Econometrics Discussion
Daniel Schick
2023-09-12
library("AER")## Warning: package 'AER' was built under R version 4.2.3## Loading required package: car## Loading required package: carData## Loading required package: lmtest## Warning: package 'lmtest' was built under R version 4.2.3## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.2.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Warning: package 'sandwich' was built under R version 4.2.3## Loading required package: survivallibrary(ggplot2)Part 1
First Data Set
data("HousePrices")
library(datasets)
HousePrices## price lotsize bedrooms bathrooms stories driveway recreation fullbase
## 1 42000 5850 3 1 2 yes no yes
## 2 38500 4000 2 1 1 yes no no
## 3 49500 3060 3 1 1 yes no no
## 4 60500 6650 3 1 2 yes yes no
## 5 61000 6360 2 1 1 yes no no
## 6 66000 4160 3 1 1 yes yes yes
## 7 66000 3880 3 2 2 yes no yes
## 8 69000 4160 3 1 3 yes no no
## 9 83800 4800 3 1 1 yes yes yes
## 10 88500 5500 3 2 4 yes yes no
## 11 90000 7200 3 2 1 yes no yes
## 12 30500 3000 2 1 1 no no no
## 13 27000 1700 3 1 2 yes no no
## 14 36000 2880 3 1 1 no no no
## 15 37000 3600 2 1 1 yes no no
## 16 37900 3185 2 1 1 yes no no
## 17 40500 3300 3 1 2 no no no
## 18 40750 5200 4 1 3 yes no no
## 19 45000 3450 1 1 1 yes no no
## 20 45000 3986 2 2 1 no yes yes
## 21 48500 4785 3 1 2 yes yes yes
## 22 65900 4510 4 2 2 yes no yes
## 23 37900 4000 3 1 2 yes no no
## 24 38000 3934 2 1 1 yes no no
## 25 42000 4960 2 1 1 yes no no
## 26 42300 3000 2 1 2 yes no no
## 27 43500 3800 2 1 1 yes no no
## 28 44000 4960 2 1 1 yes no yes
## 29 44500 3000 3 1 1 no no no
## 30 44900 4500 3 1 2 yes no no
## 31 45000 3500 2 1 1 no no yes
## 32 48000 3500 4 1 2 yes no no
## 33 49000 4000 2 1 1 yes no no
## 34 51500 4500 2 1 1 yes no no
## 35 61000 6360 2 1 2 yes no no
## 36 61000 4500 2 1 1 yes no no
## 37 61700 4032 2 1 1 yes no yes
## 38 67000 5170 3 1 4 yes no no
## 39 82000 5400 4 2 2 yes no no
## 40 54500 3150 2 2 1 no no yes
## 41 66500 3745 3 1 2 yes no yes
## 42 70000 4520 3 1 2 yes no yes
## 43 82000 4640 4 1 2 yes no no
## 44 92000 8580 5 3 2 yes no no
## 45 38000 2000 2 1 2 yes no no
## 46 44000 2160 3 1 2 no no yes
## 47 41000 3040 2 1 1 no no no
## 48 43000 3090 3 1 2 no no no
## 49 48000 4960 4 1 3 no no no
## 50 54800 3350 3 1 2 yes no no
## 51 55000 5300 5 2 2 yes no no
## 52 57000 4100 4 1 1 no no yes
## 53 68000 9166 2 1 1 yes no yes
## 54 95000 4040 3 1 2 yes no yes
## 55 38000 3630 3 3 2 no yes no
## 56 25000 3620 2 1 1 yes no no
## 57 25245 2400 3 1 1 no no no
## 58 56000 7260 3 2 1 yes yes yes
## 59 35500 4400 3 1 2 yes no no
## 60 30000 2400 3 1 2 yes no no
## 61 48000 4120 2 1 2 yes no no
## 62 48000 4750 2 1 1 yes no no
## 63 52000 4280 2 1 1 yes no no
## 64 54000 4820 3 1 2 yes no no
## 65 56000 5500 4 1 2 yes yes yes
## 66 60000 5500 3 1 2 yes no no
## 67 60000 5040 3 1 2 yes no yes
## 68 67000 6000 2 1 1 yes no yes
## 69 47000 2500 2 1 1 no no no
## 70 70000 4095 3 1 2 no yes yes
## 71 45000 4095 2 1 1 yes no no
## 72 51000 3150 3 1 2 yes no yes
## 73 32500 1836 2 1 1 no no yes
## 74 34000 2475 3 1 2 yes no no
## 75 35000 3210 3 1 2 yes no yes
## 76 36000 3180 3 1 1 no no no
## 77 45000 1650 3 1 2 no no yes
## 78 47000 3180 4 1 2 yes no yes
## 79 55000 3180 2 2 1 yes no yes
## 80 63900 6360 2 1 1 yes no yes
## 81 50000 4240 3 1 2 yes no no
## 82 35000 3240 2 1 1 no yes no
## 83 50000 3650 3 1 2 yes no no
## 84 43000 3240 3 1 2 yes no no
## 85 55500 3780 2 1 2 yes yes yes
## 86 57000 6480 3 1 2 no no no
## 87 60000 5850 2 1 1 yes yes yes
## 88 78000 3150 3 2 1 yes yes yes
## 89 35000 3000 2 1 1 yes no no
## 90 44000 3090 2 1 1 yes yes yes
## 91 47000 6060 3 1 1 yes yes yes
## 92 58000 5900 4 2 2 no no yes
## 93 163000 7420 4 1 2 yes yes yes
## 94 128000 8500 3 2 4 yes no no
## 95 123500 8050 3 1 1 yes yes yes
## 96 39000 6800 2 1 1 yes no no
## 97 53900 8250 3 1 1 yes no no
## 98 59900 8250 3 1 1 yes no yes
## 99 35000 3500 2 1 1 yes yes no
## 100 43000 2835 2 1 1 yes no no
## 101 57000 4500 3 2 2 no no yes
## 102 79000 3300 3 3 2 yes no yes
## 103 125000 4320 3 1 2 yes no yes
## 104 132000 3500 4 2 2 yes no no
## 105 58000 4992 3 2 2 yes no no
## 106 43000 4600 2 1 1 yes no no
## 107 48000 3720 2 1 1 no no no
## 108 58500 3680 3 2 2 yes no no
## 109 73000 3000 3 2 2 yes yes yes
## 110 63500 3750 2 1 1 yes yes yes
## 111 43000 5076 3 1 1 no no no
## 112 46500 4500 2 1 1 no no no
## 113 92000 5000 3 1 2 yes no no
## 114 75000 4260 4 1 2 yes no yes
## 115 75000 6540 4 2 2 no no no
## 116 85000 3700 4 1 2 yes yes no
## 117 93000 3760 3 1 2 yes no no
## 118 94500 4000 3 2 2 yes no yes
## 119 106500 4300 3 2 2 yes no yes
## 120 116000 6840 5 1 2 yes yes yes
## 121 61500 4400 2 1 1 yes no no
## 122 80000 10500 4 2 2 yes no no
## 123 37000 4400 2 1 1 yes no no
## 124 59500 4840 3 1 2 yes no no
## 125 70000 4120 2 1 1 yes no yes
## 126 95000 4260 4 2 2 yes no no
## 127 117000 5960 3 3 2 yes yes yes
## 128 122500 8800 3 2 2 yes no no
## 129 123500 4560 3 2 2 yes yes yes
## 130 127000 4600 3 2 2 yes yes no
## 131 35000 4840 2 1 2 yes no no
## 132 44500 3850 3 1 2 yes no no
## 133 49900 4900 3 1 2 no no no
## 134 50500 3850 3 1 1 yes no no
## 135 65000 3760 3 1 1 yes no no
## 136 90000 6000 4 2 4 yes no no
## 137 46000 4370 3 1 2 yes no no
## 138 35000 7700 2 1 1 yes no no
## 139 26500 2990 2 1 1 no no no
## 140 43000 3750 3 1 2 yes no