It’s a question I get pretty often, and it’s a more straightforward answer than most.
One of those “rules” about statistics you often hear is that you can’t interpret a main effect in the presence of an interaction.
Stats professors seem particularly good at drilling this into students’ brains.
Unfortunately, it’s not true.
A Linear Regression Model with an interaction between two predictors (X1 and X2) has the form:
Y = B0 + B1X1 + B2X2 + B3X1*X2.
It doesn’t really matter if X1 and X2 are categorical or continuous, but let’s assume they are continuous for simplicity.
One important concept is that B1 and B2 are not main effects, the way they would be if [Read more…] about Interpreting Lower Order Coefficients When the Model Contains an Interaction