Modeling Count Data

TLDR

The book provides clear guidelines for selecting, constructing, interpreting, and evaluating count data models. It introduces count data modeling from Poisson fundamentals through overdispersion and negative binomial models, and explores numerous variations, supported by tables, insets, and detailed suggestions for novice researchers. The text includes Stata, R, and SAS code examples that allow readers to adapt models for applications in health, ecology, econometrics, transportation, and other fields.

Abstract

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.