Modeling Binary Outcomes: Logistic Regression in R

Do you want to analyze outcomes like disease presence, voting behavior, or customer churn? Logistic regression is a powerful method for modeling binary outcomes and understanding how different factors influence the likelihood of an event. In this hands-on workshop, you’ll learn how to use R to build and interpret logistic regression models, helping you make informed decisions based on your data.

This workshop introduces logistic regression using R, with a focus on practical applications and interpretation. Participants will start with foundational concepts and gradually build to fitting multiple logistic regression models. Through guided examples, you’ll learn how to structure your data, fit models using glm(), interpret coefficients (including odds ratios), and evaluate model performance.

This session is ideal for participants who have some experience with R and are ready to explore statistical modeling in a supportive, practical setting. Attending the linear regression workshop or having prior familiarity with linear regression concepts, will enhance your learning experience in this session, but it is not required.

Workshop Preparation

A working copy of RStudio is required.

Facilitator Bio

Sahar is a PhD candidate in the Health Research Methodology program at McMaster University with a background in midwifery. She supports researchers in data analysis using statistical software such as R, SAS, and SPSS, research methodology, and evidence synthesis.

Workshop Slides

Coming soon.

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