Episode 4 introduces multiple linear regression—how to model an outcome using several predictors at once, and how to interpret each effect while holding the others constant.
We cover dummy variables for categorical data, and interaction terms (e.g., how experience and gender together can change salary patterns). We also compare regression with the two-sample mean test, showing how they’re related but regression is more flexible. We end with a practical note: p-values aren’t the whole story, and conclusions should rely on context and assumptions, with nonparametric options available when data don’t fit normality well.
