auto<-read.csv("Auto.csv", TRUE, na.strings="?")
auto<-na.omit(auto)
#1A :which of the predictorrs are quantitative and which are qualitative?
str(auto)
## 'data.frame': 392 obs. of 9 variables:
## $ mpg : num 18 15 18 16 17 15 14 14 14 15 ...
## $ cylinders : int 8 8 8 8 8 8 8 8 8 8 ...
## $ displacement: num 307 350 318 304 302 429 454 440 455 390 ...
## $ horsepower : int 130 165 150 150 140 198 220 215 225 190 ...
## $ weight : int 3504 3693 3436 3433 3449 4341 4354 4312 4425 3850 ...
## $ acceleration: num 12 11.5 11 12 10.5 10 9 8.5 10 8.5 ...
## $ year : int 70 70 70 70 70 70 70 70 70 70 ...
## $ origin : int 1 1 1 1 1 1 1 1 1 1 ...
## $ name : Factor w/ 304 levels "amc ambassador brougham",..: 49 36 231 14 161 141 54 223 241 2 ...
## - attr(*, "na.action")= 'omit' Named int 33 127 331 337 355
## ..- attr(*, "names")= chr "33" "127" "331" "337" ...
#1A: Quantitative: mpg, cylinders, displacement, horsepower, weight, acceleration, year, origin,
#1A: Qualitative: name
#1B: What is the range of each quantitative predictor?
#mpg
range(auto$mpg)
## [1] 9.0 46.6
#cylinders
range(auto$cylinders)
## [1] 3 8
#dispalcement
range(auto$displacement)
## [1] 68 455
#horsepower
range(auto$horsepower)
## [1] 46 230
#weight
range(auto$weight)
## [1] 1613 5140
#acceleration
range(auto$acceleration)
## [1] 8.0 24.8
#year
range(auto$year)
## [1] 70 82
#origin
range(auto$origin)
## [1] 1 3
#1C: What is the mean and standard deviation of each quantitative predictor?
#mpg
mean(auto$mpg)
## [1] 23.44592
sd(auto$mpg)
## [1] 7.805007
#cylinders
mean(auto$cylinders)
## [1] 5.471939
sd(auto$cylinders)
## [1] 1.705783
#horsepower
mean(auto$horsepower)
## [1] 104.4694
sd(auto$horsepower)
## [1] 38.49116
#displacement
mean(auto$displacement)
## [1] 194.412
sd(auto$displacement)
## [1] 104.644
#weight
mean(auto$weight)
## [1] 2977.584
sd(auto$weight)
## [1] 849.4026
#acceleration
mean(auto$acceleration)
## [1] 15.54133
sd(auto$acceleration)
## [1] 2.758864
#year
mean(auto$year)
## [1] 75.97959
sd(auto$year)
## [1] 3.683737
#origin
mean(auto$origin)
## [1] 1.576531
sd(auto$origin)
## [1] 0.8055182
#1D: now remove the 10th through 85th observation. What is the range, mean and standard deviation of each predictorin the subset of the data that remains?
