Regression Modeling: Variable Selection

This lecture discusses the process of model building in regression modeling, focusing on the aims of understanding and prediction. The instructor explains the importance of interpretation in understanding the underlying reality and the role of prediction in controlling processes. Different models may fit equally well, leading to the need for model selection based on alternative explanations. The lecture also covers the meta algorithm for statistical investigation, including steps such as exploratory data analysis, choosing a response variable, fitting models, and evaluating model quality. Variable selection is highlighted as a crucial step in model building, involving considerations like the independence of variables, model coherence, and the inclusion of relevant covariates.