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Joseph M. Hilbe - Modeling Count Data, Häftad

Häftad, Engelska, 2014

Modeling Count Data

Av Joseph M. Hilbe

889 kr

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Beskrivning
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.
Produktinformation
  • Utgivningsdatum: 2014-07-21
  • Mått: 177 x 235 x 15 mm
  • Vikt: 510 g
  • Format: Häftad
  • Språk: Engelska
  • Antal sidor: 300
  • Förlag: Cambridge University Press
  • ISBN: 9781107611252
  • Utmärkelser: Short-listed for PROSE Award for Mathematics 2015
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Mer information

Joseph Hilbe is a solar system ambassador with NASA's Jet Propulsion Laboratory, California Institute of Technology; an Adjunct Professor of Statistics at Arizona State University; an Emeritus Professor at the University of Hawaii; and a statistical modeling instructor for Statistics.com, a web-based continuing-education program in statistics. He is the author of several books on statistical modeling and serves as the coordinating editor for the Cambridge University Press series Predictive Analytics in Action.

Recensioner i media

'This is a first-rate introductory book for modeling count data, a key challenge in applied statistics. Hilbe's experience and affability shine in the text. His careful emphasis on establishing the defensibility of models, for example, in the face of overdispersion, will greatly benefit the beginning statistician. His clear informal explanations of important and complicated statistical principles are invaluable.' Andrew Robinson, University of Melbourne

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