General Linear Model: Model Selection

This lecture covers the General Linear Model, including significance testing and model selection. Topics include the mathematical formulation of the model, a pesticide toxicity example, comparing candidate models, and parameter inference using summary statistics. The instructor explains how to compare models using ANOVA and drop1 methods, emphasizing the importance of nested models and the consequences of correlated predictors. The lecture also delves into the Multiple R-squared concept, the Additional Sum-of-Squares principle, and provides examples to illustrate these statistical concepts.