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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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