Interested in learning how logistic regression can be used to predict outcomes and uncover relationships in data? Logistic regression is one of the most widely used statistical and machine learning techniques for modeling binary outcomes and supporting evidence-based decision-making. Through live demonstrations and guided exercises in R, participants will gain practical experience building, evaluating, and interpreting logistic regression models.

This hands-on workshop introduces the complete logistic regression workflow, from data preparation and model development to performance assessment and result interpretation. Whether you are new to predictive modeling or looking to strengthen your analytical skills, this session will provide a practical foundation for applying logistic regression in research and data analysis.

This session is ideal for researchers, students, and analysts interested in applying predictive modeling techniques to their data. Basic familiarity with R and statistical concepts is recommended.

By the end of this session, participants will be able to:

1. Prepare and preprocess data for logistic regression modeling in R.
2. Develop and train logistic regression models for binary classification tasks.
3. Evaluate model performance using discrimination metrics, including the C-statistic (AUC) and ROC curves, as well as calibration measures.
4. Interpret model coefficients, predicted probabilities, and the relative importance of predictor variables.
5. Communicate model results and key findings effectively to research and non-technical audiences.

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.

Certificate Eligibility: This workshop is eligible for the Sherman Centre's certificate program. For more information, visit scds.ca/certificate-program. It is also eligible for the Canadian Certificate for Digital Humanities. To learn more, visit ccdhhn.ca or contact scds@mcmaster.ca.