Linear Regression: Multicollinearity, Outliers, Model Specification

This lecture covers various topics important in linear regression, such as multicollinearity, outliers, and model specification. It discusses the impact of violating OLS assumptions, the importance of the base category in categorical variables, and the consequences of omitted variable bias. Practical strategies for dealing with outliers and model selection are also explored, along with the use of information criteria and specification tests. The presentation of regression results and the cautionary notes on interaction terms, higher-order functions, and categorical variables are highlighted.