The combined effect of two independent variables working together in a model is referred to as what?

The combined effect of two independent variables working together in a model is known as interaction. This concept comes into play particularly in regression analysis, where researchers are interested in understanding not just the individual effects of independent variables on a dependent variable, but also how these variables might influence each other. When two independent variables interact, it means that the effect of one independent variable on the dependent variable changes depending on the level of the other independent variable.

In a practical sense, modeling interactions allows for a more nuanced understanding of the relationships within data. For example, if you were studying the effects of education and experience on salary, the interaction might reveal that additional years of experience have a different impact on salary depending on the level of education achieved.

This distinction separates the concept of interaction from correlation, which refers to the strength and direction of a linear relationship between two variables without implying any cause-and-effect dynamics. Regression itself is a broader term that describes techniques for estimating the relationships among variables but does not specifically denote the combined effects of independent variables in an interaction sense. Multicollinearity, on the other hand, occurs when independent variables are highly correlated with each other, which can complicate the estimation of their individual effects but does not involve the combined effects per se.