Generalized linear models are designed to work with outcomes that aren’t normally distributed, but have other recognizable characteristics, such as being counts, proportions, or belonging to categories. They are often exactly what you need when you just can’t get a normal distribution to fit.
Note: This webinar is only accessible to members of the Statistically Speaking Membership Program.
Date and Time
Wednesday, September 19, 2018
11am – 12:30pm (US EDT) (In a different time zone?)
About the Instructor
Kim is a workshop instructor for The Analysis Factor and 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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- February 2013 Member Webinar: Types of Regression Models and When to Use Them
- September 2017 Member Webinar: Quantile Regression: Going Beyond the Mean
- What R Commander Can do in R Without Coding–More Than You Would Think
- Generalized Linear Models in R, Part 7: Checking for Overdispersion in Count Regression