Table 7.3, page 189. Education Expenditure Data.
get file 'd:p189.sav'. list.
state y x1 x2 x3 region ME 235 3944 325 508 1 NH 231 4578 323 564 1 VT 270 4011 328 322 1 MA 261 5233 305 846 1 RI 300 4780 303 871 1 CT 317 5889 307 774 1 NY 387 5663 301 856 1 NJ 285 5759 310 889 1 PA 300 4894 300 715 1 OH 221 5012 324 753 2 IN 264 4908 329 649 2 IL 308 5753 320 830 2 MI 379 5439 337 738 2 WI 342 4634 328 659 2 MN 378 4921 330 664 2 IA 232 4869 318 572 2 MO 231 4672 309 701 2 ND 246 4782 333 443 2 SD 230 4296 330 446 2 NB 268 4827 318 615 2 KS 337 5057 304 661 2 DE 344 5540 328 722 3 MD 330 5331 323 766 3 VA 261 4715 317 631 3 WV 214 3828 310 390 3 NC 245 4120 321 450 3 SC 233 3817 342 476 3 GA 250 4243 339 603 3 FL 243 4647 287 805 3 KY 216 3967 325 523 3 TN 212 3946 315 588 3 AL 208 3724 332 584 3 MS 215 3448 358 445 3 AR 221 3680 320 500 3 LA 244 3825 355 661 3 OK 234 4189 306 680 3 TX 269 4336 335 797 3 MT 302 4418 335 534 4 ID 268 4323 344 541 4 WY 323 4813 331 605 4 CO 304 5046 324 785 4 NM 317 3764 366 698 4 AZ 332 4504 340 796 4 UT 315 4005 378 804 4 NV 291 5560 330 809 4 WA 312 4989 313 726 4 OR 316 4697 305 671 4 CA 332 5438 307 909 4 AK 546 5613 386 484 4 HI 311 5309 333 831 4
Number of cases read: 50 Number of cases listed: 50
Table 7.4 and Figure 7.3, 7.5-7.7, page 191.
regression /dependent = y /method enter = x1 x2 x3 /scatter (*sresid, *pred) (*sresid, x1) (*sresid, x2) (*sresid, x3) /save sresid(sr).
Figure 7.4, page 191.
graph /scatterplot = region with sr.
Table 7.5 and Figure 7.8, page 194-195. Alaska omitted.
select if (state ~= "AK"). regression /dependent = y /method enter = x1 x2 x3 /scatter (*sresid, *pred) /save sresid(sr_ak).
Figure 7.9, page 194.
graph /scatterplot = region with sr_ak.
Table 7.6, page 195. Weights cj for Weighted Least Squares.
Calculation of weights is from the residuals from the regression.
regression /dependent = y /method enter = x1 x2 x3 /save resid(r).[output omitted]
compute r2 = r**2. compute const = 1. exe. aggregate outfile "e:tempag1.sav" /break =region /s2 = mean(r2). aggregate outfile "e:tempag2.sav" /break =const /r2mean = mean(r2). match files file = * /table ="e:tempag1.sav" /by region. match files file = * /table = "e:tempag2.sav" /by const. exe. compute c = sqrt(s2/r2mean). exe. temporary. split file by region. descriptives var = c r2 /stats = mean.
Table 7.7, page 195. WLS coefficients for the education data (Alaska
omitted).
compute weight = c**-2. exe. regression /regwgt = weight /dependent = y /method enter = x1 x2 x3 /save resid(wlsr) pred(wlsp).
Figure 7.10, page 196. Standardized residuals versus fitted values of for WLS
solution.
NOTE 1: The predicted values and residuals need to be adjusted to account for
the weighting.
NOTE 2: Used unstandardized residuals, so the scaling is off from the text.
compute wp = wlsp/c. compute wr = wlsr/c. exe. graph /scatterplot = wp with wr.
Figure 7.11, page 196. Standardized residuals by geographic region for WLS
solution.
graph /scatterplot = region with wr.
