Math 239 Homework 1
Monce Barajas Gomez
12/1/2019
Auto <- read.table("http://faculty.marshall.usc.edu/gareth-james/ISL/Auto.data",
header=TRUE,
na.strings = "?")
Auto=na.omit(Auto) #removes missing rows
names(Auto)## [1] "mpg" "cylinders" "displacement" "horsepower"
## [5] "weight" "acceleration" "year" "origin"
## [9] "name"Problem 1:
Part A: There are 9 variables variables. 7 variables are quantitative which include mpg, weight, cylinders, displacement, horsepower, acceleration, and year.The variables of name and origin are qualitative.
Part B:
range(Auto$mpg)## [1] 9.0 46.6range(Auto$cylinders)## [1] 3 8range(Auto$displacement)## [1] 68 455range(Auto$horsepower)## [1] 46 230range(Auto$acceleration)## [1] 8.0 24.8range(Auto$year)## [1] 70 82range(Auto$weight)## [1] 1613 5140Ranges: weight 46 - 230 mpg 9.0 - 46.6 cylinders 3 - 8 displacement 68 - 455 horsepower 46 - 230 weight 1613 - 5140 acceleration 8.0 - 24.8 year 70 - 82
Part C:
mean(Auto$mpg)## [1] 23.44592mean(Auto$cylinders)## [1] 5.471939mean(Auto$displacement)## [1] 194.412mean(Auto$horsepower)## [1] 104.4694mean(Auto$acceleration)## [1] 15.54133mean(Auto$year)## [1] 75.97959mean(Auto$weight)## [1] 2977.584sd(Auto$mpg)## [1] 7.805007sd(Auto$cylinders)## [1] 1.705783sd(Auto$displacement)## [1] 104.644sd(Auto$horsepower)## [1] 38.49116sd(Auto$acceleration)## [1] 2.758864sd(Auto$year)## [1] 3.683737sd(Auto$weight)## [1] 849.4026Means: mpg 23.44592 cylinder 5.471939 displacement 194.412 horsepower 104.4694 weight 2977.584 acceleration 15.54133 year 75.97959
Standard Deviations: mpg 7.805007 cylinders 1.705783 displacement 104.644 horsepower 38.49116 weight 849.4026 acceleration 2.758864 year 3.683737
Part D
NewAuto <- Auto[-c(10:85), ]
NewAuto## 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
## 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
## name
## 1 chevrolet chevelle malibu
## 2 buick skylark 320
## 3 plymouth satellite
## 4 amc rebel sst
## 5 ford torino
## 6 ford galaxie 500
## 7 chevrolet impala
## 8 plymouth fury iii
## 9 pontiac catalina
## 87 amc matador
## 88 chevrolet malibu
## 89 ford gran torino
## 90 dodge coronet custom
## 91 mercury marquis brougham
## 92 chevrolet caprice classic
## 93 ford ltd
## 94 plymouth fury gran sedan
## 95 chrysler new yorker brougham
## 96 buick electra 225 custom
## 97 amc ambassador brougham
## 98 plymouth valiant
## 99 chevrolet nova custom
## 100 amc hornet
## 101 ford maverick
## 102 plymouth duster
## 103 volkswagen super beetle
## 104 chevrolet impala
## 105 ford country
## 106 plymouth custom suburb
## 107 oldsmobile vista cruiser
## 108 amc gremlin
## 109 toyota carina
## 110 chevrolet vega
## 111 datsun 610
## 112 maxda rx3
## 113 ford pinto
## 114 mercury capri v6
## 115 fiat 124 sport coupe
## 116 chevrolet monte carlo s
## 117 pontiac grand prix
## 118 fiat 128
## 119 opel manta
## 120 audi 100ls
## 121 volvo 144ea
## 122 dodge dart custom
## 123 saab 99le
## 124 toyota mark ii
## 125 oldsmobile omega
## 126 plymouth duster
## 128 amc hornet
## 129 chevrolet nova
## 130 datsun b210
## 131 ford pinto
## 132 toyota corolla 1200
## 133 chevrolet vega
## 134 chevrolet chevelle malibu classic
## 135 amc matador
## 136 plymouth satellite sebring
## 137 ford gran torino
## 138 buick century luxus (sw)
## 139 dodge coronet custom (sw)
## 140 ford gran torino (sw)
## 141 amc matador (sw)
## 142 audi fox
## 143 volkswagen dasher
## 144 opel manta
## 145 toyota corona
## 146 datsun 710
## 147 dodge colt
## 148 fiat 128
## 149 fiat 124 tc
## 150 honda civic
## 151 subaru
## 152 fiat x1.9
## 153 plymouth valiant custom
## 154 chevrolet nova
## 155 mercury monarch
## 156 ford maverick
## 157 pontiac catalina
## 158 