Modeling count data /
Hilbe, Joseph M., 1944-
Modeling count data / Joseph M. Hilbe, Arizona State University and Jet Propulsion Laboratory, California Institute of Technology. - 1 online resource (xv, 283 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Machine generated contents note: 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.
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.
9781139236065 (ebook)
Multivariate analysis.
Statistics.
Linear models (Statistics)
QA278 / .H56 2014
519.5/35
Modeling count data / Joseph M. Hilbe, Arizona State University and Jet Propulsion Laboratory, California Institute of Technology. - 1 online resource (xv, 283 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Machine generated contents note: 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.
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.
9781139236065 (ebook)
Multivariate analysis.
Statistics.
Linear models (Statistics)
QA278 / .H56 2014
519.5/35
