A simple list of my tutorials hosted on GitHub

Demonstrates that almost all of the stats 101 tests are special cases of (simple) linear models, including “non-parametric” tests to some approximation. To establish this, I simulated the rank-correspondence between parametric tests and non-parametric equivalents:

- Kruskal-Wallis is (almost) a one-way ANOVA on ranked data
- Mann-Whitney is (almost) an independent-sample t-test on ranks
- Spearman is (almost) a Pearson on ranks
- Wilcoxon is (almost) a one-sample t-test on signed ranks

A simple and scalable three-step procedure to apply utility theory to regression.

A cheat sheet and a bunch of shiny applets to show various reaction-time friendly distributions and how to fit them using brms. I installed a Shiny server on Google Cloud to get it running.