SAS for Mixed Models

SAS for Mixed Models PDF Author: Walter W. Stroup
Publisher: SAS Institute
ISBN: 163526152X
Category : Computers
Languages : en
Pages : 608

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Book Description
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 Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.

SAS for Mixed Models

SAS for Mixed Models PDF Author: Walter W. Stroup
Publisher: SAS Institute
ISBN: 163526152X
Category : Computers
Languages : en
Pages : 608

Get Book Here

Book Description
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 Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.

Mixed Models Analyses Using the SAS System

Mixed Models Analyses Using the SAS System PDF Author: Jill Tao
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 538

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Book Description
This course is designed for data analysts, experimental designers, and researchers with sound statistical knowledge who want to learn how to analyze linear and nonlinear mixed models using the MIXED and NLMIXED procedures, respectively. Applications in several areas are presented, such as the semiconductor, agricultural, pharmaceutical, educational, and environmental industries.

SAS for Mixed Models

SAS for Mixed Models PDF Author: Ramon C. Littell
Publisher: SAS Press
ISBN: 9781590475003
Category : Computers
Languages : en
Pages : 814

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Book Description
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 System for Mixed Models

SAS System for Mixed Models PDF Author: Ramon C Littell
Publisher:
ISBN:
Category :
Languages : en
Pages : 633

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Book Description


Generalized Linear Mixed Models

Generalized Linear Mixed Models PDF Author: Walter W. Stroup
Publisher: CRC Press
ISBN: 1439815135
Category : Mathematics
Languages : en
Pages : 547

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Book Description
With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.

JMP for Mixed Models

JMP for Mixed Models PDF Author: Ruth Hummel
Publisher: SAS Institute
ISBN: 1952363853
Category : Computers
Languages : en
Pages : 380

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Book Description
Discover the power of mixed models with JMP and JMP Pro. Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions. JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP’s groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code. Inside you’ll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include: Learning how to appropriately recognize, set up, and interpret fixed and random effects Extending analysis of variance (ANOVA) and linear regression to numerous mixed model designs Understanding how degrees of freedom work using Skeleton ANOVA Analyzing randomized block, split-plot, longitudinal, and repeated measures designs Introducing more advanced methods such as spatial covariance and generalized linear mixed models Simulating mixed models to assess power and other important sampling characteristics Providing a solid framework for understanding statistical modeling in general Improving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference

Linear Mixed Models for Longitudinal Data

Linear Mixed Models for Longitudinal Data PDF Author: Geert Verbeke
Publisher: Springer Science & Business Media
ISBN: 1441903003
Category : Mathematics
Languages : en
Pages : 579

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Book Description
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

Mixed Models Analyses Using the SAS System Course Notes

Mixed Models Analyses Using the SAS System Course Notes PDF Author: SAS Institute
Publisher: Sas Inst
ISBN: 9781599943411
Category : Computers
Languages : en
Pages :

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Book Description


Mixed Models Analyses Using the Sas System Course Notes

Mixed Models Analyses Using the Sas System Course Notes PDF Author: Jill Tao
Publisher: Sas Inst
ISBN: 9781590471081
Category : Computer science
Languages : en
Pages : 400

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Book Description


Linear Mixed Models

Linear Mixed Models PDF Author: Brady T. West
Publisher: CRC Press
ISBN: 1420010433
Category : Mathematics
Languages : en
Pages : 376

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Book Description
Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-nav