[初賽第一階段] 用於解釋的迴歸與用於預測的迴歸（2） [Preliminary Phase 1] Regression for explanation vs. regression for prediction (2)

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, apply, analyze 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. Then, move students beyond conceptual understanding. First, introduce scenario classification: present several real-world problems and have students decide which paradigm fits best, justifying their choices. Second, demonstrate hands-on modeling: show students how to build a regression model. There is no need to go into deep implementation details, but students must know what the data looks like and what kinds of tools they can use. Key Learning Points: regression for explanations, regression for prediction; training-validation split, regularization; statistical significance; translating a real-world problem statement into a modeling goal, and selecting the appropriate paradigm (explanation vs. prediction) based on that goal