Linear and Logistic Regression

Linear regression predicts continuous outcomes: $y = \beta_0 + \beta_1 x + \epsilon$. Logistic regression predicts probabilities for binary outcomes using the sigmoid function.

Interview questions: "What are the assumptions of linear regression?" "How do you interpret the coefficients?" "What's the difference between R-squared and adjusted R-squared?" These basics are tested repeatedly.