auto_matrix<- auto[-c(10:84),-9]
auto_matrix
## mpg cylinders displacement horsepower weight acceleration year origin
## 1 18.0 8 307 130 3504 12.0 70 1
## 2 15.0 8 350 165 3693 11.5 70 1
## 3 18.0 8 318 150 3436 11.0 70 1
## 4 16.0 8 304 150 3433 12.0 70 1
## 5 17.0 8 302 140 3449 10.5 70 1
## 6 15.0 8 429 198 4341 10.0 70 1
## 7 14.0 8 454 220 4354 9.0 70 1
## 8 14.0 8 440 215 4312 8.5 70 1
## 9 14.0 8 455 225 4425 10.0 70 1
## 86 13.0 8 350 175 4100 13.0 73 1
## 87 14.0 8 304 150 3672 11.5 73 1
## 88 13.0 8 350 145 3988 13.0 73 1
## 89 14.0 8 302 137 4042 14.5 73 1
## 90 15.0 8 318 150 3777 12.5 73 1
## 91 12.0 8 429 198 4952 11.5 73 1
## 92 13.0 8 400 150 4464 12.0 73 1
## 93 13.0 8 351 158 4363 13.0 73 1
## 94 14.0 8 318 150 4237 14.5 73 1
## 95 13.0 8 440 215 4735 11.0 73 1
## 96 12.0 8 455 225 4951 11.0 73 1
## 97 13.0 8 360 175 3821 11.0 73 1
## 98 18.0 6 225 105 3121 16.5 73 1
## 99 16.0 6 250 100 3278 18.0 73 1
## 100 18.0 6 232 100 2945 16.0 73 1
## 101 18.0 6 250 88 3021 16.5 73 1
## 102 23.0 6 198 95 2904 16.0 73 1
## 103 26.0 4 97 46 1950 21.0 73 2
## 104 11.0 8 400 150 4997 14.0 73 1
## 105 12.0 8 400 167 4906 12.5 73 1
## 106 13.0 8 360 170 4654 13.0 73 1
## 107 12.0 8 350 180 4499 12.5 73 1
## 108 18.0 6 232 100 2789 15.0 73 1
## 109 20.0 4 97 88 2279 19.0 73 3
## 110 21.0 4 140 72 2401 19.5 73 1
## 111 22.0 4 108 94 2379 16.5 73 3
## 112 18.0 3 70 90 2124 13.5 73 3
## 113 19.0 4 122 85 2310 18.5 73 1
## 114 21.0 6 155 107 2472 14.0 73 1
## 115 26.0 4 98 90 2265 15.5 73 2
## 116 15.0 8 350 145 4082 13.0 73 1
## 117 16.0 8 400 230 4278 9.5 73 1
## 118 29.0 4 68 49 1867 19.5 73 2
## 119 24.0 4 116 75 2158 15.5 73 2
## 120 20.0 4 114 91 2582 14.0 73 2
## 121 19.0 4 121 112 2868 15.5 73 2
## 122 15.0 8 318 150 3399 11.0 73 1
## 123 24.0 4 121 110 2660 14.0 73 2
## 124 20.0 6 156 122 2807 13.5 73 3
## 125 11.0 8 350 180 3664 11.0 73 1
## 126 20.0 6 198 95 3102 16.5 74 1
## 128 19.0 6 232 100 2901 16.0 74 1
## 129 15.0 6 250 100 3336 17.0 74 1
## 130 31.0 4 79 67 1950 19.0 74 3
## 131 26.0 4 122 80 2451 16.5 74 1
## 132 32.0 4 71 65 1836 21.0 74 3
## 133 25.0 4 140 75 2542 17.0 74 1
## 134 16.0 6 250 100 3781 17.0 74 1
## 135 16.0 6 258 110 3632 18.0 74 1
## 136 18.0 6 225 105 3613 16.5 74 1
## 137 16.0 8 302 140 4141 14.0 74 1
## 138 13.0 8 350 150 4699 14.5 74 1
## 139 14.0 8 318 150 4457 13.5 74 1
## 140 14.0 8 302 140 4638 16.0 74 1
## 141 14.0 8 304 150 4257 15.5 74 1
## 142 29.0 4 98 83 2219 16.5 74 2
## 143 26.0 4 79 67 1963 15.5 74 2
## 144 26.0 4 97 78 2300 14.5 74 2
## 145 31.0 4 76 52 1649 16.5 