Model Selection

This lecture discusses the importance of model selection in data analysis, emphasizing the tradeoff between model complexity and residual error. It covers the concept of overspecified models with too many parameters and the need for simplicity to explain data effectively. The instructor explains the process of comparing nested models and demonstrates how to determine the best model using F-tests. Practical examples are provided to illustrate the decision-making process in selecting the most appropriate model for a given dataset.