Full course description
Level: Intermediate
Intermediate courses focus on applying a new analytic tool or framework within the topic area.
Track: Analyst track
Courses targeted to analysts focus learning new approaches for managing and analyzing data in statistical software such as R or SAS, or learning version control procedures using git.
Intended Audience: This course is targeted to individuals with introductory-level training in statistics. Knowledge of
simple linear regression is preferred, as well as previous experience with R.
Overview: This course provides an overview of five Generalized Linear Models (GLMs) commonly used to analyze public health data including linear, logistic, log-binomial, Poisson, and negative binomial regression. We review the key measures of association that are estimated using these models and discuss when to use each GLM. Through interactive examples, you’ll see how these models can help to answer causal questions, allowing you to draw valuable insights from your data that can inform public health practice. We will also arm you with the R code you need to confidently apply these methods to your own projects.
Time commitment: There are 6 total hours of course materials (4 hours of lecture materials and an 1-2 hour coding activity).
Certificate: Upon completion of all videos, quizzes and activities, participants will be awarded a certificate of completion.