no
## 141 56000 3000 3 1 2 yes no no
## 142 40000 2650 3 1 2 yes no yes
## 143 51000 4500 4 2 2 yes no yes
## 144 51000 4500 2 1 1 no no no
## 145 57250 4500 3 1 2 no no yes
## 146 44000 4500 2 1 2 yes no no
## 147 61000 2175 3 1 2 no yes yes
## 148 62000 4500 3 2 3 yes no no
## 149 80000 4800 5 2 3 no no yes
## 150 50000 4600 4 1 2 yes no no
## 151 59900 3450 3 1 2 yes no no
## 152 35500 3000 3 1 2 no no no
## 153 37000 3600 2 2 2 yes no yes
## 154 42000 3600 3 1 2 no no no
## 155 48000 3750 3 1 1 yes no no
## 156 60000 2610 4 3 2 no no no
## 157 60000 2953 3 1 2 yes no yes
## 158 60000 2747 4 2 2 no no no
## 159 62000 1905 5 1 2 no no yes
## 160 63000 3968 3 1 2 no no no
## 161 63900 3162 3 1 2 yes no no
## 162 130000 6000 4 1 2 yes no yes
## 163 25000 2910 3 1 1 no no no
## 164 50000 2135 3 2 2 no no no
## 165 52900 3120 3 1 2 no no yes
## 166 62000 4075 3 1 1 yes yes yes
## 167 73500 3410 3 1 2 no no no
## 168 38000 2800 3 1 1 yes no no
## 169 46000 2684 2 1 1 yes no no
## 170 48000 3100 3 1 2 no no yes
## 171 52500 3630 2 1 1 yes no yes
## 172 32000 1950 3 1 1 no no no
## 173 38000 2430 3 1 1 no no no
## 174 46000 4320 3 1 1 no no no
## 175 50000 3036 3 1 2 yes no yes
## 176 57500 3630 3 2 2 yes no no
## 177 70000 5400 4 1 2 yes no no
## 178 69900 3420 4 2 2 yes no yes
## 179 74500 3180 3 2 2 yes no no
## 180 42000 3660 4 1 2 no no no
## 181 60000 4410 2 1 1 no no no
## 182 50000 3990 3 1 2 yes no no
## 183 58000 4340 3 1 1 yes no no
## 184 63900 3510 3 1 2 yes no no
## 185 28000 3420 5 1 2 no no no
## 186 54000 3420 2 1 2 yes no no
## 187 44700 5495 3 1 1 yes no yes
## 188 47000 3480 4 1 2 no no no
## 189 50000 7424 3 1 1 no no no
## 190 57250 3460 4 1 2 yes no no
## 191 67000 3630 3 1 2 yes no no
## 192 52500 3630 2 1 1 yes no no
## 193 42000 3480 3 1 2 no no no
## 194 57500 3460 3 2 1 yes no yes
## 195 33000 3180 2 1 1 yes no no
## 196 34400 3635 2 1 1 no no no
## 197 40000 3960 3 1 1 yes no no
## 198 40500 4350 3 1 2 no no no
## 199 46500 3930 2 1 1 no no no
## 200 52000 3570 3 1 2 yes no yes
## 201 53000 3600 3 1 1 yes no no
## 202 53900 2520 5 2 1 no no yes
## 203 50000 3480 3 1 1 no no no
## 204 55500 3180 4 2 2 yes no no
## 205 56000 3290 2 1 1 yes no no
## 206 60000 4000 4 2 2 no no no
## 207 60000 2325 3 1 2 no no no
## 208 69500 4350 2 1 1 yes no yes
## 209 72000 3540 2 1 1 no yes yes
## 210 92500 3960 3 1 1 yes no yes
## 211 40500 2640 2 1 1 no no no
## 212 42000 2700 2 1 1 no no no
## 213 47900 2700 3 1 1 no no no
## 214 52000 3180 3 1 2 no no yes
## 215 62000 3500 4 1 2 yes no no
## 216 41000 3630 2 1 1 yes no no
## 217 138300 6000 4 3 2 yes yes yes
## 218 42000 3150 3 1 2 no no no
## 219 47000 3792 4 1 2 yes no no
## 220 64500 3510 3 1 3 yes no no
## 221 46000 3120 3 1 2 no no no
## 222 58000 3000 4 1 3 yes no yes
## 223 70100 4200 3 1 2 yes no no
## 224 78500 2817 4 2 2 no yes yes
## 225 87250 3240 4 1 3 yes no no
## 226 70800 2800 3 2 2 no no yes
## 227 56000 3816 2 1 1 yes no yes
## 228 48000 3185 2 1 1 yes no yes
## 229 68000 6321 3 1 2 yes no yes
## 230 79000 3650 3 2 2 yes no no
## 231 80000 4700 4 1 2 yes yes yes
## 232 87000 6615 4 2 2 yes yes no
## 233 25000 3850 3 1 2 yes no no
## 234 32500 3970 1 1 1 no no no
## 235 36000 3000 2 1 2 yes no no
## 236 42500 4352 4 1 2 no no no
## 237 43000 3630 4 1 2 yes no no
## 238 50000 3600 6 1 2 yes no no
## 239 26000 3000 2 1 1 yes no yes
## 240 30000 3000 4 1 2 yes no no
## 241 34000 2787 4 2 2 yes no no
## 242 52000 3000 2 1 2 yes no no
## 243 70000 4770 3 1 1 yes yes yes
## 244 27000 3649 2 1 1 yes no no
## 245 32500 3970 3 1 2 yes no yes
## 246 37200 2910 2 1 1 no no no
## 247 38000 3480 2 1 1 yes no no
## 248 42000 6615 3 1 2 yes no no
## 249 44500 3500 2 1 1 yes no no
## 250 45000 3450 3 1 2 yes no yes
## 251 48500 3450 3 1 1 yes no yes
## 252 52000 3520 2 2 1 yes no yes
## 253 53900 6930 4 1 2 no no no
## 254 60000 4600 3 2 2 yes no no
## 255 61000 4360 4 1 2 yes no no
## 256 64500 3450 3 1 2 yes no yes
## 257 71000 4410 4 3 2 yes no yes
## 258 75500 4600 2 2 1 yes no no
## 259 33500 3640 2 1 1 yes no no
## 260 41000 6000 2 1 1 yes no no
## 261 41000 5400 4 1 2 yes no no
## 262 46200 3640 4 1 2 yes no yes
## 263 48500 3640 2 1 1 yes no no
## 264 48900 4040 2 1 1 yes no no
## 265 50000 3640 2 1 1 yes no no
## 266 51000 3640 2 1 1 yes no no
## 267 52500 5640 2 1 1 no no no
## 268 52500 3600 2 1 1 yes no no
## 269 54000 3600 2 1 1 yes no no
## 270 59000 4632 4 1 2 yes no no
## 271 60000 3640 3 2 2 yes no yes
## 272 63000 4900 2 1 2 yes no yes
## 273 64000 4510 4 1 2 yes no no
## 274 64900 4100 2 2 1 yes yes yes
## 275 65000 3640 3 1 2 yes no no
## 276 66000 5680 3 1 2 yes yes no
## 277 70000 6300 3 1 1 yes no no
## 278 65500 4000 3 1 2 yes no no
## 279 57000 3960 3 1 2 yes no no
## 280 52000 5960 3 1 2 yes yes yes
## 281 54000 5830 2 1 1 yes no no
## 282 74500 4500 4 2 1 no no yes
## 283 90000 4100 3 2 3 yes no no
## 284 45000 6750 2 1 1 yes no no
## 285 45000 9000 3 1 2 yes no no
## 286 65000 2550 3 1 2 yes no yes
## 287 55000 7152 3 1 2 yes no no
## 288 62000 6450 4 1 2 yes no no
## 289 30000 3360 2 1 1 yes no no
## 290 34000 3264 2 1 1 yes no no
## 291 38000 4000 3 1 1 yes no no
## 292 39000 4000 3 1 2 yes no no
## 293 45000 3069 2 1 1 yes no no
## 294 47000 4040 2 1 1 yes no no
## 295 47500 4040 2 1 1 yes no no
## 296 49000 3185 2 1 1 yes no no
## 297 50000 5900 2 1 1 yes no no
## 298 