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Applying Generalized Linear Models (Springer Texts in Statistics)
Stock Photo: Cover May Be Different

Applying Generalized Linear Models (Springer Texts in Statistics) Hardcover - 1997 - 1st Edition

by Lindsey, J. K

  • Used
  • Hardcover

Description

Springer, 1997. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9780387982182
Used: See description
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Details

  • Title Applying Generalized Linear Models (Springer Texts in Statistics)
  • Author Lindsey, J. K
  • Binding Hardcover
  • Edition number 1st
  • Edition 1
  • Pages 256
  • Volumes 1
  • Language ENG
  • Publisher Springer, New York
  • Date 1997
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 9910274
  • ISBN 9780387982182 / 0387982183
  • Weight 1.17 lbs (0.53 kg)
  • Dimensions 9.52 x 6.37 x 0.71 in (24.18 x 16.18 x 1.80 cm)
  • Library of Congress subjects Linear models (Statistics)
  • Library of Congress Catalog Number 97006926
  • Dewey Decimal Code 519.53

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From the publisher

Generalized linear models have applications in many areas, including social science and life science. This book will serve as a reference and advanced text for students interested in the applications of statistics.

First line

Models are abstract, simplified representations of reality, often used both in science and in technology.

From the rear cover

Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression, and ANOVA.

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