Subject
5 entries
Regression
Bookmarks
Regression and Other Stories
Regression and Other Stories by Gelman, Hill, and Vehtari is a practical statistics textbook covering regression modeling from basics through causal inference — grounded in real data examples and the Bayesian workflow. The modern standard for applied regression.
An Introduction to Statistical Learning (2nd Edition)
The second edition of James, Witten, Hastie, and Tibshirani's canonical intro-level statistical learning textbook, updated in 2021 to include deep learning, survival analysis, and multiple testing. It sits between undergraduate statistics and the more demanding Elements of Statistical Learning — the best entry point for practitioners who want rigorous but accessible ML foundations.
Why I'm Not a Fan of R-Squared
John Myles White's argument that R-squared is a misleading metric for regression model quality — it conflates the variance of x with model fit and can be gamed. A clean critique that every data scientist who uses linear regression should read.
10 Types of Regressions. Which One to Use?
A reference guide to 10 regression types and when to use each — covering linear, logistic, ridge, lasso, polynomial, and more. The kind of practical decision map that was useful before sklearn docs became as thorough as they are now.
Bayesian Regression with PyMC: A Brief Tutorial
A Zipfian Academy student's tutorial on Bayesian linear regression using PyMC — notable both as an accessible introduction to probabilistic modeling and as a window into the Zipfian cohort's learning culture of public writing. PyMC was the dominant Python tool for Bayesian modeling at the time.