50000 3120 3 1 2 yes no no
## 299 52900 5450 2 1 1 yes no no
## 300 53000 4040 2 1 1 yes no no
## 301 55000 4080 2 1 1 yes no no
## 302 56000 8080 3 1 1 yes no no
## 303 58500 4040 2 1 2 yes no no
## 304 59500 4080 3 1 2 yes no no
## 305 60000 5800 3 1 1 yes no no
## 306 64000 5885 2 1 1 yes no no
## 307 67000 9667 4 2 2 yes yes yes
## 308 68100 3420 4 2 2 yes no no
## 309 70000 5800 2 1 1 yes yes yes
## 310 72000 7600 4 1 2 yes no no
## 311 57500 5400 3 1 1 yes no no
## 312 69900 4995 4 2 1 yes no yes
## 313 70000 3000 3 1 2 yes no yes
## 314 75000 5500 3 2 1 yes no yes
## 315 76900 6450 3 2 1 yes yes yes
## 316 78000 6210 4 1 4 yes yes no
## 317 80000 5000 3 1 4 yes no no
## 318 82000 5000 3 1 3 yes no no
## 319 83000 5828 4 1 4 yes yes no
## 320 83000 5200 3 1 3 yes no no
## 321 83900 5500 3 1 3 yes yes no
## 322 88500 6350 3 2 3 yes yes no
## 323 93000 8250 3 2 3 yes no no
## 324 98000 6000 3 1 1 yes no no
## 325 98500 7700 3 2 1 yes no no
## 326 99000 8880 3 2 2 yes no yes
## 327 101000 8880 2 1 1 yes no no
## 328 110000 6480 3 2 4 yes no no
## 329 115442 7000 3 2 4 yes no no
## 330 120000 8875 3 1 1 yes no no
## 331 124000 7155 3 2 1 yes yes yes
## 332 175000 8960 4 4 4 yes no no
## 333 50000 7350 2 1 1 yes no no
## 334 55000 3850 2 1 1 yes no no
## 335 60000 7000 3 1 1 yes no no
## 336 61000 7770 2 1 1 yes no no
## 337 106000 7440 3 2 1 yes yes yes
## 338 155000 7500 3 3 1 yes no yes
## 339 141000 8100 4 1 2 yes yes yes
## 340 62500 3900 3 1 2 yes no no
## 341 70000 2970 3 1 3 yes no no
## 342 73000 3000 3 1 2 yes no yes
## 343 80000 10500 2 1 1 yes no no
## 344 80000 5500 3 2 2 yes no no
## 345 88000 4500 3 1 4 yes no no
## 346 49000 3850 3 1 1 yes no no
## 347 52000 4130 3 2 2 yes no no
## 348 59500 4046 3 1 2 yes no yes
## 349 60000 4079 3 1 3 yes no no
## 350 64000 4000 3 1 2 yes no no
## 351 64500 9860 3 1 1 yes no no
## 352 68500 7000 3 1 2 yes no yes
## 353 78500 7980 3 1 1 yes no no
## 354 86000 6800 2 1 1 yes yes yes
## 355 86900 4300 6 2 2 yes no no
## 356 75000 10269 3 1 1 yes no no
## 357 78000 6100 3 1 3 yes yes no
## 358 95000 6420 3 2 3 yes no no
## 359 97000 12090 4 2 2 yes no no
## 360 107000 6600 3 1 4 yes no no
## 361 130000 6600 4 2 2 yes yes yes
## 362 145000 8580 4 3 4 yes no no
## 363 175000 9960 3 2 2 yes no yes
## 364 72000 10700 3 1 2 yes yes yes
## 365 84900 15600 3 1 1 yes no no
## 366 99000 13200 2 1 1 yes no yes
## 367 114000 9000 4 2 4 yes no no
## 368 120000 7950 5 2 2 yes no yes
## 369 145000 16200 5 3 2 yes no no
## 370 79000 6100 3 2 1 yes no yes
## 371 82000 6360 3 1 1 yes yes yes
## 372 85000 6420 3 1 1 yes no yes
## 373 100500 6360 4 2 3 yes no no
## 374 122000 6540 4 2 2 yes yes yes
## 375 126500 6420 3 2 2 yes no no
## 376 133000 6550 4 2 2 yes no no
## 377 140000 5750 3 2 4 yes yes no
## 378 190000 7420 4 2 3 yes no no
## 379 84000 7160 3 1 1 yes no yes
## 380 97000 4000 3 2 2 yes no yes
## 381 103500 9000 4 2 4 yes yes no
## 382 112500 6550 3 1 2 yes no yes
## 383 140000 13200 3 1 2 yes no yes
## 384 74700 7085 3 1 1 yes yes yes
## 385 78000 6600 4 2 2 yes yes yes
## 386 78900 6900 3 1 1 yes yes yes
## 387 83900 11460 3 1 3 yes no no
## 388 85000 7020 3 1 1 yes no yes
## 389 85000 6540 3 1 1 yes yes yes
## 390 86000 8000 3 1 1 yes yes yes
## 391 86900 9620 3 1 1 yes no yes
## 392 94500 10500 3 2 1 yes no yes
## 393 96000 5020 3 1 4 yes no no
## 394 106000 7440 3 2 4 yes no no
## 395 72000 6600 3 1 1 yes yes yes
## 396 74500 7200 3 1 2 yes yes yes
## 397 77000 6710 3 2 2 yes yes yes
## 398 80750 6660 4 2 2 yes yes yes
## 399 82900 7000 3 1 1 yes no yes
## 400 85000 7231 3 1 2 yes yes yes
## 401 92500 7410 3 1 1 yes yes yes
## 402 76000 7800 3 1 1 yes no yes
## 403 77500 6825 3 1 1 yes yes yes
## 404 80000 6360 3 1 3 yes no no
## 405 80000 6600 4 2 1 yes no yes
## 406 86000 6900 3 2 1 yes yes yes
## 407 87000 6600 3 1 1 yes yes yes
## 408 87500 6420 3 1 3 yes no yes
## 409 89000 6600 3 2 1 yes no yes
## 410 89900 6600 3 2 3 yes no no
## 411 90000 9000 3 1 1 yes no yes
## 412 95000 6500 3 2 3 yes no no
## 413 112000 6360 3 2 4 yes no no
## 414 31900 5300 3 1 1 no no no
## 415 52000 2850 3 2 2 no no yes
## 416 90000 6400 3 1 1 yes yes yes
## 417 100000 11175 3 1 1 yes no yes
## 418 91700 6750 2 1 1 yes yes yes
## 419 174500 7500 4 2 2 yes no yes
## 420 94700 6000 3 1 2 yes no no
## 421 68000 10240 2 1 1 yes no no
## 422 80000 5136 3 1 2 yes yes yes
## 423 61100 3400 3 1 2 yes no yes
## 424 62900 2880 3 1 2 yes no no
## 425 65500 3840 3 1 2 yes no no
## 426 66000 2870 2 1 2 yes yes yes
## 427 49500 5320 2 1 1 yes no no
## 428 50000 3512 2 1 1 yes no no
## 429 53500 3480 2 1 1 yes no no
## 430 58550 3600 3 1 1 yes no yes
## 431 64500 3520 2 1 2 yes no no
## 432 65000 5320 3 1 2 yes yes yes
## 433 69000 6040 3 1 1 yes no no
## 434 73000 11410 2 1 2 yes no no
## 435 75000 8400 3 1 2 yes yes yes
## 436 75000 5300 4 2 1 yes no no
## 437 132000 7800 3 2 2 yes no no
## 438 60000 3520 3 1 2 yes no no
## 439 65000 5360 3 1 2 yes no no
## 440 69000 6862 3 1 2 yes no no
## 441 51900 3520 3 1 1 yes no no
## 442 57000 4050 2 1 2 yes yes yes
## 443 65000 3520 3 1 1 yes no no
## 444 79500 4400 4 1 2 yes no no
## 445 72500 5720 2 1 2 yes no no
## 446 104900 11440 4 1 2 yes no yes
## 447 114900 7482 3 2 3 yes no no
## 448 120000 5500 4 2 2 yes no yes
## 449 58000 4320 3 1 2 yes no no
## 450 67000 5400 2 1 2 yes no no
## 451 67000 