Sas For Linear Models Fourth Edition

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Sas For Linear Models Fourth Edition

Sas For Linear Models Fourth Edition
Author: Ramon C. Littell, Ph.D.
Publisher: SAS Institute
ISBN: 9781599941424
Size: 75.10 MB
Format: PDF
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This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, authors Ramon Littell, Walter Stroup, and Rudolf Freund lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the appropriate SAS procedure for most experiment designs (including completely random, randomized blocks, and split plot) as well as factorial treatment designs and repeated measures. SAS for Linear Models, Fourth Edition, also includes analysis of covariance, multivariate linear models, and generalized linear models for non-normal data. Find inside: regression models; balanced ANOVA with both fixed- and random-effects models; unbalanced data with both fixed- and random-effects models; covariance models; generalized linear models; multivariate models; and repeated measures. New in this edition: MIXED and GENMOD procedures, updated examples, new software-related features, and other new material. This book is part of the SAS Press program.
SAS for Linear Models, Fourth Edition
Language: en
Pages: 492
Authors: Ramon C. Littell, Ph.D., Walter W. Stroup, Ph.D., Rudolf J. Freund, Ph.D.
Categories: Mathematics
Type: BOOK - Published: 2002-03-22 - Publisher: SAS Institute
This clear and comprehensive guide provides everything you need for powerful linear model analysis. Using a tutorial approach and plenty of examples, authors Ramon Littell, Walter Stroup, and Rudolf Freund lead you through methods related to analysis of variance with fixed and random effects. You will learn to use the
SAS for Linear Models
Language: en
Pages: 466
Authors: Ramon C. Littell, Walter Whitney Stroup, Rudolf Jakob Freund
Categories: Computers
Type: BOOK - Published: 2002 - Publisher: SAS Press
This clear and comprehensive guide provides everything needed for powerful linear model analysis. Using a tutorial approach and plenty of examples, the authors lead through methods related to analysis of variance with fixed and random effects. New in this edition: MIXED and GENMOD procedures, updated examples and new software-related features.
SAS System for Linear Models, 4e + Linear Models in Statistics, 2e Set
Language: en
Pages: 1184
Authors: Ramon Littell, Walter W. Stroup, Rudolf Freund, Alvin C. Rencher, G. Bruce Schaalje
Categories: Mathematics
Type: BOOK - Published: 2008-03-14 - Publisher: Wiley-Interscience
This set contains: 9780471221746 SAS for Linear Models, Fourth Edition by Ramon Littell, Walter W. Stroup, Rudolf Freund and 9780471754985 Linear Models in Statistics, Second Edition by Alvin C. Rencher, G. Bruce Shaalje.
SAS for Mixed Models
Language: en
Pages: 814
Authors: Ramon C. Littell
Categories: Computers
Type: BOOK - Published: 2006 - Publisher: SAS Press
This indispensable guide to mixed models using SAS is completely revised and updated for SAS 9. Discover the latest capabilities available for a variety of applications featuring the MIXED, GLIMMIX, and NLMIXED procedures.
SAS for Mixed Models, Second Edition
Language: en
Pages: 828
Authors: Ramon C. Littell, Ph.D., George A. Milliken, Ph.D., Walter W. Stroup, Ph.D., Russell D. Wolfinger, Ph.D., Oliver Schabenberger, Ph.D.
Categories: Mathematics
Type: BOOK - Published: 2007-06-25 - Publisher: SAS Institute
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 SAS for Mixed Models, Second Edition, the comprehensive mixed models guide for data analysis, completely revised and updated for SAS 9 by authors
SAS System for Mixed Models
Language: en
Pages: 633
Authors: Ramon C. Littell, George A. Milliken, Walter W. Stroup, Russell D. Wolfinger
Categories: Computers
Type: BOOK - Published: 1996 - Publisher: SAS Institute
At last! A comprehensive, applications-oriented mixed models guide for data analysis. Discover the latest capabilities available for a wide range of applications featuring the MIXED procedure in SAS/STAT software.
SAS for Mixed Models
Language: en
Pages: 608
Authors: Walter W. Stroup, George A. Milliken, Elizabeth A. Claassen, Russell D. Wolfinger
Categories: Computers
Type: BOOK - Published: 2018-12-12 - Publisher: SAS Institute
Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for
SAS System for Regression
Language: en
Pages: 264
Authors: Rudolf J. Freud, Ph.D., Ramon C. Littell, Ph.D.
Categories: Computers
Type: BOOK - Published: 2000-10 - Publisher: SAS Institute
Learn to perform a wide variety of regression analyses using SAS software with this example-driven favorite from SAS Publishing. With SAS System for Regression, Third Edition, you will learn the basics of performing regression analyses using a wide variety of models including nonlinear models. Other topics include performing linear regression
Applied Linear Models with SAS
Language: en
Pages:
Authors: Daniel Zelterman
Categories: Medical
Type: BOOK - Published: 2010-05-10 - Publisher: Cambridge University Press
This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues
Categorical Data Analysis Using the SAS System
Language: en
Pages: 648
Authors: Maura E. Stokes, Charles S. Davis, Gary G. Koch
Categories: Computers
Type: BOOK - Published: 2000 - Publisher: SAS Institute
Discusses hypothesis testing strategies for the assessment of association in contingency tables and sets of contingency tables. Also discusses various modeling strategies available for describing the nature of the association between a categorical outcome measure and a set of explanatory variables.