x1.9
## 260 peugeot 504
## 261 datsun b-210
## 262 mazda 626
## 263 volkswagen rabbit custom diesel
## 264 plymouth reliant
## 265 toyota corolla
## 266 renault 12tl
## 267 ford torino
## 268 chevrolet chevette
## 269 mazda glc
## 270 buick skylark
## 271 plymouth horizon
## 272 toyota corolla 1200
## 273 ford granada
## 274 buick regal sport coupe (turbo)
## 275 volkswagen dasher
## 276 ford granada gl
## 277 buick century
## 278 dodge colt (sw)
## 279 chevrolet cavalier
## 280 fiat 131
## 281 chevrolet chevelle malibu
## 282 chevrolet concours
## 283 chevrolet impala
## 284 amc matador
## 285 chevrolet monte carlo
## 286 ford mustang gl
## 287 amc concord dl 6
## 288 ford fairmont futura
## 289 mercury capri 2000
## 290 mercedes benz 300d
## 291 dodge d200
## 292 mercury monarch
## 293 chevrolet cavalier wagon
## 294 toyota mark ii
## 295 ford escort 2h
## 296 nissan stanza xe
## 297 plymouth sapporo
## 298 mazda glc custom l
## 299 honda civic
## 300 oldsmobile cutlass ciera (diesel)
## 301 triumph tr7 coupe
## 302 vokswagen rabbit
## 303 opel manta
## 304 volkswagen model 111
## 305 chrysler lebaron salon
## 306 chevrolet malibu
## 307 saab 99le
## 308 plymouth valiant
## 309 ford pinto
## 310 pontiac ventura sj
## 311 pontiac grand prix lj
## 312 toyota mark ii
## 313 chevy s-10
## 314 opel 1900
## 315 ford granada ghia
## 316 audi 5000
## 317 maxda glc deluxe
## 318 datsun b210
## 319 renault 5 gtl
## 320 ford country squire (sw)
## 321 chevy c10
## 322 pontiac phoenix
## 323 peugeot 504
## 324 ford maverick
## 325 amc hornet
## 326 toyota corolla
## 327 datsun 210
## 328 oldsmobile cutlass supreme
## 329 pontiac phoenix
## 330 ford mustang ii 2+2
## 331 peugeot 304
## 332 honda civic
## 333 ford maverick
## 334 plymouth volare custom
## 335 vw rabbit c (diesel)
## 336 datsun 810
## 337 amc pacer
## 338 fiat 128
## 339 peugeot 505s turbo diesel
## 340 chevrolet impala
## 341 pontiac phoenix lj
## 342 honda civic (auto)
## 343 chevrolet chevelle malibu
## 344 chrysler lebaron town @ country (sw)
## 345 ford country squire (sw)
## 346 buick skylark limited
## 347 chevrolet monte carlo landau
## 348 volkswagen rabbit
## 349 honda accord cvcc
## 350 oldsmobile cutlass salon brougham
## 351 ford mustang ii
## 352 ford escort 4w
## 353 chevrolet citation
## 354 cadillac eldorado
## 355 datsun 310 gx
## 356 dodge aspen se
## 357 ford thunderbird
## 358 amc rebel sst
## 359 peugeot 504
## 360 chevrolet chevette
## 361 mazda glc deluxe
## 362 mercury monarch ghia
## 363 amc concord d/l
## 364 datsun 280-zx
## 365 mazda rx-4
## 366 plymouth duster
## 367 amc concord
## 368 plymouth fury iii
## 369 datsun 210 mpg
## 370 buick century limited
## 371 chevrolet citation
## 372 vw rabbit custom
## 373 chevrolet nova custom
## 374 plymouth valiant custom
## 375 datsun b210 gx
## 376 volkswagen