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(Hierarchical) Generalized Linear Models
Join us for the Fitting Models to Data Not Data to Models workshop series! This workshop series introduces early-career researchers to statistical models beyond linear models, focusing on fitting models to data rather than forcing data to fit model assumptions. Sessions use R and RStudio; prior experience is helpful but not required - beginners are encouraged to review R Fundamentals for Data Analysis or attend the intro workshop.
(Hierarchical) Generalized Linear Models: This workshop will introduce Generalized Linear Models (GLMs), which allow one to model non-Gaussian (i.e., non-normal) data. By the end of this session, participants will be familiar with the three parts of GLMs (family of distribution, linear predictor, and link function) and will be able to decide what family of distributions and link function to choose for their data. They will also be able to interpret the output of the summary() function and diagnostic plots for (H)GLMs and recognize the limitations of (H)GLMs.
Questions? Please reach out to the Centre for Scholarly Communication at csc.ok@ubc.ca.
A full schedule of workshops can be found at csc.ok.ubc.ca/workshops/