SAS for Mixed Models, Second Edition

Author: Ramon C. Littell, Ph.D.,George A. Milliken, Ph.D.,Walter W. Stroup, Ph.D.,Russell D. Wolfinger, Ph.D.,Oliver Schabenberger, Ph.D.

Publisher: SAS Institute

ISBN: 9781599940786

Category: Mathematics

Page: 828

View: 6571

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 Ramon Littell, George Milliken, Walter Stroup, Russell Wolfinger, and Oliver Schabenberger. 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; and nonlinear mixed models. Professionals and students with a background in two-way ANOVA and regression and a basic knowledge of linear models and matrix algebra will benefit from the topics covered. This book is part of the SAS Press program.

SAS for Mixed Models

Introduction and Basic Applications

Author: Walter W. Stroup, PhD,George A. Milliken, PhD,Elizabeth A. Claassen, PhD,Russell D. Wolfinger, PhD

Publisher: SAS Institute

ISBN: 163526152X

Category: Computers

Page: 608

View: 2327

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.

JMP 13 Fitting Linear Models, Second Edition

Author: SAS

Publisher: SAS Institute

ISBN: 1629609528

Category: Computers

Page: 528

View: 8585

JMP 13 Fitting Linear Models focuses on the Fit Model platform and many of its personalities. Linear and logistic regression, analysis of variance and covariance, and stepwise procedures are covered. Also included are multivariate analysis of variance, mixed models, generalized models, and models based on penalized regression techniques.

Common Statistical Methods for Clinical Research with SAS Examples

Author: Glenn A. Walker,Jack Shostak

Publisher: SAS Institute

ISBN: 1607644258

Category: Computers

Page: 552

View: 8299

Thoroughly updated edition of the popular introductory statistics book for clinical researchers. This new edition has been extensively updated to include the use of ODS graphics in numerous examples as well as a new emphasis on PROC MIXED.

Applied Mixed Models in Medicine

Author: Helen Brown,Robin Prescott

Publisher: John Wiley & Sons

ISBN: 1118778235

Category: Medical

Page: 536

View: 7643

A fully updated edition of this key text on mixed models,focusing on applications in medical research The application of mixed models is an increasingly popular wayof analysing medical data, particularly in the pharmaceuticalindustry. A mixed model allows the incorporation of both fixed andrandom variables within a statistical analysis, enabling efficientinferences and more information to be gained from the data. Therehave been many recent advances in mixed modelling, particularlyregarding the software and applications. This third edition ofBrown and Prescott’s groundbreaking text provides an updateon the latest developments, and includes guidance on the use ofcurrent SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixedmodels in medical research, including the latest developments andnew sections on incomplete block designs and the analysis ofbilateral data. Easily accessible to practitioners in any area where mixedmodels are used, including medical statisticians andeconomists. Includes numerous examples using real data from medical andhealth research, and epidemiology, illustrated with SAS code andoutput. Features the new version of SAS, including new graphics formodel diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, andfurther material. This third edition will appeal to applied statisticians workingin medical research and the pharmaceutical industry, as well asteachers and students of statistics courses in mixed models. Thebook will also be of great value to a broad range of scientists,particularly those working in the medical and pharmaceuticalareas.

SAS System for Mixed Models

Author: Ramon C. Littell,George A. Milliken,Walter W. Stroup,Russell D. Wolfinger

Publisher: SAS Institute

ISBN: 9781555447793

Category: Computers

Page: 633

View: 5186

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.

Logistic Regression Using SAS

Theory and Application, Second Edition

Author: Paul D. Allison

Publisher: SAS Institute

ISBN: 1607649950

Category: Mathematics

Page: 348

View: 2319

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models). This book is part of the SAS Press program.

Linear Mixed Models

A Practical Guide Using Statistical Software, Second Edition

Author: Brady T. West,Kathleen B. Welch,Andrzej T Galecki

Publisher: CRC Press

ISBN: 1466560991

Category: Mathematics

Page: 440

View: 1868

Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM. New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations Use of the lmer() function in the lme4 R package New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs Updated graphical procedures in the software packages Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options More practical recommendations on using the software for analysis A new R package (WWGbook) that contains all of the data sets used in the examples Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.

SAS for Linear Models, Fourth Edition

Author: Ramon C. Littell, Ph.D.,Walter W. Stroup, Ph.D.,Rudolf J. Freund, Ph.D.

Publisher: SAS Institute

ISBN: 9781599941424

Category: Mathematics

Page: 492

View: 8456

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.

Introductory Probability and Statistics

Applications for Forestry and Natural Sciences

Author: N.A

Publisher: CABI

ISBN: 1845932757

Category: Nature

Page: 408

View: 5710

With interest growing in areas of forestry, conservation and other natural sciences, the need to organize and tabulate large amounts of forestry and natural science information has become a necessary skill. Previous attempts of applying statistical methods to these areas tend to be over-specialized and of limited use; an elementary text using methods, examples and exercises that are relevant to forestry and the natural sciences is long overdue. This book utilizes basic descriptive statistics and probability, as well as commonly used statistical inferential tools to introduce topics that are commonplace in a forestry context such as hypothesis texting, design of experiments, sampling methods, nonparametric tests and statistical quality control. It also contains examples and exercises drawn from the fields of forestry, wood science, and conservation.


Sozialismus, aus der Froschperspektive betrachtet auch: aus dem Leben eines real existierenden Taugenichts

Author: Judith Hodosi

Publisher: N.A


Category: Biography & Autobiography

Page: 319

View: 9528

Generalized Linear Mixed Models

Modern Concepts, Methods and Applications

Author: Walter W. Stroup

Publisher: CRC Press

ISBN: 1439815127

Category: Mathematics

Page: 555

View: 3008

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) 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. Along with describing common applications of GLMMs, the text introduces the essential theory and main methodology associated with linear models that accommodate random model effects and non-Gaussian data. Unlike traditional linear model textbooks that focus on normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed and effects may be fixed or random. With numerous examples using SAS® PROC GLIMMIX, this book is ideal for graduate students in statistics, statistics professionals seeking to update their knowledge, and researchers new to the generalized linear model thought process. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling. See Professor Stroup discuss the book.

