Regression Methods: Model Building and Inference

This lecture covers topics such as analysis of variance, model building, variable selection, robustness, regularized regression, mixed models, scatterplot smoothing, and function estimation. It discusses the estimation of functions with covariates, smooth functions, and the identifiability problem in additive models. The lecture also delves into effective degrees of freedom, selection of smoothing parameters, and numerical aspects of regression methods. Examples using spring barley data illustrate the application of these concepts in practice.