Member Training: Assumptions of Linear Models

What are the assumptions of linear models? If you compare two lists of assumptions, most of the time they’re not the same.


In this webinar, we introduce the nuts and bolts of the assumptions, both those explicitly stated and those that are implicit in the model equation. Not just what they are, but why they’re important, what they really tell you about how well the model works for the data.

You’ll leave with an intuitive understanding of what to check (and what you can’t), how to check them, and an idea of what to do next if assumptions fail.

Date and Time

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About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

You'll get access to this training webinar, 130+ 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.

Four Critical Steps in Building Linear Regression Models

While you’re worrying about which predictors to enter, you might be missing issues that have a big impact your analysis. This training will help you achieve more accurate results and a less-frustrating model building experience.

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