Test knowledge of residual analysis and diagnostic measures in multiple linear regression: assess assumptions (linearity, independence, homoscedasticity, normality), interpret residual vs. fitted and Q-Q plots, detect outliers and influential observations using leverage, Cook's distance, DFBETAS, and standardized residuals, understand common thresholds, and recommend appropriate remedies (transformations, variable selection, robust methods) to improve model fit.