Statistical Inference: Linear Models

This lecture covers the statistical inference for linear models, focusing on the relationship between the response variable and the explanatory variable. It discusses the assumptions of independence, noise dependence, and model fitting using maximum likelihood estimation. The lecture also delves into parameter estimation, model comparison, and variance decomposition. Additionally, it explores the analysis of residuals, model testing, and the interpretation of coefficient of determination. Practical examples with ozone data are used to illustrate the concepts.