Modeling count data
"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 linear regression and works up to an analysis of the Poisson and negative binomial models, and to the problem of overdispersion. Example…
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- Full title :Modeling count data / Joseph M. Hilbe.
Summary
"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 linear regression and works up to an analysis of the Poisson and negative binomial models, and to the problem of overdispersion. 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 public health, ecology, econometrics, transportation, and other related fields"--Del editor.
Read More ↓Edition Statements
- Publication Date:©2014
Publication Statements
- Publisher(s):Cambridge University Press
- Place of Publication:New York, NY
- Language:eng
- Publication Date:©2014
The Physical Object
- Description:xv, 283 páginas : : ilustraciones, gráficas ; : 24 cm
Edition Identifiers
- ISBN:9781107028333, 1107028337, 9781107611252, 1107611253
- oclc:ocn900266476
- LC Classification:QA278
- Dewey Decimal:519.535
Table of Contents
Preface -- 1. Varieties of count data -- 2. Poisson regression -- 3. Testing overdispersion -- 4. Assessment of fit -- 5. Negative binomial regression -- 6. Poisson inverse Gaussian regression -- 7. Problems with zeros -- 8. Modeling under-dispersed count data -- generalized Poisson -- 9. Complex data: more advanced models -- Appendix A: SAS code -- References -- Index
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