# Interpreting Interactions

### Member Training: Linear Model Assumption Violations: What’s Next?

June 30th, 2023 by

What do you do if the assumptions of linear models are violated?
(more…)

### Member Training: Interactions in Poisson and Logistic Regression

May 1st, 2023 by

Interactions in statistical models are never especially easy to interpret. Throw in non-normal outcome variables and non-linear prediction functions and they become even more difficult to understand. (more…)

### Centering a Covariate to Improve Interpretability

April 9th, 2021 by

Centering a covariate –a continuous predictor variable–can make regression coefficients much more interpretable. That’s a big advantage, particularly when you have many coefficients to interpret. Or when you’ve included terms that are tricky to interpret, like interactions or quadratic terms.

For example, say you had one categorical predictor with 4 categories and one continuous covariate, plus an interaction between them.

First, you’ll notice that if you center your covariate at the mean, there is (more…)

November 5th, 2018 by

Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.

As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)

### Using Marginal Means to Explain an Interaction to a Non-Statistical Audience

July 10th, 2018 by

Even with a few years of experience, interpreting the coefficients of interactions in a regression table can take some time to figure out. Trying to explain these coefficients  to a group of non-statistically inclined people is a daunting task.

For example, say you are going to speak to a group of dieticians. They are interested (more…)

### Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

May 14th, 2018 by

We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?

What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? (more…)