» SAS for Mixed Models, Second Edition

Price: $71.97

Rating: 4.5 / 5.00 (3 reviews)

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Manufacturer: SAS Publishing

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SAS for Mixed Models, Second Edition Details

Binding: Paperback
Dewey Decimal Number: 005.55
EAN: 9781590475003
ISBN: 1590475003
Label: SAS Publishing
Manufacturer: SAS Publishing
Number Of Items: 1
Number Of Pages: 840
Publication Date: 2006-02-21
Publisher: SAS Publishing
Studio: SAS Publishing

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SAS for Mixed Models, Second Edition Reviews

Customer Rating: Average rating of 4/5

Summary: review
Comment: Positive: 1.Well written in a field with limited sources.
Negative: 2. Not enough explanations for a lot of procedures.
2. Expensive

Customer Rating: Average rating of 5/5

Summary: good
Comment: A pretty good book which is very suitable for some people who needs to use the mixed model. The content of this version is better than the previous version. I recommend this book.

Customer Rating: Average rating of 5/5

Summary: A neccesary book for mixed model analysis
Comment: I am a Ph.D. student, and I work with longitudinal and hierarchical data. I bought this book recently and I have only had a look at it. I like it very much. This book presents mixed model methodology in the setting of numerous applications and many examples are included from several applications areas. It is a useful book even to people with no muck knowledge of analysis of variance an regression analysis. You do not need to read the book from cover to cover. This book is certainly applied.

Editorial Review for SAS for Mixed Models, Second Edition:

The indispensable, up-to-date guide to mixed models using SAS®. Discover the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures in this valuable edition of the comprehensive mixed models guide for data analysis, completely revised and updated for SAS®9. The theory underlying the models, the forms of the models for various applications, and a wealth of examples from different fields of study are integrated in the discussions of these models:
random effect only and random coefficients models
split-plot, multilocation, and repeated measures models
hierarchical models with nested random effects
analysis of covariance models
spatial correlation models
generalized linear mixed models
nonlinear mixed models

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