B4. Create a correlation matrix with four variables in the Polit2SetB dataset. The four variables are Variables #11, 18, 44, and 45: Number of visits to the doctor in the past 12 months

(docvisit); body mass index (bmi); standardized score on the Short-Form Health Survey or SF12, Physical health component subscale (sf12phys); and standardized score on the ShortForm Health Survey, Mental health component subscale

(sf12ment). The SF-12 is a brief, widely used measure of health status. In this dataset, standardized T scores were created using national norms based on a national mean of 50.0 and an SD of 10.0. For both subscales, higher scores represent more favorable health. To produce a correlation matrix, use Analyze ➜ Correlate ➜ Bivariate. Move the four variables into the variable list. The type of correlation should be set to Pearson. Click on the Options pushbutton, and select means and standard deviations under Statistics and Exclusion of cases pairwise under Missing Values. Then run the analysis and answer the following questions based on the output, ignoring information labeled “Sig. 2-tailed”:

(a) On the SF-12 subscales, were women in this sample, on average, about as healthy—both physically and mentally—as people nationally?