Interpreting the Coefficients of a Regression with an Interaction Term: A Detailed Explanation

Adding an interaction term to a linear model — estimated using regression — becomes necessary when the statistical association between a predictor and an outcome depends on the value/level of another predictor. Although adding an interaction term to a model can make it a better fit with the data, it simultaneously complicates the interpretation of the coefficients of the predictors. In this article, we explore how to interpret the coefficients of the predictors of a linear model that includes a two-way interaction term (between a continuous predictor and a binary predictor). We want to understand how the interpretation of coefficients differs between a model with an interaction term and a model without an interaction term. We use the statistical software R for estimating the models and visualizing the outcomes. <a href="https://vivdas.medium.com/interpreting-the-coefficients-of-a-regression-model-with-an-interaction-term-a-detailed-748a5e031724">Click Here</a>