Assess learners' ability to analyze residuals, leverage, and influence in multiple linear regression models. Test interpretation and calculation of residual diagnostics (raw, standardized, studentized), graphical checks (residual vs fitted, Q–Q), leverage (hat values), and influence measures (Cook's distance, DFBETAS). Require diagnostic decision-making: detecting problematic points, assessing assumption violations (linearity, homoscedasticity, normality), and choosing appropriate responses (transformations, casewise investigation, robust methods, or model refitting).