4320 3 1 1 yes no no
## 452 69000 4815 2 1 1 yes no no
## 453 73000 6100 3 1 1 yes no yes
## 454 73500 7980 3 1 1 yes no no
## 455 74900 6050 3 1 1 yes no yes
## 456 75000 3800 3 1 2 yes yes yes
## 457 79500 5400 5 1 2 yes yes yes
## 458 120900 6000 3 2 4 yes yes yes
## 459 44555 2398 3 1 1 yes no no
## 460 47000 2145 3 1 2 yes no yes
## 461 47600 2145 3 1 2 yes no yes
## 462 49000 2145 3 1 3 yes no no
## 463 49000 2610 3 1 2 yes no yes
## 464 49000 1950 3 2 2 yes no yes
## 465 49500 2145 3 1 3 yes no no
## 466 52000 2275 3 1 3 yes no no
## 467 54000 2856 3 1 3 yes no no
## 468 55000 2015 3 1 2 yes no yes
## 469 55000 2176 2 1 2 yes yes no
## 470 56000 2145 4 2 1 yes no yes
## 471 60000 2145 3 1 3 yes no no
## 472 60500 2787 3 1 1 yes no yes
## 473 50000 9500 3 1 2 yes no no
## 474 64900 4990 4 2 2 yes yes yes
## 475 93000 6670 3 1 3 yes no yes
## 476 85000 6254 4 2 1 yes no yes
## 477 61500 10360 2 1 1 yes no no
## 478 88500 5500 3 2 1 yes yes yes
## 479 88000 5450 4 2 1 yes no yes
## 480 89000 5500 3 1 3 yes no no
## 481 89500 6000 4 1 3 yes yes yes
## 482 95000 5700 3 1 1 yes yes yes
## 483 95500 6600 2 2 4 yes no yes
## 484 51500 4000 2 1 1 yes no no
## 485 62900 4880 3 1 1 yes no no
## 486 118500 4880 4 2 2 yes no no
## 487 42900 8050 2 1 1 yes no no
## 488 44100 8100 2 1 1 yes no no
## 489 47000 5880 3 1 1 yes no no
## 490 50000 5880 2 1 1 yes no no
## 491 50000 12944 3 1 1 yes no no
## 492 53000 6020 3 1 1 yes no no
## 493 53000 4050 2 1 1 yes no no
## 494 54000 8400 2 1 1 yes no no
## 495 58500 5600 2 1 1 yes no no
## 496 59000 5985 3 1 1 yes no yes
## 497 60000 4500 3 1 1 yes no yes
## 498 62900 4920 3 1 2 yes no no
## 499 64000 8250 3 1 1 yes no no
## 500 65000 8400 4 1 4 yes no no
## 501 67900 6440 2 1 1 yes no no
## 502 68500 8100 4 1 4 yes no yes
## 503 70000 6720 3 1 1 yes no no
## 504 70500 5948 3 1 2 yes no no
## 505 71500 8150 3 2 1 yes yes yes
## 506 71900 4800 2 1 1 yes yes yes
## 507 75000 9800 4 2 2 yes yes no
## 508 75000 8520 3 1 1 yes no no
## 509 87000 8372 3 1 3 yes no no
## 510 64000 4040 3 1 2 yes no no
## 511 70000 4646 3 1 2 yes yes yes
## 512 47500 4775 4 1 2 yes no no
## 513 62600 4950 4 1 2 yes no no
## 514 66000 5010 3 1 2 yes no yes
## 515 58900 6060 2 1 1 yes no yes
## 516 53000 3584 2 1 1 yes no no
## 517 95000 6000 3 2 3 yes yes no
## 518 96500 6000 4 2 4 yes no no
## 519 101000 6240 4 2 2 yes no no
## 520 102000 6000 3 2 2 yes yes no
## 521 103000 7680 4 2 4 yes yes no
## 522 105000 6000 4 2 4 yes yes no
## 523 108000 6000 4 2 4 yes no no
## 524 110000 6000 4 2 4 yes no no
## 525 113000 6000 4 2 4 yes no no
## 526 120000 7475 3 2 4 yes no no
## 527 105000 5150 3 2 4 yes no no
## 528 106000 6325 3 1 4 yes no no
## 529 107500 6000 3 2 4 yes no no
## 530 108000 6000 3 2 3 yes no no
## 531 113750 6000 3 1 4 yes yes no
## 532 120000 7000 3 1 4 yes no no
## 533 70000 12900 3 1 1 yes no no
## 534 71000 7686 3 1 1 yes yes yes
## 535 82000 5000 3 1 3 yes no no
## 536 82000 5800 3 2 4 yes no no
## 537 82500 6000 3 2 4 yes no no
## 538 83000 4800 3 1 3 yes no no
## 539 84000 6500 3 2 3 yes no no
## 540 85000 7320 4 2 2 yes no no
## 541 85000 6525 3 2 4 yes no no
## 542 91500 4800 3 2 4 yes yes no
## 543 94000 6000 3 2 4 yes no no
## 544 103000 6000 3 2 4 yes yes no
## 545 105000 6000 3 2 2 yes yes no
## 546 105000 6000 3 1 2 yes no no
## gasheat aircon garage prefer
## 1 no no 1 no
## 2 no no 0 no
## 3 no no 0 no
## 4 no no 0 no
## 5 no no 0 no
## 6 no yes 0 no
## 7 no no 2 no
## 8 no no 0 no
## 9 no no 0 no
## 10 no yes 1 no
## 11 no yes 3 no
## 12 no no 0 no
## 13 no no 0 no
## 14 no no 0 no
## 15 no no 0 no
## 16 no yes 0 no
## 17 no no 1 no
## 18 no no 0 no
## 19 no no 0 no
## 20 no no 1 no
## 21 no yes 1 no
## 22 no no 0 no
## 23 no yes 0 no
## 24 no no 0 no
## 25 no no 0 no
## 26 no no 0 no
## 27 no no 0 no
## 28 no yes 0 no
## 29 no yes 0 no
## 30 no yes 0 no
## 31 no no 0 no
## 32 no yes 2 no
## 33 no no 0 no
## 34 no no 0 no
## 35 no no 0 no
## 36 no yes 2 no
## 37 no no 0 no
## 38 no yes 0 no
## 39 no yes 2 no
## 40 no no 0 no
## 41 no no 0 no
## 42 no yes 0 no
## 43 no no 1 no
## 44 no no 2 no
## 45 no no 0 no
## 46 no no 0 no
## 47 no no 0 no
## 48 no no 0 no
## 49 no no 0 no
## 50 no no 0 no
## 51 no no 0 no
## 52 no no 0 no
## 53 no yes 2 no
## 54 yes no 1 no
## 55 no no 0 no
## 56 no no 0 no
## 57 no no 0 no
## 58 no no 3 no
## 59 no no 0 no
## 60 no no 0 no
## 61 no no 0 no
## 62 no no 0 no
## 63 no yes 2 no
## 64 no no 0 no
## 65 no no 0 no
## 66 no yes 0 no
## 67 no yes 0 no
## 68 no yes 1 no
## 69 no yes 0 no
## 70 no yes 0 no
## 71 no no 2 no
## 72 no no 0 no
## 73 no no 0 no
## 74 no no 0 no
## 75 no no 0 no
## 76 no no 0 no
## 77 no no 0 no
## 78 no yes 0 no
## 79 no no 2 no
## 80 no yes 1 no
## 81 no yes 0 no
## 82 no no 1 no
## 83 no no 0 no
## 84 no no 2 no
## 85 no no 0 no
## 86 no yes 1 no
## 87 no no 2 no
## 88 no yes 0 no
## 89 no no 1 no
## 90 no no 0 no
## 91 no no 0 no
## 92 no no 1 no
## 93 no yes 2 no
## 94 no yes 2 no
## 95 no yes 1 no
## 96 no no 0 no
## 97 no no 2 no
## 98 no no 3 no
## 99 no no 0 no
## 100 no no 0 no
## 101 no yes 0 no
## 102 no no 0 no
## 103 yes no 2 no
## 104 yes no 2 no
## 105 no no 2 no
## 106 no no 0 no
## 107 no yes 0 no
## 108 no no 0 no
## 109 no no 0 no
## 110 no no 0 no
## 111 no no 0 no
## 112 no no 0 no
## 113 no yes 0 no
## 114 no yes 0 no
## 115 no yes 0 no
## 116 