How do you include and interpret interactions in regression?

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Answer

$x_{1}\; \cdot \;x_{2}$
$x_{1}$
$Center predictors before interaction to avoid multicollinearity between the main effect and the product term.$
$For categorical \times numeric interactions: separate slopes per category.$
$Test with joint F-test or LRT.$
$In production ML, tree-based methods learn interactions automatically — GLM interactions matter mostly for interpretation.$

Check yourself — multiple choice

Just add
$x_{1} \cdot x_{2}$ with main effects; effect of $x_{1}$ depends on $x_{2}$ ; center predictors to reduce collinearity; test with F or LRT
Only for logistic
Not needed

Interactions: product term + main effects; center to reduce collinearity.

#regression

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