Includes checking for multicollinearity (when variables are too similar), model adequacy, and using "dummy variables" for non-numerical data.
Techniques like variable selection and validation to ensure the model works on new data. Where to Find it in EPUB
A "complete version" of a regression course or text generally covers the following progression:
values to determine how much variance is explained by the model.
Finding the "best-fitting line" that minimizes prediction errors. Key Metrics: Interpretation of coefficients (slopes) and R2cap R squared
Using two or more independent variables to predict a single outcome.
Modeling the relationship between one dependent variable ( ) and one independent variable (