no yes 0 no
## 117 yes no 2 no
## 118 no yes 1 no
## 119 no no 1 no
## 120 no yes 1 no
## 121 no no 1 no
## 122 no no 1 no
## 123 no no 0 no
## 124 no no 1 no
## 125 no no 1 no
## 126 yes no 0 no
## 127 no no 1 no
## 128 no yes 2 no
## 129 no yes 1 no
## 130 no yes 2 no
## 131 no no 0 no
## 132 no no 0 no
## 133 no no 0 no
## 134 no no 2 no
## 135 no no 2 no
## 136 no no 1 no
## 137 no no 0 no
## 138 no no 0 no
## 139 no no 1 no
## 140 no no 0 no
## 141 no no 0 no
## 142 no no 1 no
## 143 no no 2 no
## 144 no no 0 no
## 145 no yes 0 no
## 146 yes no 1 no
## 147 no yes 0 no
## 148 yes no 1 no
## 149 yes no 0 no
## 150 no no 0 no
## 151 no no 1 no
## 152 no no 0 no
## 153 no no 1 no
## 154 no no 1 no
## 155 no no 0 no
## 156 no no 0 no
## 157 no yes 0 no
## 158 no no 0 no
## 159 no no 0 no
## 160 no no 0 no
## 161 no yes 1 no
## 162 no no 2 no
## 163 no no 0 no
## 164 no no 0 no
## 165 yes no 0 no
## 166 no no 2 no
## 167 no yes 0 no
## 168 no no 0 no
## 169 no yes 1 no
## 170 no no 0 no
## 171 no no 0 no
## 172 yes no 0 no
## 173 no no 0 no
## 174 no no 1 no
## 175 no no 0 no
## 176 yes no 2 no
## 177 no no 0 no
## 178 no yes 2 no
## 179 no no 2 no
## 180 no no 0 no
## 181 no no 1 no
## 182 no no 0 no
## 183 no no 0 no
## 184 no no 0 no
## 185 no no 0 no
## 186 yes no 1 no
## 187 no no 0 no
## 188 no no 1 no
## 189 no no 0 no
## 190 no yes 0 no
## 191 no no 2 no
## 192 no yes 0 no
## 193 no no 1 no
## 194 no yes 1 no
## 195 no no 0 no
## 196 no no 0 no
## 197 no no 0 no
## 198 yes no 1 no
## 199 no no 0 no
## 200 no no 0 no
## 201 no no 1 no
## 202 no yes 1 no
## 203 no yes 0 no
## 204 no no 0 no
## 205 yes no 1 no
## 206 no no 0 no
## 207 no no 0 no
## 208 no no 0 no
## 209 no no 0 no
## 210 no no 2 no
## 211 no no 1 no
## 212 no no 0 no
## 213 no no 0 no
## 214 no no 0 no
## 215 no no 2 no
## 216 no no 0 no
## 217 yes no 2 no
## 218 no no 0 no
## 219 no no 0 no
## 220 no no 0 no
## 221 no no 0 no
## 222 no yes 2 no
## 223 no no 1 no
## 224 no no 1 no
## 225 no no 1 no
## 226 no yes 1 no
## 227 no yes 2 no
## 228 no no 2 no
## 229 no yes 1 no
## 230 no no 2 no
## 231 no yes 1 no
## 232 yes no 1 no
## 233 no no 0 no
## 234 no no 0 no
## 235 no no 0 no
## 236 no no 1 no
## 237 no no 3 no
## 238 no no 1 no
## 239 no no 2 no
## 240 no no 0 no
## 241 no no 0 no
## 242 no yes 0 no
## 243 no no 0 no
## 244 no no 0 no
## 245 no no 0 no
## 246 no no 0 no
## 247 no no 1 no
## 248 no no 0 no
## 249 no no 0 no
## 250 no no 0 no
## 251 no no 2 no
## 252 no no 0 no
## 253 no no 1 no
## 254 no yes 1 no
## 255 no no 0 no
## 256 no no 1 no
## 257 no no 2 no
## 258 no yes 2 no
## 259 no no 0 no
## 260 no no 0 no
## 261 no no 0 no
## 262 no no 0 no
## 263 no no 0 no
## 264 no no 0 no
## 265 no no 1 no
## 266 no no 0 no
## 267 no no 0 no
## 268 no no 0 no
## 269 no no 0 no
## 270 no yes 0 no
## 271 no no 0 no
## 272 no no 0 no
## 273 no yes 2 no
## 274 no no 0 no
## 275 no yes 0 no
## 276 no yes 1 no
## 277 no yes 2 no
## 278 no no 1 no
## 279 no no 0 no
## 280 no no 0 no
## 281 no no 2 no
## 282 no yes 2 no
## 283 no yes 2 no
## 284 no no 0 no
## 285 no no 2 no
## 286 no no 0 no
## 287 no yes 0 no
## 288 no no 0 no
## 289 no no 1 no
## 290 no no 0 no
## 291 no no 0 no
## 292 no no 1 no
## 293 no no 1 no
## 294 no no 0 no
## 295 no no 1 no
## 296 no no 2 no
## 297 no no 1 no
## 298 no no 1 no
## 299 no no 0 no
## 300 no no 0 no
## 301 no no 0 no
## 302 no yes 2 no
## 303 no no 1 no
## 304 no no 2 no
## 305 yes no 2 no
## 306 no yes 1 no
## 307 no no 1 no
## 308 no no 0 no
## 309 no yes 0 no
## 310 no yes 2 no
## 311 no no 3 no
## 312 no no 0 no
## 313 no yes 0 no
## 314 no no 0 no
## 315 yes no 0 no
## 316 no yes 0 no
## 317 no no 0 no
## 318 no yes 0 no
## 319 no no 0 no
## 320 no yes 0 no
## 321 no yes 1 no
## 322 no yes 0 no
## 323 no yes 0 no
## 324 no yes 1 no
## 325 no no 2 no
## 326 no yes 1 no
## 327 no yes 1 no
## 328 no yes 2 no
## 329 no yes 2 no
## 330 no no 1 no
## 331 no yes 2 no
## 332 no yes 3 no
## 333 no no 1 no
## 334 no no 0 no
## 335 no no 3 no
## 336 no no 1 no
## 337 no yes 0 yes
## 338 no yes 2 yes
## 339 no yes 2 yes
## 340 no no 0 no
## 341 no no 0 no
## 342 no no 0 no
## 343 no no 1 no
## 344 no no 1 no
## 345 no yes 0 no
## 346 no no 0 no
## 347 no no 2 no
## 348 no no 1 no
## 349 no no 0 no
## 350 no no 2 no
## 351 no no 0 no
## 352 no no 0 no
## 353 no no 2 no
## 354 no no 2 no
## 355 no no 0 no
## 356 no no 1 yes
## 357 no yes 0 yes
## 358 no yes 0 yes
## 359 no no 2 yes
## 360 no yes 3 yes
## 361 no yes 1 yes
## 362 no yes 2 yes
## 363 no no 2 yes
## 364 no no 0 no
## 365 no yes 2 no
## 366 yes no 1 no
## 367 no yes 2 no
## 368 yes no 2 no
## 369 no no 0 no
## 370 no no 2 yes
## 371 no yes 2 yes
## 372 no yes 0 yes
## 373 no yes 2 yes
## 374 no yes 2 yes
## 375 no yes 1 yes
## 376 no yes 1 yes
## 377 no yes 1 yes
## 378 no yes 2 yes
## 379 no no 2 yes
## 380 no yes 0 yes
## 381 no yes 1 yes
## 382 no yes 0 yes
## 383 no yes 2 yes
## 384 no no 2 yes
## 385 no no 0 yes
## 386 no no 0 yes
## 387 no no 2 yes
## 388 no yes 2 yes
## 389 no no 2 yes
## 390 no yes 2 yes
## 391 no no 2 yes
## 392 no yes 1 yes
## 393 no yes 0 yes
## 394 no no 1 yes
## 395 no no 0 yes
## 396 no no 1 yes
## 397 no no 1 yes
## 398 no no 1 yes
## 399 no no 2 yes
## 400 no yes 0 yes
## 401 no yes 2 yes
## 402 no yes 2 yes
## 403 no yes 0 yes
## 404 no no 0 yes
## 405 no no 0 yes