74 3
## 146 32.0 4 83 61 2003 19.0 74 3
## 147 28.0 4 90 75 2125 14.5 74 1
## 148 24.0 4 90 75 2108 15.5 74 2
## 149 26.0 4 116 75 2246 14.0 74 2
## 150 24.0 4 120 97 2489 15.0 74 3
## 151 26.0 4 108 93 2391 15.5 74 3
## 152 31.0 4 79 67 2000 16.0 74 2
## 153 19.0 6 225 95 3264 16.0 75 1
## 154 18.0 6 250 105 3459 16.0 75 1
## 155 15.0 6 250 72 3432 21.0 75 1
## 156 15.0 6 250 72 3158 19.5 75 1
## 157 16.0 8 400 170 4668 11.5 75 1
## 158 15.0 8 350 145 4440 14.0 75 1
## 159 16.0 8 318 150 4498 14.5 75 1
## 160 14.0 8 351 148 4657 13.5 75 1
## 161 17.0 6 231 110 3907 21.0 75 1
## 162 16.0 6 250 105 3897 18.5 75 1
## 163 15.0 6 258 110 3730 19.0 75 1
## 164 18.0 6 225 95 3785 19.0 75 1
## 165 21.0 6 231 110 3039 15.0 75 1
## 166 20.0 8 262 110 3221 13.5 75 1
## 167 13.0 8 302 129 3169 12.0 75 1
## 168 29.0 4 97 75 2171 16.0 75 3
## 169 23.0 4 140 83 2639 17.0 75 1
## 170 20.0 6 232 100 2914 16.0 75 1
## 171 23.0 4 140 78 2592 18.5 75 1
## 172 24.0 4 134 96 2702 13.5 75 3
## 173 25.0 4 90 71 2223 16.5 75 2
## 174 24.0 4 119 97 2545 17.0 75 3
## 175 18.0 6 171 97 2984 14.5 75 1
## 176 29.0 4 90 70 1937 14.0 75 2
## 177 19.0 6 232 90 3211 17.0 75 1
## 178 23.0 4 115 95 2694 15.0 75 2
## 179 23.0 4 120 88 2957 17.0 75 2
## 180 22.0 4 121 98 2945 14.5 75 2
## 181 25.0 4 121 115 2671 13.5 75 2
## 182 33.0 4 91 53 1795 17.5 75 3
## 183 28.0 4 107 86 2464 15.5 76 2
## 184 25.0 4 116 81 2220 16.9 76 2
## 185 25.0 4 140 92 2572 14.9 76 1
## 186 26.0 4 98 79 2255 17.7 76 1
## 187 27.0 4 101 83 2202 15.3 76 2
## 188 17.5 8 305 140 4215 13.0 76 1
## 189 16.0 8 318 150 4190 13.0 76 1
## 190 15.5 8 304 120 3962 13.9 76 1
## 191 14.5 8 351 152 4215 12.8 76 1
## 192 22.0 6 225 100 3233 15.4 76 1
## 193 22.0 6 250 105 3353 14.5 76 1
## 194 24.0 6 200 81 3012 17.6 76 1
## 195 22.5 6 232 90 3085 17.6 76 1
## 196 29.0 4 85 52 2035 22.2 76 1
## 197 24.5 4 98 60 2164 22.1 76 1
## 198 29.0 4 90 70 1937 14.2 76 2
## 199 33.0 4 91 53 1795 17.4 76 3
## 200 20.0 6 225 100 3651 17.7 76 1
## 201 18.0 6 250 78 3574 21.0 76 1
## 202 18.5 6 250 110 3645 16.2 76 1
## 203 17.5 6 258 95 3193 17.8 76 1
## 204 29.5 4 97 71 1825 12.2 76 2
## 205 32.0 4 85 70 1990 17.0 76 3
## 206 28.0 4 97 75 2155 16.4 76 3
## 207 26.5 4 140 72 2565 13.6 76 1
## 208 20.0 4 130 102 3150 15.7 76 2
## 209 13.0 8 318 150 3940 13.2 76 1
## 210 19.0 4 120 88 3270 21.9 76 2
## 211 19.0 6 156 108 2930 15.5 76 3
## 212 16.5 6 168 120 3820 16.7 76 2
## 213 16.5 8 350 180 4380 12.1 76 1
## 214 13.0 8 350 145 4055 12.0 76 1
## 215 13.0 8 302 130 3870 15.0 76 1
## 216 13.0 8 318 150 