rabbit custom
## 377 volvo 264gl
## 378 ford gran torino (sw)
## 379 toyouta corona mark ii (sw)
## 380 amc hornet
## 381 mercury zephyr
## 382 amc matador
## 383 plymouth champ
## 384 toyota celica gt liftback
## 385 plymouth fury
## 386 honda civic 1300
## 387 amc matador
## 388 honda civic 1500 gl
## 389 ford gran torino
## 390 pontiac catalina brougham
## 391 chevrolet chevette
## 392 datsun 200-sx
## 393 buick lesabre custom
## 394 mercedes-benz 280s
## 395 plymouth horizon tc3
## 396 toyota corolla tercel
## 397 chevrolet chevette
## 398 amc gremlinConvertir la variable categórica de Cilindros a factor
- Para conocer su frecuencia con summary()
datos$cylinders <- factor(datos$cylinders, levels = c(3,4,5,6,8),
labels = c('3c', '4c','5c','6c','8c'))
summary(datos)## No mpg cylinders displacement horsepower
## Min. : 1.0 Min. : 9.00 3c: 4 Min. : 68.0 Min. : 46.0
## 1st Qu.:100.2 1st Qu.:17.50 4c:204 1st Qu.:104.2 1st Qu.: 76.0
## Median :199.5 Median :23.00 5c: 3 Median :148.5 Median : 92.0
## Mean :199.5 Mean :23.51 6c: 84 Mean :193.4 Mean :104.1
## 3rd Qu.:298.8 3rd Qu.:29.00 8c:103 3rd Qu.:262.0 3rd Qu.:125.0
## Max. :398.0 Max. :46.60 Max. :455.0 Max. :230.0
##
## weight acceleration model_year car_name
## Min. :1613 Min. : 8.00 Min. :70.00 ford pinto : 6
## 1st Qu.:2224 1st Qu.:13.82 1st Qu.:73.00 amc matador : 5
## Median :2804 Median :15.50 Median :76.00 ford maverick : 5
## Mean :2970 Mean :15.57 Mean :76.01 toyota corolla: 5
## 3rd Qu.:3608 3rd Qu.:17.18 3rd Qu.:79.00 amc gremlin : 4
## Max. :5140 Max. :24.80 Max. :82.00 amc hornet : 4
## (Other) :369Tabla de correlación
cor(x=datos[,-c(1,3,8,9)], method = "pearson")## mpg displacement horsepower weight acceleration
## mpg 1.0000000 -0.8042028 -0.7756163 -0.8317409 0.4202889
## displacement -0.8042028 1.0000000 0.8975294 0.9328241 -0.5436841
## horsepower -0.7756163 0.8975294 1.0000000 0.8636062 -0.6880550
## weight -0.8317409 0.9328241 0.8636062 1.0000000 -0.4174573
## acceleration 0.4202889 -0.5436841 -0.6880550 -0.4174573 1.0000000Mostrando correlaciones con pairs()
pairs(x=datos[,-c(1,3,8,9)], lower.panel = NULL)
Correlación con corrplot()
corrplot(corr = cor(x=datos[,-c(1,3,8,9)], method = "pearson"), method = "number")
Dividir conjunto de entrenamiento y datos de validación o prueba
set.seed(2018)
entrena <- createDataPartition(datos$mpg, p=0.7, list = FALSE)
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## [280,] 398nrow(entrena) # Cuantos datos de entrenamiento## [1] 280Generando el modelo lineal con los datos de entrenamiento
- Los datos de entrenamiento son los registros de datos que están en esas posiciones que fueron generados por createDataPartition
- Los datos de validación son los que no son d entrenamiento
- No nos interesan las columnas 1 8 y 9 de los datos, no resultan significativas para la regresión.
