Modeling count data
Hilbe, Joseph M.
creator
text
bibliography
nyu
Delhi
Cambridge University Press
2014
monographic
eng
xv, 283 pages : illustrations ; 25 cm
"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"--
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.
Joseph M. Hilbe, Arizona State University and Jet Propulsion Laboratory, California Institute of Technology.
Includes bibliographical references and index.
Multivariate analysis
Statistics
Linear models (Statistics)
MATHEMATICS / Probability & Statistics / General
QA278 .H56 2014
519.535 HIL/Mod
MAT029000
9781107611252 (pb)
2014021778
DLC
140619
20160518150143.0
18194001
eng