Bayesian Regression Modeling with rstanarm

Yes, upon completing the course, you will receive a certificate of completion.

This course covers a variety of topics related to Bayesian regression and the rstanarm package. This includes reviewing frequentist regression and establishing core principles in the Bayesian framework, modifying a Bayesian model, assessing model fit, and presenting and using a Bayesian regression.

This course is useful for anyone interested in developing a deeper understanding of Bayesian regression, especially data scientists, statisticians, analysts, and software developers.

For this course, you must be familiar with linear regression, basic probability, and the R programming language. Additionally, a working knowledge of the ggplot2 and tidyverse packages is beneficial but not required.

This course should take approximately 4 hours to complete

This course focuses on the rstanarm package, so it is recommended that you use this package for the exercises. However, the concepts and techniques learned in this course should be applicable to other software packages as well.

Bayesian modeling is an alternative to frequentist modeling, which is heavily focused on p-values and hypothesis testing. With Bayesian modeling, uncertainty is expressed in the form of a probability distribution and parameters are expressed in terms of the expected values of their posterior distributions.