This GitHub repository contains the course materials and code for the 2024 edition of the “Statistical Rethinking” course taught by Richard McElreath, focusing on Bayesian data analysis using R and Stan.

ROUGH NOTES: [let me know if you spot any errors– there might be a couple!] Often, in randomized control trial where individuals are randomly assigned to treatment and control conditions, covariates are included to improve precision by reducing error and improving statistical power. However, when binary outcomes are used (e.g., patient recovers or not), there are several additional concerns that have gone unnoticed by many applied researchers.Take a simulated example where our true model data generating process is (to keep things simple, the intercept is zero and the parameters are both set to 1):

The focus of this document is on data science tools and techniques in R, including basic programming knowledge, visualization practices, modeling, and more, along with exercises to practice further. In addition, the demonstrations of most content in Python is available via Jupyter notebooks.

R is an open source implementation of the S-Plus language. It is freely available and is licensed under the GPL. The language simplifies many statistical computations and can be a powerful tool.