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. In this session, participants will:

• Understand foundational concepts of logistic regression

• Learn how to structure and prepare data for logistic regression analysis

• Use the glm() function in R to fit multiple logistic regression models

• Interpret model coefficients, including odds ratios

• Evaluate the performance and fit of logistic regression models

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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