## 406 no no 0 yes
## 407 no no 2 yes
## 408 no no 0 yes
## 409 no yes 0 yes
## 410 no yes 0 yes
## 411 no no 1 yes
## 412 no yes 0 yes
## 413 no yes 0 yes
## 414 no yes 0 yes
## 415 no no 0 yes
## 416 no yes 1 yes
## 417 no yes 1 yes
## 418 no no 2 yes
## 419 no yes 3 yes
## 420 yes no 1 yes
## 421 no yes 2 yes
## 422 no yes 0 yes
## 423 no no 2 yes
## 424 no no 0 yes
## 425 no no 1 yes
## 426 no no 0 yes
## 427 no no 1 yes
## 428 no no 1 yes
## 429 no no 0 yes
## 430 no yes 0 yes
## 431 no no 0 yes
## 432 no no 0 yes
## 433 no no 2 yes
## 434 no no 0 yes
## 435 no yes 2 yes
## 436 no yes 0 yes
## 437 no no 0 yes
## 438 no no 0 yes
## 439 no no 2 yes
## 440 no yes 2 yes
## 441 no no 2 yes
## 442 no no 0 yes
## 443 no no 0 yes
## 444 no yes 2 yes
## 445 no yes 0 yes
## 446 no no 1 yes
## 447 yes no 1 yes
## 448 no yes 1 yes
## 449 no no 2 yes
## 450 no no 0 yes
## 451 no no 0 yes
## 452 no yes 0 yes
## 453 no yes 0 yes
## 454 no no 1 yes
## 455 no no 0 yes
## 456 no no 1 yes
## 457 no yes 0 yes
## 458 no yes 0 yes
## 459 no no 0 yes
## 460 no no 0 yes
## 461 no no 0 yes
## 462 no no 0 yes
## 463 no no 0 yes
## 464 no no 0 yes
## 465 no no 0 yes
## 466 yes yes 0 yes
## 467 no no 0 yes
## 468 no no 0 yes
## 469 no no 0 yes
## 470 no no 0 yes
## 471 no no 1 yes
## 472 no no 0 yes
## 473 no no 3 yes
## 474 no no 0 yes
## 475 no no 0 yes
## 476 no no 1 yes
## 477 no no 1 yes
## 478 no no 2 yes
## 479 no yes 0 yes
## 480 no no 1 yes
## 481 no no 0 yes
## 482 no yes 2 yes
## 483 no no 0 yes
## 484 no no 0 yes
## 485 no no 2 yes
## 486 no yes 1 yes
## 487 no no 0 no
## 488 no no 1 no
## 489 no no 1 no
## 490 no no 0 no
## 491 no no 0 no
## 492 no no 0 no
## 493 no no 0 no
## 494 no no 1 no
## 495 no yes 0 no
## 496 no no 0 no
## 497 no no 0 no
## 498 no no 1 no
## 499 no no 0 no
## 500 no no 3 no
## 501 no yes 3 no
## 502 no yes 2 no
## 503 no no 0 no
## 504 no yes 0 no
## 505 no no 0 no
## 506 no no 0 no
## 507 no no 2 no
## 508 no yes 2 no
## 509 no yes 2 no
## 510 no no 1 no
## 511 no no 2 no
## 512 no no 0 no
## 513 no yes 0 no
## 514 no no 0 no
## 515 no no 1 no
## 516 yes no 0 no
## 517 no yes 0 no
## 518 no yes 0 no
## 519 no yes 1 no
## 520 no no 1 no
## 521 no yes 1 no
## 522 no yes 1 no
## 523 no yes 1 no
## 524 no no 2 no
## 525 no yes 1 no
## 526 no yes 2 no
## 527 no yes 2 no
## 528 no yes 1 no
## 529 no yes 1 no
## 530 no yes 0 no
## 531 no yes 2 no
## 532 no yes 2 no
## 533 no no 2 no
## 534 yes no 0 no
## 535 no yes 0 no
## 536 no yes 0 no
## 537 no yes 0 no
## 538 no yes 0 no
## 539 no yes 0 no
## 540 no no 0 no
## 541 no no 1 no
## 542 no yes 0 no
## 543 no yes 0 no
## 544 no yes 1 no
## 545 no yes 1 no
## 546 no yes 1 noSecond Data Set
data("CigarettesSW")
CigarettesSW## state year cpi population packs income tax price taxs
## 1 AL 1985 1.076 3973000 116.48628 46014968 32.50000 102.18167 33.34834
## 2 AR 1985 1.076 2327000 128.53459 26210736 37.00000 101.47500 37.00000
## 3 AZ 1985 1.076 3184000 104.52261 43956936 31.00000 108.57875 36.17042
## 4 CA 1985 1.076 26444000 100.36304 447102816 26.00000 107.83734 32.10400
## 5 CO 1985 1.076 3209000 112.96354 49466672 31.00000 94.26666 31.00000
## 6 CT 1985 1.076 3201000 109.27835 60063368 42.00000 128.02499 51.48333
## 7 DE 1985 1.076 618000 143.85114 9927301 30.00000 102.49166 30.00000
## 8 FL 1985 1.076 11352000 122.18112 166919248 37.00000 115.29000 42.49000
## 9 GA 1985 1.076 5963000 127.23462 78364336 28.00000 97.02517 28.84183
## 10 IA 1985 1.076 2830000 113.74558 37902896 34.00000 101.84200 37.91700
## 11 ID 1985 1.076 994000 103.01811 11577261 25.10000 102.89933 29.05767
## 12 IL 1985 1.076 11401000 123.20848 176786352 28.00001 104.44025 28.91526
## 13 IN 1985 1.076 5460000 137.63737 71751616 26.50000 96.18000 31.08000
## 14 KS 1985 1.076 2428000 116.68040 34784360 32.00000 98.92291 34.88125
## 15 KY 1985 1.076 3695000 186.03519 42703144 19.00000 87.00125 23.14292
## 16 LA 1985 1.076 4409000 127.55727 53431900 32.00000 108.39400 36.16900
## 17 MA 1985 1.076 5881000 115.67760 98328688 42.00000 112.20834 42.00000
## 18 MD 1985 1.076 4414000 120.97871 74851664 29.00000 91.96667 29.00000
## 19 ME 1985 1.076 1163000 128.11694 14575292 36.00000 107.04750 41.09750
## 20 MI 1985 1.076 9077000 128.00485 133728040 37.00000 104.91417 37.83083
## 21 MN 1985 1.076 4185000 112.90323 63152360 34.00000 113.64967 40.43300
## 22 MO 1985 1.076 5001000 130.37393 69341920 29.00000 99.33817 29.85484
## 23 MS 1985 1.076 2588000 117.04018 25678534 27.58333 105.29333 33.54333
## 24 MT 1985 1.076 822000 104.25790 9785230 32.00000 99.29166 32.00000
## 25 NC 1985 1.076 6255000 155.28377 79104656 18.00000 84.96799 21.26800
## 26 ND 1985 1.076 677000 105.46529 8672948 34.00000 106.80800 38.10800
## 27 NE 1985 1.076 1585000 107.38171 21778072 34.00000 104.60667 38.02333
## 28 NH 1985 1.076 997000 197.99399 15767469 33.00000 95.50000 33.00000
## 29 NJ 1985 1.076 7566000 116.52128 133549208 41.00000 110.41666 41.00000
## 30 NM 1985 1.076 1439000 88.74218 17258916 28.00000 102.77800 31.95300
## 31 NV 1985 1.076 951000 141.95584 14581495 31.00000 114.18850 37.46350
## 32 NY 1985 1.076 17794000 116.66292 297728512 37.00000 109.99783 37.88117
## 33 OH 1985 1.076 10736000 127.59874 153455776 30.00000 100.42374 34.78208
## 34 OK 1985 1.076 3272000 127.13937 43395580 34.00000 101.46808 34.82642
## 35 OR 1985 1.076 2673000 119.45380 