3755 14.0 76 1
## 217 31.5 4 98 68 2045 18.5 77 3
## 218 30.0 4 111 80 2155 14.8 77 1
## 219 36.0 4 79 58 1825 18.6 77 2
## 220 25.5 4 122 96 2300 15.5 77 1
## 221 33.5 4 85 70 1945 16.8 77 3
## 222 17.5 8 305 145 3880 12.5 77 1
## 223 17.0 8 260 110 4060 19.0 77 1
## 224 15.5 8 318 145 4140 13.7 77 1
## 225 15.0 8 302 130 4295 14.9 77 1
## 226 17.5 6 250 110 3520 16.4 77 1
## 227 20.5 6 231 105 3425 16.9 77 1
## 228 19.0 6 225 100 3630 17.7 77 1
## 229 18.5 6 250 98 3525 19.0 77 1
## 230 16.0 8 400 180 4220 11.1 77 1
## 231 15.5 8 350 170 4165 11.4 77 1
## 232 15.5 8 400 190 4325 12.2 77 1
## 233 16.0 8 351 149 4335 14.5 77 1
## 234 29.0 4 97 78 1940 14.5 77 2
## 235 24.5 4 151 88 2740 16.0 77 1
## 236 26.0 4 97 75 2265 18.2 77 3
## 237 25.5 4 140 89 2755 15.8 77 1
## 238 30.5 4 98 63 2051 17.0 77 1
## 239 33.5 4 98 83 2075 15.9 77 1
## 240 30.0 4 97 67 1985 16.4 77 3
## 241 30.5 4 97 78 2190 14.1 77 2
## 242 22.0 6 146 97 2815 14.5 77 3
## 243 21.5 4 121 110 2600 12.8 77 2
## 244 21.5 3 80 110 2720 13.5 77 3
## 245 43.1 4 90 48 1985 21.5 78 2
## 246 36.1 4 98 66 1800 14.4 78 1
## 247 32.8 4 78 52 1985 19.4 78 3
## 248 39.4 4 85 70 2070 18.6 78 3
## 249 36.1 4 91 60 1800 16.4 78 3
## 250 19.9 8 260 110 3365 15.5 78 1
## 251 19.4 8 318 140 3735 13.2 78 1
## 252 20.2 8 302 139 3570 12.8 78 1
## 253 19.2 6 231 105 3535 19.2 78 1
## 254 20.5 6 200 95 3155 18.2 78 1
## 255 20.2 6 200 85 2965 15.8 78 1
## 256 25.1 4 140 88 2720 15.4 78 1
## 257 20.5 6 225 100 3430 17.2 78 1
## 258 19.4 6 232 90 3210 17.2 78 1
## 259 20.6 6 231 105 3380 15.8 78 1
## 260 20.8 6 200 85 3070 16.7 78 1
## 261 18.6 6 225 110 3620 18.7 78 1
## 262 18.1 6 258 120 3410 15.1 78 1
## 263 19.2 8 305 145 3425 13.2 78 1
## 264 17.7 6 231 165 3445 13.4 78 1
## 265 18.1 8 302 139 3205 11.2 78 1
## 266 17.5 8 318 140 4080 13.7 78 1
## 267 30.0 4 98 68 2155 16.5 78 1
## 268 27.5 4 134 95 2560 14.2 78 3
## 269 27.2 4 119 97 2300 14.7 78 3
## 270 30.9 4 105 75 2230 14.5 78 1
## 271 21.1 4 134 95 2515 14.8 78 3
## 272 23.2 4 156 105 2745 16.7 78 1
## 273 23.8 4 151 85 2855 17.6 78 1
## 274 23.9 4 119 97 2405 14.9 78 3
## 275 20.3 5 131 103 2830 15.9 78 2
## 276 17.0 6 163 125 3140 13.6 78 2
## 277 21.6 4 121 115 2795 15.7 78 2
## 278 16.2 6 163 133 3410 15.8 78 2
## 279 31.5 4 89 71 1990 14.9 78 2
## 280 29.5 4 98 68 2135 16.6 78 3
## 281 21.5 6 231 115 3245 15.4 79 1
## 282 19.8 6 200 85 2990 18.2 79 1
## 283 22.3 4 140 88 2890 17.3 79 1
## 284 20.2 6 232 90 3265 18.2 79 1
## 285 20.6 6 225 110 3360 16.6 79 1
## 286 17.0 8 305 130 3840 15.4 79 1
## 287 17.6 