B9. With only 3.8% missing values on the CES-D scale in a very large sample, we might well use listwise deletion for any further substantive analyses with the cesd variable. As an exercise, however, we will impute missing values on the CES-D total scale using regression analysis. We will restrict the regression to two predictors that are significantly correlated with CES-D scores, but that have minimal missing values themselves: educational attainment and current employment status. (Other variables with low levels of missing data—age, race/ethnicity, and number of children—were not significantly correlated with CES-D scores; you could verify this yourself.) Use the instructions for running a standard multiple regression as discussed in the topic on Multiple Regression, with cesd as the dependent variable and educational attainment and current employment status as the independents. Then answer the following questions:

(a) How many cases were used in this regression analysis?

(b) What was the value of R?

(c) Was the overall model statistically significant?

(d) Was educational attainment significant, once current employment was controlled?

(e) Was current employment status significant with educational attainment controlled?

(f) Interpret what the b weights mean in terms of scores on the CES-D. (g) What is the regression equation for imputing missing values on cesd?