36205164 35.00000 97.03333 35.00000
## 36 PA 1985 1.076 11772000 117.70303 170033840 34.00000 109.07401 40.17400
## 37 RI 1985 1.076 967000 132.78178 14229156 39.00000 100.94167 39.00000
## 38 SC 1985 1.076 3304000 127.20944 38536176 23.00000 90.64125 27.31625
## 39 SD 1985 1.076 698000 106.59026 8340000 31.00000 97.08334 31.00000
## 40 TN 1985 1.076 4716000 129.83459 57749668 29.00000 101.94550 34.77050
## 41 TX 1985 1.076 16275000 115.10293 231003152 35.25000 107.38000 39.38000
## 42 UT 1985 1.076 1643000 68.04626 19462380 28.00000 110.19584 34.23750
## 43 VA 1985 1.076 5716000 134.00980 87361632 18.50000 91.61533 22.02367
## 44 VT 1985 1.076 530000 145.28302 6887097 33.00000 100.98334 33.00000
## 45 WA 1985 1.076 4401000 96.22813 64846548 39.00000 129.46109 47.46942
## 46 WI 1985 1.076 4748000 107.87700 65732720 41.00000 114.59000 46.45667
## 47 WV 1985 1.076 1907000 112.84740 20852964 33.00000 108.91125 38.18625
## 48 WY 1985 1.076 500000 129.39999 7116756 24.00000 93.46667 24.00000
## 49 AL 1995 1.524 4262731 101.08543 83903280 40.50000 158.37134 41.90467
## 50 AR 1995 1.524 2480121 111.04297 45995496 55.50000 175.54251 63.85917
## 51 AZ 1995 1.524 4306908 71.95417 88870496 65.33333 198.60750 74.79082
## 52 CA 1995 1.524 31493524 56.85931 771470144 61.00000 210.50467 74.77133
## 53 CO 1995 1.524 3738061 82.58292 92946544 44.00000 167.35001 44.00000
## 54 CT 1995 1.524 3265293 79.47219 104315120 74.00000 218.28050 86.35550
## 55 DE 1995 1.524 718265 124.46660 18237436 48.00000 165.60001 48.00000
## 56 FL 1995 1.524 14185403 93.07455 333525344 57.90000 187.71718 68.52551
## 57 GA 1995 1.524 7188538 97.47462 159800448 36.00000 156.57307 37.43142
## 58 IA 1995 1.524 2840860 92.40160 60170928 60.00000 190.89000 69.09000
## 59 ID 1995 1.524 1165000 74.84978 22868920 52.00000 179.63751 60.55417
## 60 IL 1995 1.524 11884935 83.26508 304767456 68.00001 198.47617 79.23450
## 61 IN 1995 1.524 5791819 134.25835 126525008 39.50000 154.53375 46.85875
## 62 KS 1995 1.524 2586942 88.75344 56626672 48.00000 175.21001 56.34333
## 63 KY 1995 1.524 3855248 172.64778 74079712 27.00000 145.97968 35.26300
## 64 LA 1995 1.524 4327978 105.17613 84572688 44.00000 167.79535 50.45367
## 65 MA 1995 1.524 6062335 76.62064 170051568 75.00000 217.10501 85.33833
## 66 MD 1995 1.524 5023650 77.47355 135115456 60.00000 186.03375 68.85875
## 67 ME 1995 1.524 1237438 102.46978 25045934 61.00000 197.23065 72.16400
## 68 MI 1995 1.524 9659871 81.38825 231594240 99.00000 240.84967 112.63300
## 69 MN 1995 1.524 4605445 82.94530 113216856 72.00000 220.34866 86.41534
## 70 MO 1995 1.524 5324610 122.45028 117639672 41.00000 157.23009 42.38842
## 71 MS 1995 1.524 2690788 105.58245 46241956 42.00000 169.22940 53.07108
## 72 MT 1995 1.524 868522 87.15957 16296835 42.00000 156.21667 42.00000
## 73 NC 1995 1.524 7185403 121.53806 157633568 29.00000 149.99400 34.76900
## 74 ND 1995 1.524 641548 79.80697 12243384 68.00000 192.24867 78.88200
## 75 NE 1995 1.524 1635142 87.27071 36293064 58.00000 182.17500 66.67500
## 76 NH 1995 1.524 1145604 156.33675 28649564 49.00000 166.64166 49.00000
## 77 NJ 1995 1.524 7965523 80.37137 233208576 64.00000 203.08717 75.49550
## 78 NM 1995 1.524 1682417 64.66887 31716160 45.00000 176.14624 53.38792
## 79 NV 1995 1.524 1525777 93.52612 39377292 59.00000 206.59917 72.51583
## 80 NY 1995 1.524 18150928 70.81732 503163328 80.00000 221.85800 88.53300
## 81 OH 1995 1.524 11155493 111.38010 255312928 48.00000 165.89125 55.89958
## 82 OK 1995 1.524 3265547 108.68011 63333300 47.00000 170.13499 55.10167
## 83 OR 1995 1.524 3141421 92.15575 71209312 62.00000 190.30000 62.00000
## 84 PA 1995 1.524 12044780 95.64309 285923232 55.00000 176.16316 64.97150
## 85 RI 1995 1.524 989203 92.59980 23786644 80.00000 224.45924 94.68425
## 86 SC 1995 1.524 3699943 108.08275 72050072 31.00000 152.81874 38.27708
## 87 SD 1995 1.524 728251 97.21923 14454129 47.00000 168.03799 53.46300
## 88 TN 1995 1.524 5241168 122.32005 114259984 37.00000 167.06700 49.37533
## 89 TX 1995 1.524 18679706 73.07931 402096768 65.00000 198.20233 76.21900
## 90 UT 1995 1.524 1976774 49.27220 37278220 50.50000 180.97626 59.11792
## 91 VA 1995 1.524 6601392 105.38687 161441792 26.50001 166.66125 34.43626
## 92 VT 1995 1.524 582827 122.33475 12448607 44.00000 175.63875 52.36375
## 93 WA 1995 1.524 5431024 65.53092 129680832 80.50000 239.10934 96.14267
## 94 WI 1995 1.524 5137004 92.46635 115959680 62.00000 201.38126 71.58958
## 95 WV 1995 1.524 1820560 115.56883 32611268 41.00000 166.51718 50.42550
## 96 WY 1995 1.524 478447 112.23814 10293195 36.00000 158.54166 36.00000Descriptions for Data sets
1.HousePrices:
- Sales prices of houses sold in the city of Windsor, Canada, during July, August and September, 1987. A data fame containing 546 observations on 12 variables. We can see, based on the graph and table below, that this data set is cross sectional.
str(HousePrices)## 'data.frame': 546 obs. of 12 variables:
## $ price : num 42000 38500 49500 60500 61000 66000 66000 69000 83800 88500 ...
## $ lotsize : num 5850 4000 3060 6650 6360 4160 3880 4160 4800 5500 ...
## $ bedrooms : num 3 2 3 3 2 3 3 3 3 3 ...
## $ bathrooms : num 1 1 1 1 1 1 2 1 1 2 ...
## $ stories : num 2 1 1 2 1 1 2 3 1 4 ...
## $ driveway : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ recreation: Factor w/ 2 levels "no","yes": 1 1 1 2 1 2 1 1 2 2 ...