chevrolet bel air
## 159 plymouth grand fury
## 160 ford ltd
## 161 buick century
## 162 chevroelt chevelle malibu
## 163 amc matador
## 164 plymouth fury
## 165 buick skyhawk
## 166 chevrolet monza 2+2
## 167 ford mustang ii
## 168 toyota corolla
## 169 ford pinto
## 170 amc gremlin
## 171 pontiac astro
## 172 toyota corona
## 173 volkswagen dasher
## 174 datsun 710
## 175 ford pinto
## 176 volkswagen rabbit
## 177 amc pacer
## 178 audi 100ls
## 179 peugeot 504
## 180 volvo 244dl
## 181 saab 99le
## 182 honda civic cvcc
## 183 fiat 131
## 184 opel 1900
## 185 capri ii
## 186 dodge colt
## 187 renault 12tl
## 188 chevrolet chevelle malibu classic
## 189 dodge coronet brougham
## 190 amc matador
## 191 ford gran torino
## 192 plymouth valiant
## 193 chevrolet nova
## 194 ford maverick
## 195 amc hornet
## 196 chevrolet chevette
## 197 chevrolet woody
## 198 vw rabbit
## 199 honda civic
## 200 dodge aspen se
## 201 ford granada ghia
## 202 pontiac ventura sj
## 203 amc pacer d/l
## 204 volkswagen rabbit
## 205 datsun b-210
## 206 toyota corolla
## 207 ford pinto
## 208 volvo 245
## 209 plymouth volare premier v8
## 210 peugeot 504
## 211 toyota mark ii
## 212 mercedes-benz 280s
## 213 cadillac seville
## 214 chevy c10
## 215 ford f108
## 216 dodge d100
## 217 honda accord cvcc
## 218 buick opel isuzu deluxe
## 219 renault 5 gtl
## 220 plymouth arrow gs
## 221 datsun f-10 hatchback
## 222 chevrolet caprice classic
## 223 oldsmobile cutlass supreme
## 224 dodge monaco brougham
## 225 mercury cougar brougham
## 226 chevrolet concours
## 227 buick skylark
## 228 plymouth volare custom
## 229 ford granada
## 230 pontiac grand prix lj
## 231 chevrolet monte carlo landau
## 232 chrysler cordoba
## 233 ford thunderbird
## 234 volkswagen rabbit custom
## 235 pontiac sunbird coupe
## 236 toyota corolla liftback
## 237 ford mustang ii 2+2
## 238 chevrolet chevette
## 239 dodge colt m/m
## 240 subaru dl
## 241 volkswagen dasher
## 242 datsun 810
## 243 bmw 320i
## 244 mazda rx-4
## 245 volkswagen rabbit custom diesel
## 246 ford fiesta
## 247 mazda glc deluxe
## 248 datsun b210 gx
## 249 honda civic cvcc
## 250 oldsmobile cutlass salon brougham
## 251 dodge diplomat
## 252 mercury monarch ghia
## 253 pontiac phoenix lj
## 254 chevrolet malibu
## 255 ford fairmont (auto)
## 256 ford fairmont (man)
## 257 plymouth volare
## 258 amc concord
## 259 buick century special
## 260 mercury zephyr
## 261 dodge aspen
## 262 amc concord d/l
## 263 chevrolet monte carlo landau
## 264 buick regal sport coupe (turbo)
## 265 ford futura
## 266 dodge magnum xe
## 267 chevrolet chevette
## 268 toyota corona
## 269 datsun 510
## 270 dodge omni
## 271 toyota celica gt liftback
## 272 plymouth sapporo
## 273 oldsmobile starfire sx
## 274 datsun 200-sx
## 275 audi 5000
## 276 volvo 264gl
## 277 saab 99gle
## 278 peugeot 604sl
## 279 volkswagen scirocco
## 280 honda accord lx
## 281 pontiac lemans v6
## 282 mercury zephyr 6
## 283 ford fairmont 4
## 284 amc concord dl 6
## 285 dodge aspen 6
## 286 chevrolet caprice classic
## 287 ford ltd landau
## 288 mercury grand marquis
## 289 dodge st. regis
## 290 buick estate wagon (sw)
## 291 ford country squire (sw)
## 292 chevrolet malibu classic (sw)
## 293 chrysler lebaron town @ country (sw)
## 294 vw rabbit custom
## 295 maxda glc deluxe
## 296 dodge colt hatchback custom
## 297 amc spirit dl
## 298 mercedes benz 300d
## 299 cadillac eldorado
## 300 peugeot 504
## 301 oldsmobile cutlass salon brougham
## 302 plymouth horizon
## 303 plymouth horizon tc3
## 304 datsun 210
## 305 fiat strada custom
## 306 buick skylark limited
## 307 chevrolet citation
## 308 oldsmobile omega brougham
## 309 pontiac phoenix
## 310 vw rabbit
