B3. In this exercise, you will run a simultaneous multiple regression analysis to predict the women’s level of depression (scores on the CESD depression scale, cesd) based on several demographic...

B3. In this exercise, you will run a simultaneous multiple regression analysis to predict the women’s level of depression (scores on the CESD depression scale, cesd) based on several demographic characteristics, socioeconomic characteristics, health status, and self-reported incidence of abuse in the prior year. The list of predictors is as follows: the two race/ethnicity dummy variables you created in exercise B2; age; educatn (educational attainment); worknow (a dummy variable for currently employed); nabuse (number of different types of abuse experienced in the past year, including verbal abuse, efforts to control, threats of harm, and physical abuse); and poorhlth (a dummy coded variable indicating self-reported poor health at the time of the interview. Bring up the regression dialog box by selecting Analyze ➞ Regression ➞ Linear. Insert the variable cesd in the box labeled Dependent. Insert the 7 predictor variables that we just mentioned into the box for Independent(s). Make sure that Method is set to “Enter,” the command for entering all predictors simultaneously. Click the Statistics pushbutton and then select the following options: Estimates (under “Regression Coefficients”); Model Fit; Descriptives; and Collinearity Diagnostics. Then click Continue, and OK to run the analysis. Answer the following questions: (a) How large is the sample on which the regression analysis was run? (b) Interpret the mean value for poor health self-rating. (c) Which predictor has the highest zero-order correlation with cesd? (d) What were the values of R²
and adjusted R²
? (e) Which predictors in the analysis were significantly predictive of the women’s depression scores, once other predictors were included? Which were not significantly predictive? (f) For this sample of women, which predictor variable appeared to be the most powerful in predicting depression? (g) Did any of the tolerance levels suggest a problem with multicollinearity?