STA Class 4: Interactions Between Variables

An interaction in a regression model allows the slope of one variable to depend on __________.

the value of another variable.

An interaction is modeled as _____ _____ of two variables.

the product.

Non-interacted coefficients are called ____ ______.

main effects.

In a model with an interaction term X1 * X2, you must also keep ____ _____ _____.

the main effects.

The main effect of X1 represents the predicted increase in Y for a 1-unit change in X1, holding X2 ___ ____ ___.

constant at 0.

Interactions make a model more complex to analyze, so it’s only worth it when you get a substantial bump in __________ by including the interaction.

R-squared.

Choose interactions by thinking about what you are trying to model: if you suspect the impact of one variable depends on the value of another, try an __________ term between them.

interaction.

Interactions are not the same as __________ between predictors.

correlations.

Interactions model a situation where the relationship of one predictor variable and Y is different depending on ___ ____ ___.

another X variable.

If the effect of one variable depends on the value of another, then we need an __________.

interaction.

Interactions serve as ___ -_______.

slope-modifiers.

By the hierarchical principle, if we choose to include an interaction, we must also include the _____ _____.

main effects.