MOOC Applied Logistic Regression

About the course

This Applied Logistic Regression course provides theoretical and practical training for epidemiologists, biostatisticians and professionals of related disciplines in statistical modeling with particular emphasis on logistic regression.

The increasingly popular logistic regression model has become the standard method for regression analysis of binary response data in the health sciences.

By the end of this course, students should

  • Master methods of statistical modeling when the response variable is binary.
  • Be confident users of the Stata package for computing binary logistic regression models.

This is a hands-on, applied course where students will become proficient at using computer software to analyze data drawn primarily from the fields of medicine, epidemiology and public health.

There will be many practical examples and homework exercises in this class to help you learn. If you fully apply yourself in this course and complete all of the homework, you will have the opportunity to master various methods of statistical modeling and you will become a more confident user of the Stata* package for computing linear, polynomial and multiple regression.

*Access to Stata will be provided at no cost for the duration of this course.

Course team

Prof. Stanley Lemeshow is Founding Dean of the Ohio State University’s College of Public Health and served in that capacity for 10 years (between 2003 and 2013). He has been with the University since 1999 as a biostatistics professor in the School of Public Health and the Department of Statistics, director of the biostatistics core of the Comprehensive Cancer Center and Director of the University’s Center for Biostatistics. His biostatistics research includes statistical modeling of medical data, sampling, health disparities and cancer prevention.


Eight-week course. Each sequence will contain exercises and literature references.

Course plan

Week 1: Introduction to logistic regression

Week 2: Likelihood ratio and Wald test

Week 3: Coefficient interpretation

Week 4: Polynomous variables

Week 5: Interaction and confounding effects

Week 6: Stratified analysis

Week 7: Quality of adjustment and calibration

Week 8: Assessing the fit of the model


Epidemiologists, biostatisticians, and other professionals working in fields using statistical modeling.

Economics & Finance, Statistics & Data Analysis

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