Interpreting regression coefficients can be tricky, especially when the model has interactions or categorical predictors (or worse – both).
But there is a secret weapon that can help you make sense of your regression results: marginal means.
They’re not the same as descriptive stats. They aren’t usually included by default in our output. And they sometimes go by the name LS or Least-Square means.
And they’re your new best friend.
So what are these mysterious, helpful creatures?
What do they tell us, really? And how can we use them?
In this webinar, we’ll address these important questions and more, like:
— Can they be produced for interactions?
— How can we generate them?
— Does all statistical software produce the same results?
— If not, how are the results different?
We’ll get in under the hood to understand how marginal means are produced. We’ll also show examples for linear, logistic and count models. And we’ll compare and contrast the output from SPSS, R, SAS and Stata.
You’ll learn when and how to best use marginal means to make your results make sense – to you and to your audience.
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
Jeff Meyer is a statistical consultant, instructor and writer for The Analysis Factor.
Jeff has an MBA from the Thunderbird School of Global Management and an MPA with a focus on policy from NYU Wagner School of Public Service.
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