Always check your assumptions before trusting the results. A residual plot takes 30 seconds and can save you from presenting misleading findings.
Strong answers cover: use cases (predicting outcomes, understanding relationships, controlling for confounds), key assumptions (linearity, independence, homoscedasticity, normality of residuals), and practical diagnostics (residual plots, VIF for multicollinearity). Best candidates discuss when simpler methods are better and how to communicate regression results to non-technical audiences.
Tests statistical depth. Not all analyst roles need regression, but understanding it reveals statistical literacy. Ask follow-up: "How do you explain a regression coefficient to a marketing manager?"