Online Program

Relevance to Conference Goals (302854)

Modeling in the 21st century has grown beyond the basic ANOVA and linear model as most people learned them. This is especially true when the data cannot reasonably be assumed to be normally distributed. When data aren’t normal, why should your statistical analysis be? With the continued improvements in computing power and a wide variety of software options, the ability to model complex experiments and quasi-experiments with a variety of response variable distributions not limited to Gaussian is a required skill for data analysts. This course will introduce (or provide a refresher to) generalized linear mixed models as an overarching family that contains all its predecessors to participants. The course will familiarize participants with the essential thought processes required to use GLMMs effectively and with a wide variety of practical applications.