02301cam a2200313 i 4500
18194001
20160518150143.0
140619s2014 nyua b 001 0 eng
2014021778
9781107611252 (pb)
DLC
eng
DLC
rda
DLC
pcc
QA278
.H56 2014
519.535 HIL/Mod
23
MAT029000
bisacsh
Hilbe, Joseph M.
Modeling count data
Joseph M. Hilbe, Arizona State University and Jet Propulsion Laboratory, California Institute of Technology.
Delhi:
Cambridge University Press;
2014
xv, 283 pages :
illustrations ;
25 cm
Includes bibliographical references and index.
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 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"--
Multivariate analysis.
Statistics.
Linear models (Statistics)
MATHEMATICS / Probability & Statistics / General.
bisacsh
7
cbc
orignew
1
ecip
20
y-gencatlg
0
0
0
519_535000000000000_HILMOD
0
157038
GUL
GUL
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2016-05-18
1
1
519.535 HIL/Mod
161296
2017-03-15 00:00:00
2017-02-06
2016-05-18
BK
rm15 2014-06-19
rm15 2014-06-19 ONIX
rm15 2014-06-19 (telework)
rm15 2014-07-08 to Dewey
rl00 2014-09-29 to SMA
xn08 2014-09-29 1 copy rec'd., to CIP ver.
120445
120445