Episode 5 connects the “big picture” of multiple linear regression: the matrix form of the model, how least squares and maximum likelihood lead to the same estimates under standard assumptions, and what the ANOVA table is really decomposing.
We compare r-square vs. adjusted r-square, review t-tests for individual predictors and the F-test for overall model validity, and finish with practical model selection (AIC and partial F-tests) plus examples on diagnosing outliers and interpreting results.
