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Modeling count data

Hilbe, Joseph M., (1944-)

Modeling count data

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modeling count data/ Joseph M. Hilbe.
作者:	Hilbe, Joseph M.,
出版者:	Cambridge :Cambridge University Press, : 2014.,
面頁冊數:	xv, 283 p. :ill., digital ; : 24 cm.;
提要註:	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.
標題:	Linear models (Statistics) -
電子資源:	https://doi.org/10.1017/CBO9781139236065
ISBN:	9781139236065

Modeling count data
Hilbe, Joseph M.,1944-

Modeling count data[electronic resource] /Joseph M. Hilbe. - Cambridge :Cambridge University Press,2014. - xv, 283 p. :ill., digital ;24 cm.

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.

ISBN: 9781139236065Subjects--Topical Terms:

236283
Linear models (Statistics)

LC Class. No.: QA278 / .H56 2014

Dewey Class. No.: 519.535

Modeling count data
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505 8 $a 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.
520 $a 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.
650 0 $2 96060 $a Linear models (Statistics) $3 236283
650 0 $2 96060 $a Statistics. $3 219456
650 0 $2 96060 $a Multivariate analysis. $3 231740
856 4 0 $u https://doi.org/10.1017/CBO9781139236065

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