Regression: Linear Models

This lecture covers linear regression, focusing on least squares regression, residuals, confidence intervals for coefficients and variance, and regression diagnostics. It explains the setup of linear regression models, polynomial regression examples, and assumptions like normal theory assumptions. The lecture also delves into the Gauss-Markov Theorem, maximum likelihood approach, and regression diagnostics through residual analysis and distribution plots.