Statistical Reasoning for Public Health 2: Regression Methods

A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.

About The Course

Regression methods are paramount for expanding the scope of analyses beyond the tools covered in Statistical Reasoning for Public Health 1.  In this course, a conceptual framework is employed to highlight the similarities between linear, logistic and Cox Proportion Hazards methods, while also focusing on the differences, in terms of usage and the interpretations of results from such models.  The course will start by laying out the details for all the regression approaches in the “simple” scenario, involving relating an outcome to single predictor.  After this overview of simple regression, confounding and effect-modification will be compared and contrasted, and adjusted and stratum-specific estimates will be introduced. Finally, multiple-regression models will then be debuted, and will be used to assess confounding and effect modification, produce adjusted and stratum-specific estimates, and to allow for better prediction of an outcome via the use of multiple predictors.  Linear spline models and propensity score methods for adjustment will also be briefly introduced.  All of the topics covered in this course will be reinforced with multiple examples from current and classical public health and medical studies.

Frequently Asked Questions

  • Will I get a Statement of Accomplishment after completing this class?

    Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.

  • What resources will I need for this class?

    For this course, all you need is an Internet connection and a willingness to challenge yourself.

Recommended Background

Statistical Reasoning 1: Estimation, Inference, and Interpretation