8 302 129 3725 13.4 79 1
## 288 16.5 8 351 138 3955 13.2 79 1
## 289 18.2 8 318 135 3830 15.2 79 1
## 290 16.9 8 350 155 4360 14.9 79 1
## 291 15.5 8 351 142 4054 14.3 79 1
## 292 19.2 8 267 125 3605 15.0 79 1
## 293 18.5 8 360 150 3940 13.0 79 1
## 294 31.9 4 89 71 1925 14.0 79 2
## 295 34.1 4 86 65 1975 15.2 79 3
## 296 35.7 4 98 80 1915 14.4 79 1
## 297 27.4 4 121 80 2670 15.0 79 1
## 298 25.4 5 183 77 3530 20.1 79 2
## 299 23.0 8 350 125 3900 17.4 79 1
## 300 27.2 4 141 71 3190 24.8 79 2
## 301 23.9 8 260 90 3420 22.2 79 1
## 302 34.2 4 105 70 2200 13.2 79 1
## 303 34.5 4 105 70 2150 14.9 79 1
## 304 31.8 4 85 65 2020 19.2 79 3
## 305 37.3 4 91 69 2130 14.7 79 2
## 306 28.4 4 151 90 2670 16.0 79 1
## 307 28.8 6 173 115 2595 11.3 79 1
## 308 26.8 6 173 115 2700 12.9 79 1
## 309 33.5 4 151 90 2556 13.2 79 1
## 310 41.5 4 98 76 2144 14.7 80 2
## 311 38.1 4 89 60 1968 18.8 80 3
## 312 32.1 4 98 70 2120 15.5 80 1
## 313 37.2 4 86 65 2019 16.4 80 3
## 314 28.0 4 151 90 2678 16.5 80 1
## 315 26.4 4 140 88 2870 18.1 80 1
## 316 24.3 4 151 90 3003 20.1 80 1
## 317 19.1 6 225 90 3381 18.7 80 1
## 318 34.3 4 97 78 2188 15.8 80 2
## 319 29.8 4 134 90 2711 15.5 80 3
## 320 31.3 4 120 75 2542 17.5 80 3
## 321 37.0 4 119 92 2434 15.0 80 3
## 322 32.2 4 108 75 2265 15.2 80 3
## 323 46.6 4 86 65 2110 17.9 80 3
## 324 27.9 4 156 105 2800 14.4 80 1
## 325 40.8 4 85 65 2110 19.2 80 3
## 326 44.3 4 90 48 2085 21.7 80 2
## 327 43.4 4 90 48 2335 23.7 80 2
## 328 36.4 5 121 67 2950 19.9 80 2
## 329 30.0 4 146 67 3250 21.8 80 2
## 330 44.6 4 91 67 1850 13.8 80 3
## 332 33.8 4 97 67 2145 18.0 80 3
## 333 29.8 4 89 62 1845 15.3 80 2
## 334 32.7 6 168 132 2910 11.4 80 3
## 335 23.7 3 70 100 2420 12.5 80 3
## 336 35.0 4 122 88 2500 15.1 80 2
## 338 32.4 4 107 72 2290 17.0 80 3
## 339 27.2 4 135 84 2490 15.7 81 1
## 340 26.6 4 151 84 2635 16.4 81 1
## 341 25.8 4 156 92 2620 14.4 81 1
## 342 23.5 6 173 110 2725 12.6 81 1
## 343 30.0 4 135 84 2385 12.9 81 1
## 344 39.1 4 79 58 1755 16.9 81 3
## 345 39.0 4 86 64 1875 16.4 81 1
## 346 35.1 4 81 60 1760 16.1 81 3
## 347 32.3 4 97 67 2065 17.8 81 3
## 348 37.0 4 85 65 1975 19.4 81 3
## 349 37.7 4 89 62 2050 17.3 81 3
## 350 34.1 4 91 68 1985 16.0 81 3
## 351 34.7 4 105 63 2215 14.9 81 1
## 352 34.4 4 98 65 2045 16.2 81 1
## 353 29.9 4 98 65 2380 20.7 81 1
## 354 33.0 4 105 74 2190 14.2 81 2
## 356 33.7 4 107 75 2210 14.4 81 3
## 357 32.4 4 108 75 2350 16.8 81 3
## 358 32.9 4 119 100 2615 14.8 81 3
## 359 31.6 4 120 74 2635 18.3 81 3
## 360 28.1 4 141 80 3230 20.4 81 2
## 361 30.7 6 145 76 3160 