Analysis of Messy Data Volume 1

Designed Experiments, Second Edition

Author: George A. Milliken,Dallas E. Johnson

Publisher: CRC Press

ISBN: 9781420010152

Category: Mathematics

Page: 674

View: 7966

A bestseller for nearly 25 years, Analysis of Messy Data, Volume 1: Designed Experiments helps applied statisticians and researchers analyze the kinds of data sets encountered in the real world. Written by two long-time researchers and professors, this second edition has been fully updated to reflect the many developments that have occurred since the original publication. New to the Second Edition Several modern suggestions for multiple comparison procedures Additional examples of split-plot designs and repeated measures designs The use of SAS-GLM to analyze an effects model The use of SAS-MIXED to analyze data in random effects experiments, mixed model experiments, and repeated measures experiments The book explores various techniques for multiple comparison procedures, random effects models, mixed models, split-plot experiments, and repeated measures designs. The authors implement the techniques using several statistical software packages and emphasize the distinction between design structure and the structure of treatments. They introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study. Bringing a classic work up to date, this edition will continue to show readers how to effectively analyze real-world, nonstandard data sets.

SAS System for Elementary Statistical Analysis

Author: Sandra D. Schlotzhauer,Ramon C. Littell

Publisher: SAS Institute

ISBN: 9781580250184

Category: Computers

Page: 440

View: 6294

This updated edition shows how to use SAS to perform basic statistical analysis. General topics include creating a data set with SAS; summarizing data with descriptive statistics, frequency tables, and bar charts; comparing groups (t-tests, one-way ANOVA, and nonparametric analogues); performing basic linear regression (lines, curves, and two-variable models); performing simple regression diagnostics (residuals plots, studentized residuals); and creating and analyzing tables of data. Using real-life examples, this beginner's guide bridges the gap between statistics texts and SAS documentation.

Ökonometrie für Dummies

Author: Roberto Pedace

Publisher: John Wiley & Sons

ISBN: 3527801529

Category: Business & Economics

Page: 388

View: 660

Theorien verstehen und Techniken anwenden Was haben die Gehälter von Spitzensportlern und der Mindestlohn gemeinsam? Richtig, man kann sie mit Ökonometrie erforschen. Im Buch steht, wie es geht. Und nicht nur dafür, sondern für viele weitere Gebiete lohnt es sich, der zunächst etwas trocken und sperrig anmutenden Materie eine Chance zu geben. Lernen Sie von den Autoren, wie Sie spannende Fragen formulieren, passende Variablen festlegen, treffsichere Modelle entwerfen und Ihre Aussagen auf Herz und Nieren prüfen. Werden Sie sicher im Umgang mit Hypothesentests, Regressionsmodellen, Logit- & Probit-Modellen und allen weiteren gängigen Methoden der Ökonometrie. So begleitet Ökonometrie für Dummies Sie Schritt für Schritt und mit vielen Beispielen samt R Output durch dieses spannende Thema.

Univariate and Multivariate General Linear Models

Theory and Applications with SAS, Second Edition

Author: Kevin Kim,Neil Timm

Publisher: CRC Press

ISBN: 1420011367

Category: Mathematics

Page: 549

View: 1059

Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences. With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis, and multivariate GLMs that cover multivariate regression analysis, MANOVA, MANCOVA, and repeated measurement data analyses. The book also analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New to the Second Edition Two chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedure Expanded theory of unrestricted general linear, multivariate general linear, SUR, and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithm Applications of MI, MIANALYZE, TRANSREG, and CALIS procedures A practical introduction to GLMs, Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework.

Analysis of Biomarker Data

A Practical Guide

Author: Stephen W. Looney,Joseph L. Hagan

Publisher: John Wiley & Sons

ISBN: 1118552458

Category: Social Science

Page: 424

View: 4991

A “how to” guide for applying statistical methods to biomarker data analysis Presenting a solid foundation for the statistical methods that are used to analyze biomarker data, Analysis of Biomarker Data: A Practical Guide features preferred techniques for biomarker validation. The authors provide descriptions of select elementary statistical methods that are traditionally used to analyze biomarker data with a focus on the proper application of each method, including necessary assumptions, software recommendations, and proper interpretation of computer output. In addition, the book discusses frequently encountered challenges in analyzing biomarker data and how to deal with them, methods for the quality assessment of biomarkers, and biomarker study designs. Covering a broad range of statistical methods that have been used to analyze biomarker data in published research studies, Analysis of Biomarker Data: A Practical Guide also features: A greater emphasis on the application of methods as opposed to the underlying statistical and mathematical theory The use of SAS®, R, and other software throughout to illustrate the presented calculations for each example Numerous exercises based on real-world data as well as solutions to the problems to aid in reader comprehension The principles of good research study design and the methods for assessing the quality of a newly proposed biomarker A companion website that includes a software appendix with multiple types of software and complete data sets from the book’s examples Analysis of Biomarker Data: A Practical Guide is an ideal upper-undergraduate and graduate-level textbook for courses in the biological or environmental sciences. An excellent reference for statisticians who routinely analyze and interpret biomarker data, the book is also useful for researchers who wish to perform their own analyses of biomarker data, such as toxicologists, pharmacologists, epidemiologists, environmental and clinical laboratory scientists, and other professionals in the health and environmental sciences.