This lecture provides a comprehensive overview of linear regression modeling, focusing on ordinary least squares (OLS) estimation, its mathematical formulation, and statistical properties. It discusses the derivation of the least squares estimator, the interpretation of the Hat matrix as a projection, the distributional assumptions under the normal linear model, inference using t- and F-tests, and model diagnostics including residual analysis and influence measures, concluding with extensions to generalized least squares for correlated errors.