In regression, what is the primary purpose of including interaction terms?

Including interaction terms in a regression model serves the primary purpose of assessing the combined effect of variables. Interaction terms are created by multiplying two or more predictor variables together, allowing the model to capture the scenario where the effect of one predictor on the dependent variable varies depending on the level of another predictor.

This detailed analysis can reveal more complex relationships in the data that a simple additive model would overlook. For instance, if you have two variables related to customer behavior—such as price and advertising spend—the interaction term would help you understand how the impact of price on sales might change at different levels of advertising spend. Without interaction terms, the model would assume that the effect of each predictor is constant, which may not be the case in real-world situations.

This approach is particularly useful in uncovering synergies or conflicts between variables that are not visible when examining each variable independently, thus enhancing the interpretability and validity of the model's results.