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.

Details: Any preparatory work for the session can be found on its information page. This virtual workshop will be recorded and shared on the same page, and discoverable via the Sherman Centre's Online Learning Catalogue.

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.