## 311 toyota corolla tercel
## 312 chevrolet chevette
## 313 datsun 310
## 314 chevrolet citation
## 315 ford fairmont
## 316 amc concord
## 317 dodge aspen
## 318 audi 4000
## 319 toyota corona liftback
## 320 mazda 626
## 321 datsun 510 hatchback
## 322 toyota corolla
## 323 mazda glc
## 324 dodge colt
## 325 datsun 210
## 326 vw rabbit c (diesel)
## 327 vw dasher (diesel)
## 328 audi 5000s (diesel)
## 329 mercedes-benz 240d
## 330 honda civic 1500 gl
## 332 subaru dl
## 333 vokswagen rabbit
## 334 datsun 280-zx
## 335 mazda rx-7 gs
## 336 triumph tr7 coupe
## 338 honda accord
## 339 plymouth reliant
## 340 buick skylark
## 341 dodge aries wagon (sw)
## 342 chevrolet citation
## 343 plymouth reliant
## 344 toyota starlet
## 345 plymouth champ
## 346 honda civic 1300
## 347 subaru
## 348 datsun 210 mpg
## 349 toyota tercel
## 350 mazda glc 4
## 351 plymouth horizon 4
## 352 ford escort 4w
## 353 ford escort 2h
## 354 volkswagen jetta
## 356 honda prelude
## 357 toyota corolla
## 358 datsun 200sx
## 359 mazda 626
## 360 peugeot 505s turbo diesel
## 361 volvo diesel
## 362 toyota cressida
## 363 datsun 810 maxima
## 364 buick century
## 365 oldsmobile cutlass ls
## 366 ford granada gl
## 367 chrysler lebaron salon
## 368 chevrolet cavalier
## 369 chevrolet cavalier wagon
## 370 chevrolet cavalier 2-door
## 371 pontiac j2000 se hatchback
## 372 dodge aries se
## 373 pontiac phoenix
## 374 ford fairmont futura
## 375 volkswagen rabbit l
## 376 mazda glc custom l
## 377 mazda glc custom
## 378 plymouth horizon miser
## 379 mercury lynx l
## 380 nissan stanza xe
## 381 honda accord
## 382 toyota corolla
## 383 honda civic
## 384 honda civic (auto)
## 385 datsun 310 gx
## 386 buick century limited
## 387 oldsmobile cutlass ciera (diesel)
## 388 chrysler lebaron medallion
## 389 ford granada l
## 390 toyota celica gt
## 391 dodge charger 2.2
## 392 chevrolet camaro
## 393 ford mustang gl
## 394 vw pickup
## 395 dodge rampage
## 396 ford ranger
## 397 chevy s-10range(NewAuto$mpg)## [1] 11.0 46.6range(NewAuto$cylinders)## [1] 3 8range(NewAuto$displacement)## [1] 68 455range(NewAuto$horsepower)## [1] 46 230range(NewAuto$acceleration)## [1] 8.5 24.8range(NewAuto$year)## [1] 70 82range(NewAuto$weight)## [1] 1649 4997mean(NewAuto$mpg)## [1] 24.40443mean(NewAuto$cylinders)## [1] 5.373418mean(NewAuto$displacement)## [1] 187.2405mean(NewAuto$horsepower)## [1] 100.7215mean(NewAuto$acceleration)## [1] 15.7269mean(NewAuto$year)## [1] 77.14557mean(NewAuto$weight)## [1] 2935.972sd(NewAuto$mpg)## [1] 7.867283sd(NewAuto$cylinders)## [1] 1.654179sd(NewAuto$displacement)## [1] 99.67837sd(NewAuto$horsepower)## [1] 35.70885sd(NewAuto$acceleration)## [1] 2.693721sd(NewAuto$year)## [1] 3.106217sd(NewAuto$weight)## [1] 811.3002Ranges: weight 1649 - 4997 mpg 11.0 - 46.6 cylinders 3 - 8 displacement 68 - 455 horsepower 46 - 230 weight 1613 - 5140 acceleration 8.5 - 24.8 year 70 - 82
Means: mpg 24.40443 cylinder 5.373418 displacement 187.2405 horsepower 100.7215 weight 2935.972 acceleration 15.7269 year 77.14557
Standard Deviations: mpg 7.867283 cylinders 1.654179 displacement 99.67837 horsepower 35.7088 weight 811.3002 acceleration 2.693721 year 3.106217
Part E:
pairs(Auto)
Part F:
plot(Auto$weight, Auto$mpg, col="red")
plot(Auto$horsepower, Auto$mpg, col="red")
plot(Auto$cylinders, Auto$mpg, col="red")
plot(Auto$acceleration, Auto$mpg, col="red")
plot(Auto$year, Auto$mpg, col="red")
Scatterplots allow a quick visual guide to analyze whether there is a correlation between variables. The scatterplots analyzed I found that there was a correlation between mgp/cyliders and mpg/horsepower. To predict mpg it would be best to use cylinders or horsepower variables.
