Christopher Baum's An Introduction to Stata Programming is worthwhile for anyone wanting to learn about programming in Stata. For the beginner, Baum assumes only that the user is familiar with Stata, and so he builds up accordingly. For the more advanced Stata programmer, the book introduces...

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions—a class so rich that it includes the commonly used logit, probit, and Poisson distributions. Although one can...

The Workflow of Data Analysis Using Stata, by J. Scott Long, is a productivity tool for data analysts. Long guides you toward streamlining your workflow, because a good workflow is essential for replicating your work, and replication is essential for good science. A workflow of data analysis is...

Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, by J. Scott Long and Jeremy Freese, shows how to fit and interpret regression models for categorical data with Stata. Nearly 50% longer than the previous edition, the book covers new topics for fitting and...

A Visual Guide to Stata Graphics gives you a detailed guide to Stata's graphics capabilities in an easy-to-use format. From beginning to end, author Michael Mitchell demonstrates through step-by-step examples the most efficient methods to create even the most complicated graphs. It will be the...

This text is a Stata-specific treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are "mixed" in the sense that they allow fixed and random effects and are "generalized" in the sense that they are appropriate not only for continuous...