Solution) Use SPSS to solve Data Analysis Problem 1 a, b, c, d, e in chapter 21. Use 11ef tutorial. Regression

Question: Use SPSS to solve Data Analysis Problem 1 a, b, c, d, e in chapter 21. Use 11ef tutorial.

Regression

Variables Entered/Removed^b
Model	Variables Entered	Variables Removed	Method
1	HIGHEST YEAR SCHOOL COMPLETED, MOTHER^a	.	Enter
a. All requested variables entered.
b. Dependent Variable: HIGHEST YEAR SCHOOL COMPLETED, FATHER

Model Summary^b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.639^a	.408	.407	3.162
a. Predictors: (Constant), HIGHEST YEAR SCHOOL COMPLETED, MOTHER
b. Dependent Variable: HIGHEST YEAR SCHOOL COMPLETED, FATHER

ANOVA^b
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	6231.521	1	6231.521	623.457	.000^a
	Residual	9045.579	905	9.995
	Total	15277.100	906
a. Predictors: (Constant), HIGHEST YEAR SCHOOL COMPLETED, MOTHER
b. Dependent Variable: HIGHEST YEAR SCHOOL COMPLETED, FATHER

Coefficients^a
Model		Un-standardized Coefficients		Standardized Coefficients	t	Sig.	95% Confidence Interval for B
Model		B	Std. Error	Beta	t	Sig.	Lower Bound	Upper Bound
1	(Constant)	2.572	.367		7.009	.000	1.852	3.292
1	HIGHEST YEAR SCHOOL COMPLETED, MOTHER	.760	.030	.639	24.969	.000	.701	.820
a. Dependent Variable: HIGHEST YEAR SCHOOL COMPLETED, FATHER

Residuals Statistics^a
	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	2.57	17.78	11.35	2.623	907
Std. Predicted Value	-3.348	2.450	.000	1.000	907
Standard Error of Predicted Value	.106	.367	.140	.050	907
Adjusted Predicted Value	2.44	17.82	11.35	2.624	907
Residual	-11.696	9.825	.000	3.160	907
Std. Residual	-3.699	3.108	.000	.999	907
Stud. Residual	-3.701	3.110	.000	1.001	907
Deleted Residual	-11.709	9.838	.000	3.167	907
Stud. Deleted Residual	-3.728	3.125	.000	1.002	907
Mahal. Distance	.017	11.209	.999	1.800	907
Cook's Distance	.000	.062	.001	.003	907
Centered Leverage Value	.000	.012	.001	.002	907
a. Dependent Variable: HIGHEST YEAR SCHOOL COMPLETED, FATHER

Run a regression equation to predict father’s education from mother’s education (variables paeduc and maeduc). Include 95% confidence intervals for the slope and intercept. Save the standard error of the mean prediction.

a. Write the linear regression equation to predict father’s education for mother’s education.

b. Based on the results of the linear regression, can you reject the null hypothesis that there is no linear relationship between father’s and mother’s education?

c. What proportion of the variability in mother’s education is explained by father’s education?

d. How can you tell from the slope if the correlation coefficient between the two variables is positive or negative?

e. What can you conclude about the population correlation coefficient based on what you know about the slope? Can you reject the null hypothesis that the population correlation coefficient is 0?

f. Based on the 95% confidence interval for the slope, can you reject the null hypothesis that the population value for the slope is 1? Explain

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