Download or read book Multiple Comparison Procedures written by Larry E. Toothaker and published by SAGE. This book was released on 1993 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you conduct research with more than two groups and want to find out if they are significantly different when compared two at a time, then you need Multiple Comparison Procedures. Using examples to illustrate major concepts, this concise volume is your guide to multiple comparisons. Toothaker thoroughly explains such essential issues as planned vs. post-hoc comparisons, stepwise vs. simultaneous test procedures, types of error rate, unequal sample sizes and variances, and interaction tests vs. cell mean tests.

Download or read book Multiple Comparisons for Researchers written by Larry E. Toothaker and published by SAGE Publications, Incorporated. This book was released on 1991-08-27 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through clear exposition and step-by-step procedures, Toothaker describes all the most important multiple comparison procedures along with relevant concepts, such as error rate, power, robustness and coverage of two-way ANOVA including the controversy on cell mean versus tests on interaction effects. The book also includes samples of multiple comparison programs in SAS and SPSS.

Download or read book Robust Multiple Comparison Procedures written by Susan E. White and published by . This book was released on 1991 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiple Comparison Procedures written by Yosef Hochberg and published by . This book was released on 1987-10-05 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a balanced, up-to-date view of multiple comparison procedures, this book refutes the belief held by some statisticians that such procedures have no place in data analysis. With equal emphasis on theory and applications, it establishes the advantages of multiple comparison techniques in reducing error rates and in ensuring the validity of statistical inferences. Provides detailed descriptions of the derivation and implementation of a variety of procedures, paying particular attention to classical approaches and confidence estimation procedures. Also discusses the benefits and drawbacks of other methods. Numerous examples and tables for implementing procedures are included, making this work both practical and informative.

Download or read book Multiple Comparisons for Researchers written by Larry E. Toothaker and published by . This book was released on with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through clear exposition and step-by-step procedures, Toothaker describes all the most important multiple comparison procedures along with relevant concepts, such as error rate, power, robustness and coverage of two-way ANOVA including the controversy on cell mean versus tests on interaction effects. The book also includes samples of multiple comparison programs in SAS and SPSS.

Download or read book Multiple Comparisons Using R written by Frank Bretz and published by CRC Press. This book was released on 2016-04-19 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes’ test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey’s all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques. Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa. See Dr. Bretz discuss the book.

Download or read book The robustness of Scheffe s Multiple Comparison Procedures in the Analysis of Variances written by W. Y. Tan and published by . This book was released on 1976* with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Some Results on Familywise Robustness for Multiple Comparison Procedures written by Ka Man Chan and published by . This book was released on 2005 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robustness and Power Consideration in the Tukey Multiple Comparison Procedure written by Donald G. MacGregor and published by . This book was released on 1975 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiple Comparison Procedures written by Martin A. Hamilton and published by . This book was released on 1965 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Experimental Design and Data Analysis for Biologists written by Gerald Peter Quinn and published by Cambridge University Press. This book was released on 2002-03-21 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression, analysis of variance, correlation, graphical.

Download or read book Introduction to Robust Estimation and Hypothesis Testing written by Rand R. Wilcox and published by Academic Press. This book was released on 2012-01-12 with total page 713 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--

Download or read book Statistical Analysis of Designed Experiments written by Ajit C. Tamhane and published by John Wiley & Sons. This book was released on 2012-09-12 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: A indispensable guide to understanding and designing modern experiments The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences. The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests. Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab® software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets. With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.

Download or read book Repeated Measures Multiple Comparison Procedures with a Mixed Model Analysis written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Nonparametric Statistical Methods written by Thomas P. Hettmansperger and published by CRC Press. This book was released on 2010-12-20 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It follows the approach of the first edition by developing rank-based m

Download or read book Repeated Measures Multiple Comparison Procedures with a Mixed Model Analysis written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: One approach to the analysis of repeated measures designs allows researchers to model the variance-covariance structure of their data rather than presume a certain structure as is the case with conventional univariate and multivariate test statistics (Littell, Milliken, Stroup, & Wolfinger, 1996). This mixed-model approach was evaluated for testing all possible pairwise differences among repeated measures marginal means in a between- by within-subjects design. Specifically, Type I error control and power were examined for simultaneous and stepwise multiple comparison procedures using SAS' (1996) PROC MIXED in an unbalanced repeated measures design when normality and variance covariance homogeneity assumptions did not hold. The potential advantage of the MIXED procedure with its ability to specify various variance-covariance structures was compared to known robust multiple comparison procedures based on a between-subjects heterogeneous unstructured form of the variance-covariance matrix with Satterthwaite (1941, 1946) degrees of freedom (Keselman, 1994; Keselman, Keselman, & Shaffer, 1991; Keselman & Lix, 1995). Specifically, the testing strategies of always fitting an unstructured variance-covariance matrix, fitting the true population structure, or allowing two model selection criteria available through PROC MIXED to select the best structure were investigated. Rates of Type I error control were similar across the testing strategies for each of the multiple comparison procedures. The recommendation of always fitting an unstructured variance-covariance matrix to the data was based on the fact that a researcher does not need prior knowledge about the true population structure and does not need to rely on a model selection criterion to provide good Type I error control. Furthermore, results showed two stepwise multiple comparison procedures as particularly notable. Shaffer's (1986) sequentially rejective Bonferroni and Hochberg's (1988) sequentially acceptive Bonferro.

Download or read book Multiple Testing Problems in Pharmaceutical Statistics written by Alex Dmitrienko and published by CRC Press. This book was released on 2009-12-08 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Useful Statistical Approaches for Addressing Multiplicity IssuesIncludes practical examples from recent trials Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, Multiple Testing Problems in Pharmaceutical Statistics explores the rapidly growing area of multiple c