Linear regression is often one of the first statistical models researchers encounter, but using it well involves much more than drawing a straight line. Researchers must understand what the model is estimating, whether its assumptions are reasonable, and how the results relate to the original research question.

This series introduces linear regression as a framework for studying relationships, testing hypotheses, and making predictions. Participants will progress from simple and multiple linear regression to model interpretation, prediction, interaction effects, hierarchical models, and penalized regression. Emphasis is placed on understanding model assumptions, evaluating model fit and predictive performance, and communicating results without overstating what the model can establish.

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. 

Fitting vs. Predicting (workshop 4 of 6): A model that describes the observed data well does not necessarily predict new observations well. This session separates explanatory modelling from prediction and introduces training and test data, out-of-sample evaluation, and common prediction metrics. Participants will also distinguish confidence intervals from prediction intervals and examine how overfitting can give a misleading impression of performance.

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/