Member Training: Interactions in Poisson and Logistic Regression – Part 2

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.

In Part 1 of this two-part training, you learned how to understand interactions in Poisson and Logistic Regression models. The focus in that training was on the differences and similarities to interactions in linear models, with examples of interactions between one numeric and one categorical variable.

But there are other types of interactions! In this training, we’ll step it up by looking at two other types of interactions: categorical by categorical and numeric by numeric.

Once again, we’ll review how these interactions work in linear models, then extend those principles to Poisson and logistic regression models. As in the first training, you’ll learn how both graphs and model estimates help you interpret the interactions.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

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About the Instructor

Kim LoveKim is a Statistically Speaking mentor and workshop instructor for The Analysis Factor. She is also owner/lead consultant at K.R. Love Quantitative Consulting and Collaboration.

She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center.

Kim has more than a decade of professional and academic experience in the fields of regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design. She has a B.A. in mathematics from the University of Virginia, and an M.S. and PhD in statistics from Virginia Tech.

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