Problem 2:
# Box office Star Wars (in millions!)
new_hope <- c(460.998, 314.4)
empire_strikes <- c(290.475, 247.900)
return_jedi <- c(309.306, 165.8)
# Vectors region and titles, used for naming
region <- c("US", "non-US")
titles <- c("A New Hope", "The Empire Strikes Back", "Return of the Jedi")Part A
starWars <- matrix(c(new_hope, empire_strikes, return_jedi), nrow = 3, byrow = TRUE)
rownames(starWars)<-c("New_hope", "Empire_strikes_back", "Return_of_the_Jedi")
starWars## [,1] [,2]
## New_hope 460.998 314.4
## Empire_strikes_back 290.475 247.9
## Return_of_the_Jedi 309.306 165.8Part B
movies <- c(new_hope, empire_strikes, return_jedi)
starWars <- matrix(movies, nrow = 3, byrow = TRUE)
rownames(starWars)<-c("New_hope", "Empire_strikes_back", "Return_of_the_Jedi")
colnames(starWars)<- c("US", "non-US")
starWars## US non-US
## New_hope 460.998 314.4
## Empire_strikes_back 290.475 247.9
## Return_of_the_Jedi 309.306 165.8Part C:
movie_sums <- matrix(data=rowSums(starWars),
nrow = 3,
ncol = 1)
rownames(movie_sums) <- titles
colnames(movie_sums) <- c("Worldwide")
movie_sums## Worldwide
## A New Hope 775.398
## The Empire Strikes Back 538.375
## Return of the Jedi 475.106Part D:
StarWarsWS <- cbind(starWars , movie_sums)
StarWarsWS## US non-US Worldwide
## New_hope 460.998 314.4 775.398
## Empire_strikes_back 290.475 247.9 538.375
## Return_of_the_Jedi 309.306 165.8 475.106PhantomMenace <- c(474.5 , 552.5)
AttackClones <- c(310.7 , 338.7)
RevengeSith <- c(380.3 , 468.5)
Titles <- c("The Phantom Menace" , "Attack of the Clones" , "Revenge of the Sith")Part E:
StarWars2 = matrix(data=c(PhantomMenace,AttackClones,RevengeSith) , nrow = 3, ncol = 2)
rownames(StarWars2) <- Titles
colnames(StarWars2) <- region
movie_sums2 <- matrix(data = rowSums(StarWars2),
nrow = 3,
ncol = 1)
rownames(movie_sums2) <- Titles
colnames(movie_sums2) <- c("Worldwide")
starWars2WS <- cbind(StarWars2 , movie_sums2)
starWars2WS## US non-US Worldwide
## The Phantom Menace 474.5 338.7 813.2
## Attack of the Clones 552.5 380.3 932.8
## Revenge of the Sith 310.7 468.5 779.2Part F:
allStarWars <- rbind(StarWarsWS , starWars2WS)
allStarWars## US non-US Worldwide
## New_hope 460.998 314.4 775.398
## Empire_strikes_back 290.475 247.9 538.375
## Return_of_the_Jedi 309.306 165.8 475.106
## The Phantom Menace 474.500 338.7 813.200
## Attack of the Clones 552.500 380.3 932.800
## Revenge of the Sith 310.700 468.5 779.200Part G:
colSums(allStarWars)## US non-US Worldwide
## 2398.479 1915.600 4314.079Problem 3:
Part A:
college <- read.csv("~/Desktop/MATH239/College.csv", header=FALSE)
#View(college)Part B:
rownames(college) <- college[,1]
#View(college)college <- college[,-1]
#View(college)Part C:
a:
summary(college)## V2 V3 V4 V5 V6
