### SPSS Textbook Examples Applied Regression Analysis by John Fox Chapter 6: Statistical inference for regression

page 117 The standard errors and confidence intervals for a and b.

GET FILE='D:\davis.sav'.

if subject=12 measwt=57.
if subject=12 measht=166.
execute.

USE ALL.
COMPUTE filter_$=(female=1). VARIABLE LABEL filter_$ 'female=1 (FILTER)'.
VALUE LABELS filter_$0 'Not Selected' 1 'Selected'. FORMAT filter_$ (f1.0).
FILTER BY filter_$. EXECUTE. REGRESSION /STATISTICS COEFF OUTS CI R ANOVA /DEPENDENT measwt /METHOD=ENTER reptwt.  Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 Reported Weight(a) . Enter a All requested variables entered. b Dependent Variable: Measured Weight Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .955(a) .912 .911 2.057 a Predictors: (Constant), Reported Weight ANOVA(b) Model Sum of Squares df Mean Square F Sig. 1 Regression 4334.889 1 4334.889 1024.544 .000(a) Residual 418.873 99 4.231 Total 4753.762 100 a Predictors: (Constant), Reported Weight b Dependent Variable: Measured Weight Coefficients(a) Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B Model B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 1.778 1.744 1.019 .311 -1.684 5.239 Reported Weight .977 .031 .955 32.009 .000 .917 1.038 a Dependent Variable: Measured Weight page 122 The regression coefficients listed at the top of the page. GET FILE='D:\duncan.sav'. REGRESSION /STATISTICS COEFF OUTS R ANOVA /DEPENDENT prestige /METHOD=ENTER educ income.  Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 Percent of males in occupation earning$3500 or more in 1950, Percent of males in occupation in 1950 who were high-school graduates(a) . Enter
a All requested variables entered.
b Dependent Variable: Percent of raters in NORC study rating occupation as excellent or good in presti

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .910(a) .828 .820 13.369
a Predictors: (Constant), Percent of males in occupation earning $3500 or more in 1950, Percent of males in occupation in 1950 who were high-school graduates ANOVA(b) Model Sum of Squares df Mean Square F Sig. 1 Regression 36180.946 2 18090.473 101.216 .000(a) Residual 7506.699 42 178.731 Total 43687.644 44 a Predictors: (Constant), Percent of males in occupation earning$3500 or more in 1950, Percent of males in occupation in 1950 who were high-school graduates
b Dependent Variable: Percent of raters in NORC study rating occupation as excellent or good in presti

Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) -6.065 4.272
-1.420 .163
Percent of males in occupation in 1950 who were high-school graduates .546 .098 .516 5.555 .000
Percent of males in occupation earning $3500 or more in 1950 .599 .120 .464 5.003 .000 a Dependent Variable: Percent of raters in NORC study rating occupation as excellent or good in presti page 125 The statistics in the middle of the page. REGRESSION /STATISTICS COEFF OUTS R ANOVA /DEPENDENT prestige /METHOD=ENTER income /method=test (educ).  Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 Percent of males in occupation earning$3500 or more in 1950(a) . Enter
2 Percent of males in occupation in 1950 who were high-school graduates . Test
a All requested variables entered.
b Dependent Variable: Percent of raters in NORC study rating occupation as excellent or good in presti

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .838(a) .702 .695 17.403
2 .910(b) .828 .820 13.369
a Predictors: (Constant), Percent of males in occupation earning $3500 or more in 1950 b Predictors: (Constant), Percent of males in occupation earning$3500 or more in 1950, Percent of males in occupation in 1950 who were high-school graduates

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression 30664.844 1 30664.844 101.252 .000(a)
Residual 13022.800 43 302.856

Total 43687.644 44

2 Subset Tests Percent of males in occupation in 1950 who were high-school graduates 5516.101 1 5516.101 30.863 .000(b) .126
Regression 36180.946 2 18090.473 101.216 .000(c)
Residual 7506.699 42 178.731

Total 43687.644 44

a Predictors: (Constant), Percent of males in occupation earning $3500 or more in 1950 b Tested against the full model. c Predictors in the Full Model: (Constant), Percent of males in occupation earning$3500 or more in 1950, Percent of males in occupation in 1950 who were high-school graduates.
d Dependent Variable: Percent of raters in NORC study rating occupation as excellent or good in presti

Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 2.457 5.190
.473 .638
Percent of males in occupation earning $3500 or more in 1950 1.080 .107 .838 10.062 .000 2 (Constant) -6.065 4.272 -1.420 .163 Percent of males in occupation earning$3500 or more in 1950 .599 .120 .464 5.003 .000
Percent of males in occupation in 1950 who were high-school graduates .546 .098 .516 5.555 .000
a Dependent Variable: Percent of raters in NORC study rating occupation as excellent or good in presti

Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 Percent of males in occupation in 1950 who were high-school graduates .516(a) 5.555 .000 .651 .475
a Predictors in the Model: (Constant), Percent of males in occupation earning \$3500 or more in 1950
b Dependent Variable: Percent of raters in NORC study rating occupation as excellent or good in presti

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