19.6 81 2
## 362 25.4 6 168 116 2900 12.6 81 3
## 363 24.2 6 146 120 2930 13.8 81 3
## 364 22.4 6 231 110 3415 15.8 81 1
## 365 26.6 8 350 105 3725 19.0 81 1
## 366 20.2 6 200 88 3060 17.1 81 1
## 367 17.6 6 225 85 3465 16.6 81 1
## 368 28.0 4 112 88 2605 19.6 82 1
## 369 27.0 4 112 88 2640 18.6 82 1
## 370 34.0 4 112 88 2395 18.0 82 1
## 371 31.0 4 112 85 2575 16.2 82 1
## 372 29.0 4 135 84 2525 16.0 82 1
## 373 27.0 4 151 90 2735 18.0 82 1
## 374 24.0 4 140 92 2865 16.4 82 1
## 375 36.0 4 105 74 1980 15.3 82 2
## 376 37.0 4 91 68 2025 18.2 82 3
## 377 31.0 4 91 68 1970 17.6 82 3
## 378 38.0 4 105 63 2125 14.7 82 1
## 379 36.0 4 98 70 2125 17.3 82 1
## 380 36.0 4 120 88 2160 14.5 82 3
## 381 36.0 4 107 75 2205 14.5 82 3
## 382 34.0 4 108 70 2245 16.9 82 3
## 383 38.0 4 91 67 1965 15.0 82 3
## 384 32.0 4 91 67 1965 15.7 82 3
## 385 38.0 4 91 67 1995 16.2 82 3
## 386 25.0 6 181 110 2945 16.4 82 1
## 387 38.0 6 262 85 3015 17.0 82 1
## 388 26.0 4 156 92 2585 14.5 82 1
## 389 22.0 6 232 112 2835 14.7 82 1
## 390 32.0 4 144 96 2665 13.9 82 3
## 391 36.0 4 135 84 2370 13.0 82 1
## 392 27.0 4 151 90 2950 17.3 82 1
## 393 27.0 4 140 86 2790 15.6 82 1
## 394 44.0 4 97 52 2130 24.6 82 2
## 395 32.0 4 135 84 2295 11.6 82 1
## 396 28.0 4 120 79 2625 18.6 82 1
## 397 31.0 4 119 82 2720 19.4 82 1
range(auto_matrix$mpg)
## [1] 11.0 46.6
range(auto_matrix$cylinders)
## [1] 3 8
range(auto_matrix$displacement)
## [1] 68 455
range(auto_matrix$horsepower)
## [1] 46 230
range(auto_matrix$weight)
## [1] 1649 4997
range(auto_matrix$acceleration)
## [1] 8.5 24.8
range(auto_matrix$year)
## [1] 70 82
range(auto_matrix$origin)
## [1] 1 3
mean(auto_matrix$mpg)
## [1] 24.36845
mean(auto_matrix$cylinders)
## [1] 5.381703
mean(auto_matrix$displacement)
## [1] 187.7539
mean(auto_matrix$horsepower)
## [1] 100.9558
mean(auto_matrix$weight)
## [1] 2939.644
mean(auto_matrix$acceleration)
## [1] 15.7183
mean(auto_matrix$year)
## [1] 77.13249
mean(auto_matrix$origin)
## [1] 1.599369
sd(auto_matrix$mpg)
## [1] 7.880898
sd(auto_matrix$cylinders)
## [1] 1.658135
sd(auto_matrix$displacement)
## [1] 99.93949
sd(auto_matrix$horsepower)
## [1] 35.89557
sd(auto_matrix$weight)
## [1] 812.6496
sd(auto_matrix$acceleration)
## [1] 2.693813
sd(auto_matrix$year)
## [1] 3.110026
sd(auto_matrix$origin)
## [1] 0.8193079
#1E create at least 3 plots, highlighting relationships among predictors. Comment on your findings.
pairs(auto_matrix)
auto$cylinders<-as.factor(auto$cylinders)
plot(auto$cylinders, auto$mpg)
plot(auto$year, auto$mpg)
plot(auto$weight, auto$acceleration)
