Assess understanding of the key assumptions underlying ordinary least squares linear regression (linearity, independence, homoscedasticity, normality of errors, and absence of serious multicollinearity), ability to interpret common residual plots (residuals vs fitted, Q–Q, scale-location, residuals vs leverage), recognize patterns that indicate assumption violations, and recommend appropriate diagnostic or corrective actions; questions will focus on conceptual interpretation and diagnostic reasoning rather than heavy computation.