December 12-13, 2023 | (Face-to-face)
Mixed models are a powerful, flexible tool that empower biologists to control for various sources of non-independence (“pseudoreplication”) without loosing (hardly any) statistical power to detect a significant pattern in their variable of interest. In this two-day course, students, who must already familiar with linear models and GLM in R, will learn how to code an implement the next natural step: mixed models. Students will learn the difference between fixed effects and random effects, will learn how to ditch clunky transformations and instead embrace the native distribution of the data, and will be forced to think critically about experimental design.
Application deadline: October 27, 2023
More details available here
Communication, Advancement and Engagement Unit (CA&EU)