datosentrenamiento <- datos[entrena, -c(1,8,9)]
datosvalidacion <- datos[-entrena, -c(1,8,9)]
# Veremos que no son los mis datos los de entrenamiento y los datos de validación
head(datos)## No mpg cylinders displacement horsepower weight acceleration model_year
## 1 1 28 4c 140 90 2264 15.5 71
## 2 2 19 3c 70 97 2330 13.5 72
## 3 3 36 4c 107 75 2205 14.5 82
## 4 4 28 4c 97 92 2288 17.0 72
## 5 5 21 6c 199 90 2648 15.0 70
## 6 6 23 4c 115 95 2694 15.0 75
## car_name
## 1 chevrolet vega 2300
## 2 mazda rx2 coupe
## 3 honda accord
## 4 datsun 510 (sw)
## 5 amc gremlin
## 6 audi 100lshead(datosentrenamiento)## mpg cylinders displacement horsepower weight acceleration
## 3 36.0 4c 107 75 2205 14.5
## 4 28.0 4c 97 92 2288 17.0
## 5 21.0 6c 199 90 2648 15.0
## 6 23.0 4c 115 95 2694 15.0
## 7 15.5 8c 304 120 3962 13.9
## 8 32.9 4c 119 100 2615 14.8head(datosvalidacion)## mpg cylinders displacement horsepower weight acceleration
## 1 28.0 4c 140 90 2264 15.5
## 2 19.0 3c 70 97 2330 13.5
## 11 12.0 8c 429 198 4952 11.5
## 13 13.0 8c 302 130 3870 15.0
## 14 27.9 4c 156 105 2800 14.4
## 18 14.0 8c 400 175 4385 12.0modelo <- lm(mpg ~ ., data = datosentrenamiento)
modelo##
## Call:
## lm(formula = mpg ~ ., data = datosentrenamiento)
##
## Coefficients:
## (Intercept) cylinders4c cylinders5c cylinders6c cylinders8c
## 37.284202 6.231475 8.248195 2.131026 4.568171
## displacement horsepower weight acceleration
## 0.002245 -0.057543 -0.004665 0.050745- Prediccion de mpg está dada por la fórmula:
- mpg = 37.284202 + 6.231475 * 4c + 8.248195 * 5c + 2.131026 * 6c + 4.568171 * 8c + 0.002245 * displacement - 0.057543 * horsepower -0.004665 * weight + 0.050745 * acceleration ## Summary(modelo)
- La pregunta es: ¿Éste modelo predice bien o predice mal?
- No todas las variables son importantes, hay algunas que tienen mayor presencia que el resto.
- summary(), para ver otras variables
summary(modelo)##
## Call:
## lm(formula = mpg ~ ., data = datosentrenamiento)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0606 -2.4686 -0.4435 1.9821 16.0907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.2842024 3.6497412 10.216 < 2e-16 ***
## cylinders4c 6.2314753 2.4926855 2.500 0.01301 *
## cylinders5c 8.2481946 3.8091396 2.165 0.03123 *
## cylinders6c 2.1310256 2.7759570 0.768 0.44335
## cylinders8c 4.5681710 3.2054454 1.425 0.15527
## displacement 0.0022449 0.0108924 0.206 0.83687
## horsepower -0.0575428 0.0202773 -2.838 0.00489 **
## weight -0.0046652 0.0009999 -4.665 4.84e-06 ***
## acceleration 0.0507454 0.1443575 0.352 0.72547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.092 on 271 degrees of freedom
## Multiple R-squared: 0.7304, Adjusted R-squared: 0.7224
## F-statistic: 91.75 on 8 and 271 DF, p-value: < 2.2e-16- Las estrellitas significa que las variabels aportan mayor significación a la variable independiente “mpg”
- Los residuos es como se distribuyen los errores en la estimación
- Los errores que se cometen en la regresión con el boxplot observamos que tanto están cerca de la mediana, en realidad son pocos que salen
boxplot(modelo$residuals)
Error cuadrático medio del modelo en los datos de entrenamiento
sqrt(mean((modelo$fitted.values - datosentrenamiento$mpg) ^ 2))## [1] 4.026021Evaluar con los datos de validación que no serán los de entrenamiento
mpg_prediccion <- predict(modelo, datosvalidacion)
# mpg_prediccion
# summary(prediccion)Error cuadrático medio del modelo en los datos de validación
sqrt(mean((mpg_prediccion - datosvalidacion$mpg) ^ 2))## [1] 3.894627Gráficas varias
par(mfrow=c(2,2))
plot(modelo)