Comparisons Among Treatment Means in an Analysis of Variance PDF Download

Author: Victor Chew
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 72

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Book Description

Author: Larry E. Toothaker
Publisher: SAGE Publications, Incorporated
ISBN:
Category : Reference
Languages : en
Pages : 184

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Book Description
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.

Author: Victor Chew
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 72

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Book Description

Author: Daniel Navarro
Publisher: Lulu.com
ISBN: 1326189727
Category : Computers
Languages : en
Pages : 617

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Book Description
"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Author: Victor Chew
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 0

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Book Description

Author: Alan J. Klockars
Publisher: SAGE
ISBN: 9780803920514
Category : Mathematics
Languages : en
Pages : 92

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Book Description
Describes the most important methods used to investigate differences between levels of an independent variable within an experimental design. Readers will learn not only how to conduct multiple comparisons in experimental designs but also how to better understand and evaluate published research. "A highly readable introduction to multiple comparison methods, which demands little from its reader in the way of background other than some familiarity with analysis of variance." --The Statistician

Author: Jason Hsu
Publisher: CRC Press
ISBN: 1482221276
Category : Mathematics
Languages : en
Pages : 292

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Book Description
Multiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method

Author: Frank Bretz
Publisher: CRC Press
ISBN: 1420010905
Category : Mathematics
Languages : en
Pages : 202

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Book Description
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.

Author: Peter R. Nelson
Publisher: SIAM
ISBN: 9780898718362
Category : Analysis of means
Languages : en
Pages : 259

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Book Description
The analysis of means (ANOM) is a graphical procedure used to quantify differences among treatment groups in a variety of experimental design and observational study situations. The ANOM decision chart allows one to easily draw conclusions and interpret results with respect to both statistical and practical significance. It is an excellent choice for multiple comparisons of means, rates, or proportions and can be used with both balanced and unbalanced data. Key advances in ANOM procedures that have appeared only in technical journals during the last 20 years are included in this first comprehensive modern treatment of the ANOM containing all of the needed information for practitioners to understand and apply ANOM.

Author: Maxwell Roberts
Publisher: Routledge
ISBN: 1317725069
Category : Psychology
Languages : en
Pages : 286

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Book Description
In the investigation of human behaviour, statistical techniques are employed widely in the social sciences. Whilst introductory statistics courses cover essential techniques, the complexities of behaviour demand that more flexible and comprehensive methods are also employed. Analysis of Variance (ANOVA) has become one of the most common of these and it is therefore essential for both student and researcher to have a thorough understanding of it. A Student's Guide to Analysis of Variance covers a range of statistical techniques associated with ANOVA, including single and multiple factor designs, various follow-up procedures such as post-hoc tests, and how to make sense of interactions. Suggestions on the best use of techniques and advice on how to avoid the pitfalls are included, along with guidelines on the writing of formal reports. Introductory level topics such as standard deviation, standard error and t-tests are revised, making this book an invaluable aid to all students for whom ANOVA is a compulsory topic. It will also serve as a useful refresher for the more advanced student and practising researcher.