In linear regression: 1) List and explain the OLS assumptions (linearity, independence/no autocorrelation, homoscedasticity, normality of errors for inference, no perfect multicollinearity, correct specification). 2) Define multicollinearity and describe its effects on coefficient variance, stability, confidence intervals, and p-values while noting that OLS point estimates remain unbiased. 3) Show how to diagnose multicollinearity (correlation matrix, VIF thresholds, condition number, eigenvalue analysis). 4) Propose remedies (collect more data, drop/combine features, center variables and interaction terms, ridge/LASSO/elastic net, PCA/PLS) and discuss their trade-offs. 5) If two predictors are perfectly collinear, what happens to X'X and how do implementations typically handle it?