In this section, you learn how to:
- Interpret individual effects, based on their substantive and statistical significance
- Learned about causal effects and necessary conditions for causality
- Describe other purposes of regression modeling, including regression function for pricing, benchmarking studies, and predicting future observations.
Video Overview of the Section (Alternative .mp4 Version -8:49 min)
Model inference is the final stage of the modeling process. By studying the behavior of models, we hope to learn something about the real world. Models serve to impose an order on reality and provide a basis for understanding reality through the nature of the imposed order. Further, statistical models are based on reasoning with the available data from a sample. Thus, models serve as an important guide for predicting the behavior of observations outside the available sample.