regression-diagnostics

Read a regression output table and check whether it can be trusted for inference. Always use this skill when someone shares regression output and asks how to read the estimates, standard errors, t values, p-values, R-squared, adjusted R-squared or F test; whether a coefficient is significant and can be trusted; whether a model with a low R-squared but a significant F test is any good; whether to use robust or clustered standard errors, for example because the same customers or stores appear in many rows; what a residual or Q-Q plot shows; or whether outliers or influential points (leverage, Cook's distance) drive the fit, even when the question sounds routine. Also use it for fan-shaped residuals, unequal variance and the Breusch-Pagan test, heavy tails and curved residual patterns. Not for judging how large prediction errors are, not for deciding whether X causes Y, and not for multicollinearity, VIF or regularisation.