This time including SEX as a predictor (coded SEX = 1 if female, SEX = 0 if male).
a. Examine a plot of the studentized or jackknife residuals versus the predicted values. Are any regression assumption violations apparent? If so, suggest possible remedies.
b. Examine numerical descriptive statistics, histograms, box-and-whisker plots, and normal probability plots of jackknife residuals. Is the normality assumption violated? If so, suggest possible remedies.
c. Examine outlier diagnostics, including Cook's distance, leverage statistics, and jackknife residuals, and identify any potential outliers. What course of action, if any, should be taken when outliers are identified?
d. Examine variance inflation factors, condition indices (unadjusted and adjusted for the intercept), and variance proportions. Are there any important collinearity problems? If so, suggest possible remedies.