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

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