## No :212 1006 : 3 452 : 4 177 : 5 20 : 37
## Private: 1 440 : 3 340 : 3 295 : 5 10 : 35
## Yes :565 663 : 3 384 : 3 172 : 4 12 : 32
## 1011 : 2 405 : 3 176 : 4 16 : 31
## 1109 : 2 494 : 3 227 : 4 15 : 28
## 1154 : 2 501 : 3 279 : 4 25 : 28
## (Other):763 (Other):759 (Other):752 (Other):587
## V7 V8 V9 V10 V11
## 55 : 20 1115 : 3 30 : 7 6550 : 13 4100 : 9
## 60 : 20 1306 : 3 166 : 6 10500 : 5 3600 : 7
## 36 : 19 1345 : 3 35 : 5 8400 : 5 3700 : 7
## 50 : 19 1707 : 3 1 : 4 8840 : 5 3400 : 6
## 52 : 18 500 : 3 27 : 4 10800 : 4 3750 : 6
## 40 : 17 662 : 3 28 : 4 11200 : 4 4200 : 6
## (Other):665 (Other):760 (Other):748 (Other):742 (Other):737
## V12 V13 V14 V15 V16
## 500 :178 1000 : 45 77 : 26 96 : 30 12.1 : 15
## 600 :128 1200 : 33 73 : 24 92 : 29 11.3 : 14
## 400 : 76 1500 : 32 90 : 23 90 : 28 11.1 : 13
## 450 : 60 800 : 32 71 : 21 89 : 26 12.5 : 13
## 550 : 41 900 : 27 75 : 21 95 : 26 13.3 : 13
## 700 : 28 500 : 26 81 : 21 75 : 25 11.5 : 12
## (Other):267 (Other):583 (Other):642 (Other):614 (Other):698
## V17 V18 V19
## 10 : 32 10872 : 2 72 : 24
## 16 : 31 10912 : 2 67 : 23
## 17 : 27 10922 : 2 58 : 22
## 26 : 27 4900 : 2 63 : 22
## 24 : 26 5935 : 2 65 : 21
## 13 : 25 6333 : 2 52 : 20
## (Other):610 (Other):766 (Other):646b:
pairs(college[,1:10])
c:
plot(college$V2 , college$V10)
d:
Elite <- rep("No" , nrow(college))
Elite[college$Top10perc > 50] = "Yes"
Elite <- as.factor(Elite)
college <- data.frame(college, Elite)
summary(college)## V2 V3 V4 V5 V6
## No :212 1006 : 3 452 : 4 177 : 5 20 : 37
## Private: 1 440 : 3 340 : 3 295 : 5 10 : 35
## Yes :565 663 : 3 384 : 3 172 : 4 12 : 32
## 1011 : 2 405 : 3 176 : 4 16 : 31
## 1109 : 2 494 : 3 227 : 4 15 : 28
## 1154 : 2 501 : 3 279 : 4 25 : 28
## (Other):763 (Other):759 (Other):752 (Other):587
## V7 V8 V9 V10 V11
## 55 : 20 1115 : 3 30 : 7 6550 : 13 4100 : 9
## 60 : 20 1306 : 3 166 : 6 10500 : 5 3600 : 7
## 36 : 19 1345 : 3 35 : 5 8400 : 5 3700 : 7
## 50 : 19 1707 : 3 1 : 4 8840 : 5 3400 : 6
## 52 : 18 500 : 3 27 : 4 10800 : 4 3750 : 6
## 40 : 17 662 : 3 28 : 4 11200 : 4 4200 : 6
## (Other):665 (Other):760 (Other):748 (Other):742 (Other):737
## V12 V13 V14 V15 V16
## 500 :178 1000 : 45 77 : 26 96 : 30 12.1 : 15
## 600 :128 1200 : 33 73 : 24 92 : 29 11.3 : 14
## 400 : 76 1500 : 32 90 : 23 90 : 28 11.1 : 13
## 450 : 60 800 : 32 71 : 21 89 : 26 12.5 : 13
## 550 : 41 900 : 27 75 : 21 95 : 26 13.3 : 13
## 700 : 28 500 : 26 81 : 21 75 : 25 11.5 : 12
## (Other):267 (Other):583 (Other):642 (Other):614 (Other):698
## V17 V18 V19 Elite
## 10 : 32 10872 : 2 72 : 24 No:778
## 16 : 31 10912 : 2 67 : 23
## 17 : 27 10922 : 2 58 : 22
## 26 : 27 4900 : 2 63 : 22
## 24 : 26 5935 : 2 65 : 21
## 13 : 25 6333 : 2 52 : 20
## (Other):610 (Other):766 (Other):646plot(college$Elite , college$V10)