What do you do if the assumptions of linear models are violated?
In this training, you’ll learn common next steps to deal with serious violations of the explicit assumptions of linear regression models. From issues with independence, linearity, variance homogeneity, and normality, we will talk about various strategies and how they help out with the violations of these assumptions.
You’ll leave with a general idea of the various options available when standard linear regression models just aren’t cutting it.
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
Clark Kogan is an experienced statistical scientist and owner of StatsCraft LLC.
His expertise includes Bayesian models, generalized linear mixed models, research design, and R programming. As a collaborator, consultant, and mentor across multiple fields (including agriculture, veterinary medicine, psychology and pharmacy), Clark loves the challenge of finding the best statistical strategies for the specific research project. He especially enjoys the collaborative discussions that lead to insights and solutions.
Clark has a Ph.D. in Mathematics from the University of Montana, and currently serves as an adjunct faculty at Washington State University. In his prior positions, he was Associate Director of the Center for Interdisciplinary Statistical Education and Research at Washington State University and Data Scientist at Trove Predictive Data Science.
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