Linear Models

OLS in interview form: the assumptions and which ones matter for prediction vs inference, reading coefficients without lying, catching multicollinearity with VIF, and knowing when $R^2$ is the wrong scoreboard

A linear model pushed through a sigmoid: coefficients are log-odds, $e^\beta$ is an odds ratio, and cross-entropy — not MSE — is the loss, for reasons every interviewer asks about