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:
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Understand foundational concepts of logistic regression
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Learn how to structure and prepare data for logistic regression analysis
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Use the glm() function in R to fit multiple logistic regression models
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Interpret model coefficients, including odds ratios
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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.