Linear regression is a powerful starting point, but many outcomes are not continuous and many relationships are not straight lines. Binary responses, counts, proportions, and nonlinear trends require models that can adapt to the structure of the data while remaining interpretable.

This six-part series introduces generalized linear models and generalized additive models as flexible extensions of ordinary linear regression. Participants will learn how response distributions, link functions, smooth effects, prediction, and grouped data can be incorporated into a statistical model. Emphasis is placed on selecting a model that matches the outcome, interpreting results on the correct scale, checking model adequacy, and communicating what the fitted model can support.

All workshops will use R and RStudio, so some experience with R or another programming language is encouraged but not required. See the R Fundamentals for Data Analysis for an introduction to R and RStudio. Attendees without prior experience are encouraged to review this content. No prior knowledge of linear regression is required, and each workshop will introduce the background needed for that session. 

Interpreting Generalized Linear Models (workshop 2 of 6): Coefficients from a generalized linear model are not always expressed on the scale researchers care about most. This session shows how to move between the link and response scales and interpret quantities such as odds ratios, rate ratios, fitted probabilities, and expected counts. Participants will also examine uncertainty, model fit, and common diagnostics.

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/