## $ fullbase : Factor w/ 2 levels "no","yes": 2 1 1 1 1 2 2 1 2 1 ...
## $ gasheat : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ aircon : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 1 1 2 ...
## $ garage : num 1 0 0 0 0 0 2 0 0 1 ...
## $ prefer : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...Price: Sale price of a house
lotsize: lot size of a property in square feet
bedrooms: number of bedrooms
stories: number of stories excluding basement
driveway: does the house have a driveway
recreation: does the house have a rec room
fullbase: is the basement fully finished
gasheat: gas or hotwater heating?
aircon: is there AC?
garage: number of garage places
prefer: is it located in a preferred neighborhood of the city?
price_na <- any(is.na(HousePrices$price))
price_na## [1] FALSElotsize_na <- any(is.na(HousePrices$lotsize))
lotsize_na## [1] FALSEbedroom_na <- any(is.na(HousePrices$bedrooms))
bedroom_na## [1] FALSEbathroom_na <- any(is.na(HousePrices$bathrooms))
bathroom_na## [1] FALSEstories_na <- any(is.na(HousePrices$stories))
stories_na## [1] FALSEdriveway_na <- any(is.na(HousePrices$driveway))
driveway_na## [1] FALSErecreation_na <- any(is.na(HousePrices$recreation))
recreation_na## [1] FALSEfullbase_na <- any(is.na(HousePrices$fullbase))
fullbase_na## [1] FALSEgasheat_na <- any(is.na(HousePrices$gasheat))
gasheat_na## [1] FALSEaircon_na <- any(is.na(HousePrices$aircon))
aircon_na## [1] FALSEgarage_na <- any(is.na(HousePrices$garage))
garage_na## [1] FALSEprefer_na <- any(is.na(HousePrices$prefer))
prefer_na## [1] FALSENo values missing in the data set.
g1 <- plot(HousePrices$price ~ HousePrices$lotsize,main = 'Price of House Sold Based on Lot Size', xlab= 'Lot Size (Sq. Feet)', ylab= 'Sale Price')
g1## NULL2. CigarettesSW :
- This data set consists of panel data on cigarette consumption for the 48 continental US States from 1985-1995. This data set contains 48 observations on 7 variables for 2 periods. We can tell by the graph, the summary of the data set in the library, and the table that this is a panel data set.
str(CigarettesSW)## 'data.frame': 96 obs. of 9 variables:
## $ state : Factor w/ 48 levels "AL","AR","AZ",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ year : Factor w/ 2 levels "1985","1995": 1 1 1 1 1 1 1 1 1 1 ...
## $ cpi : num 1.08 1.08 1.08 1.08 1.08 ...
## $ population: num 3973000 2327000 3184000 26444000 3209000 ...
## $ packs : num 116 129 105 100 113 ...
## $ income : num 4.60e+07 2.62e+07 4.40e+07 4.47e+08 4.95e+07 ...
## $ tax : num 32.5 37 31 26 31 ...
## $ price : num 102.2 101.5 108.6 107.8 94.3 ...
## $ taxs : num 33.3 37 36.2 32.1 31 ...State:(1-48) factor indicating which state is being analyzed out of the 48 states observed
Year: a factor indicating the year (1985 or 1995)
CPI: consumer price index
Population: state population
Packs: number of packs per capita
Income: state personal income (total, nominal)
Tax: average state, federal and average local excise taxes for fiscal year
Price: average price during fiscal year, including sales tax
Taxs: Average excise taxes for fiscal year, including sales tax
state_na <- any(is.na(CigarettesSW$state))
state_na## [1] FALSEyear_na <- any(is.na(CigarettesSW$year))
year_na## [1] FALSEcpi_na <- any(is.na(CigarettesSW$cpi))
cpi_na## [1] FALSEpopulation_na <- any(is.na(CigarettesSW$population))
population_na## [1] FALSEpacks_na <- any(is.na(CigarettesSW$packs))
packs_na## [1] FALSEincome_na <- any(is.na(CigarettesSW$income))
income_na## [1] FALSEtax_na <- any(is.na(CigarettesSW$tax))
tax_na## [1] FALSEprice_na <- any(is.na(CigarettesSW$price))
price_na## [1] FALSEtaxs_na <- any(is.na(CigarettesSW$taxs))
taxs_na## [1] FALSENo values missing in observations.
CigarettesMatrix <- matrix((CigarettesSW$packs), ncol=48)
CigarettesMatrix## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 116.4863 104.5226 112.9635 143.8511 127.2346 103.0181 137.6374 186.0352
## [2,] 128.5346 100.3630 109.2784 122.1811 113.7456 123.2085 116.6804 127.5573
## [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## [1,] 115.6776 128.1169 112.9032 117.0402 155.2838 107.3817 116.52128 141.9558
## [2,] 120.9787 128.0049 130.3739 104.2579 105.4653 197.9940 88.74218 116.6629
## [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
## [1,] 127.5987 119.4538 132.7818 106.5903 115.10293 134.0098 96.22813 112.8474
## [2,] 127.1394 117.7030 127.2094 129.8346 68.04626 145.2830 107.87700 129.4000
## [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
## [1,] 101.0854 71.95417 82.58292 124.46660 97.47462 74.84978 134.25835 172.6478
## [2,] 111.0430 56.85931 79.47219 93.07455 92.40160 83.26508 88.75344 105.1761
## [,33] [,34] [,35] [,36] [,37] [,38] [,39]
## [1,] 76.62064 102.46978 82.9453 105.58245 121.53806 87.27071 80.37137
## [2,] 77.47355 81.38825 122.4503 87.15957 79.80697 156.33675 64.66887
## [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47]
## [1,] 93.52612 111.3801 92.15575 92.5998 97.21923 73.07931 105.3869 65.53092
## [2,] 70.81732 108.6801 95.64309 108.0827 122.32005 49.27220 122.3348 92.46635
## [,48]
## [1,] 115.5688
## [2,] 112.2381barplot(CigarettesMatrix, legend=TRUE, beside=TRUE, main='Packs Per Capita by State', xlab='State', ylab='Number of Packs Per Capita' , ylim = c(0,200))
CigarettesSW$"1985" = (CigarettesSW$year==1985)
CigarettesSW$"1995" = (CigarettesSW$year==1995)
data1 <- matrix(CigarettesSW$packs, ncol=48)
margin.table(data1, margin=1)## [1] 5333.807 5147.706sum(CigarettesSW$packs)## [1] 10481.51g2 <- table(CigarettesSW$year, CigarettesSW$state)
g2##
## AL AR AZ CA CO CT DE FL GA IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT
## 1985 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1995 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##
## NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY
## 1985 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1995 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1Part 2.
Covariance measures the change in x with respect to a unit change in y.
Variance measures the dispersion of values around the mean
Covariance measures the joint variability of y and x,while variance measures how much x changes. By taking the ratio, we get the average change in y for each unit change in x, which is the slope.
lm(data=HousePrices, price ~ lotsize)## ## Call: ## lm(formula = price ~ lotsize, data = HousePrices) ## ## Coefficients: ## (Intercept) lotsize ## 34136.192 6.599cov(HousePrices$price, HousePrices$lotsize)/ var(HousePrices$lotsize)## [1] 6.598768