If you’ve used much analysis of variance (ANOVA), you’ve probably heard that ANOVA is a special case of linear regression. Unless you’ve seen why, though, that may not make a lot of sense. After all, ANOVA compares means between categories, while regression predicts outcomes with numeric variables.
When you understand how the underlying model is the same, you can better interpret results, diagnose issues, and use the right approach to answer your specific research questions. You will also be ready to work with different kinds of predictors in more complicated regression models.
In this training, we review common ways of measuring results from linear regression and ANOVA. We demonstrate how ANOVA results can be represented through regression coefficients. You’ll see how it applies to examples of one-way and two-way ANOVA. Finally, we look at ANCOVA and general linear models, which combine linear regression and ANOVA.
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.
About the Instructor
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.
You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.