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Charles E. Mcculloch - Generalized, Linear, and Mixed Models - 9780470073711 - V9780470073711
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Generalized, Linear, and Mixed Models

€ 198.64
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Description for Generalized, Linear, and Mixed Models Hardcover. - This new edition presents a unified, accessible, and self-contained treatment of the latest statistical methods for analyzing correlated, non-normally distributed data. - The book's unified treatment addresses the needs of applications-oriented users of statistical packages and also graduate students in statistics. Series: Wiley Series in Probability and Statistics. Num Pages: 424 pages, ill. BIC Classification: PBWH. Category: (P) Professional & Vocational. Dimension: 241 x 159 x 30. Weight in Grams: 774.
An accessible and self-contained introduction to statistical models-now in a modernized new edition

Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.

A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models ... Read more

Product Details

Format
Hardback
Publication date
2008
Publisher
John Wiley and Sons Ltd United Kingdom
Number of pages
424
Condition
New
Series
Wiley Series in Probability and Statistics
Number of Pages
424
Place of Publication
, United States
ISBN
9780470073711
SKU
V9780470073711
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50

About Charles E. Mcculloch
Charles E. McCulloch, PhD, is Professor and Head of the Division of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association, Dr. McCulloch is the author of numerous published articles in the areas of longitudinal data analysis, generalized linear mixed models, and latent class models and their applications. ... Read more

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