[初賽第一階段] 用於解釋的迴歸與用於預測的迴歸（1） [Preliminary Phase 1] Regression for explanation vs. regression for prediction (1)

Big Concept: Regression for explanation vs. regression for prediction Topic: Differences in the processes and focuses on using regression for explanation and prediction Bloom's Taxonomy: remember, understand Learning Goal: Deliver a clear lecture explaining the differences between using regression for explanation and using regression for prediction. In particular, explain that the purpose of explanation is to identify and interpret important influencing factors, while the purpose of prediction is to build a model that can make accurate predictions on new data. Explain why training-validation splits and regularization are central when building prediction models, but are usually not the main focus in traditional explanation-oriented regression. Also explain why statistical significance is important when building explanation models, while predictive performance is usually the main criterion for prediction models. Use real-world examples to illustrate these ideas. Key Learning Points: regression for explanations, regression for prediction; training-validation split, regularization; statistical significance