7.1 Introduction

In this chapter we will be looking at the interaction effects caused by predictors acting together on the response variable.

…additional variation in the response can be explained by the effect of two or more predictors working in conjunction with each other.

As an example consided are the effects of water and fertilizer on the yield of a field corn crop. “With no water but some fertilizer, the crop of field corn will produce no yield since water is a necessary requirement for plant growth. Conversely, with a sufficient amount of water but no fertilizer, a crop of field corn will produce some yield. However, yield is best optimized with a sufficient amount of water and a sufficient amount of fertilizer. Hence water and fertilizer, when combined in the right amounts, produce a yield that is greater than what either would produce alone.”

predictors are said to interact if their combined effect is different (less or greater) than what we would expect if we were to add the impact of each of their effects when considered alone.

• Correlations between predictors, for example, are not directly related to whether there is an interaction effect or not

• The individual variables (e.g., fertilizer and water) are referred to as the main effect terms when outside of an interaction.