Multiple Linear Regression in `R`

This workshop will demystify ANOVAs by framing them in the context of linear models with multiple predictors (i.e., multiple linear regression). The session will also introduce attendees to Directed Acyclical Graphs (DAGs) and demonstrate how to use them to infer causality in one’s model.

By the end of this session participants should be able to fit linear models with more than one predictor, check for collinearity between predictors, and interpret linear models using DAGs.

Fitting Models to Data Not Data to Models Workshop Series

You might have heard someone say that “all models are wrong, but some are useful”. The best way to ensure models are useful is to choose a model that is appropriate to your data and research questions, rather than forcing your data to fit your model’s assumptions (e.g., normality, independence, constant variance).

This series introduces early-career researchers to statistical models that extend beyond linear models (i.e., ANOVAs) so that they may learn how to fit models to their data rather than fitting their data to models. All workshops will use R and RStudio, so some experience with R or other programming languages is encouraged but not required. See the R Fundamentals for Data Analysis for an introduction to R and RStudio. Attendees who do not have experience with R are encouraged to review this content or take the introductory workshop concurrently if it’s being offered.

Register for individual workshops

Register for all workshops in the series

For a complete list of upcoming CSC Workshops, please visit our workshops page.

This workshop qualifies for the Scholarly Research, Writing, and Publishing Credential offered through the College of Graduate Studies.

Questions? Contact the Centre for Scholarly Communication at csc.ok@ubc.ca.