Modeling Count Data

Nonfiction, Science & Nature, Mathematics, Statistics, Business & Finance
Cover of the book Modeling Count Data by Joseph M. Hilbe, Cambridge University Press
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Author: Joseph M. Hilbe ISBN: 9781139985369
Publisher: Cambridge University Press Publication: July 21, 2014
Imprint: Cambridge University Press Language: English
Author: Joseph M. Hilbe
ISBN: 9781139985369
Publisher: Cambridge University Press
Publication: July 21, 2014
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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