Assess learners' ability to analyze how multicollinearity influences linear regression coefficient estimates, standard errors, hypothesis tests, and interpretability; evaluate detection techniques (VIF, condition indices, correlation matrices), quantify effects on parameter stability and p-values, and select appropriate remedies (variable selection, ridge/LASSO regularization, PCA) while advising on communicating results to stakeholders.