Analysis of Variance

Author: Gudmund R. Iversen,Albert R. Wildt,Helmut Norpoth,Helmut P. Norpoth

Publisher: SAGE

ISBN: 9780803930018

Category: Mathematics

Page: 94

View: 1881

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The second edition of this book provides a conceptual understanding of analysis of variance. It outlines methods for analysing variance that are used to study the effect of one or more nominal variables on a dependent, interval level variable. The book presumes only elementary background in significance testing and data analysis.
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The Analysis of Variance

Author: Henry Scheffé

Publisher: John Wiley & Sons

ISBN: 9780471345053

Category: Mathematics

Page: 477

View: 4588

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Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.
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The Analysis of Variance

Fixed, Random and Mixed Models

Author: Hardeo Sahai,Mohammed I. Ageel

Publisher: Springer Science & Business Media

ISBN: 9780817640125

Category: Mathematics

Page: 742

View: 4813

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The analysis of variance (ANOYA) models have become one of the most widely used tools of modern statistics for analyzing multifactor data. The ANOYA models provide versatile statistical tools for studying the relationship between a dependent variable and one or more independent variables. The ANOYA mod els are employed to determine whether different variables interact and which factors or factor combinations are most important. They are appealing because they provide a conceptually simple technique for investigating statistical rela tionships among different independent variables known as factors. Currently there are several texts and monographs available on the sub ject. However, some of them such as those of Scheffe (1959) and Fisher and McDonald (1978), are written for mathematically advanced readers, requiring a good background in calculus, matrix algebra, and statistical theory; whereas others such as Guenther (1964), Huitson (1971), and Dunn and Clark (1987), although they assume only a background in elementary algebra and statistics, treat the subject somewhat scantily and provide only a superficial discussion of the random and mixed effects analysis of variance.
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A Student's Guide to Analysis of Variance

Author: Maxwell Roberts,Riccardo Russo

Publisher: Routledge

ISBN: 1317725069

Category: Psychology

Page: 288

View: 4036

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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.
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Analysis of Variance, Design, and Regression

Applied Statistical Methods

Author: Ronald Christensen

Publisher: CRC Press

ISBN: 9780412062919

Category: Mathematics

Page: 608

View: 1599

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This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.
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Multivariate Analysis of Variance

Author: James H. Bray,Scott E. Maxwell,Scott E.. Maxwell

Publisher: SAGE

ISBN: 9780803923102

Category: Social Science

Page: 80

View: 1457

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Bray's monograph considers the multivariate form of analysis of variance (MANOVA). The primary emphasis of the book is on methods for interpreting MANOVA statistical analyses. These are illustrated through the use of two numerical examples: one involves a small, hypothetical data set, which can be analyzed by the reader with minimal effort; the other involves real data and shows how MANOVA can be used in actual research. MANOVA represents a logical extension of Analysis of Variance by Iversen and Norpoth (QASS 1).
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Applied Analysis of Variance in Behavioral Science

Author: Lynne Edwards

Publisher: CRC Press

ISBN: 9780824788964

Category: Mathematics

Page: 640

View: 4719

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A reference devoted to the discussion of analysis of variance (ANOVA) techniques. It presents ANOVA as a research design, a collection of statistical models, an analysis model, and an arithmetic summary of data. Discussion focuses primarily on univariate data, but multivariate generalizations are to
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Fundamentals of Exploratory Analysis of Variance

Author: David C. Hoaglin,Frederick Mosteller,John W. Tukey

Publisher: John Wiley & Sons

ISBN: 0470317663

Category: Mathematics

Page: 448

View: 5058

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The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most chapters include exercises and the appendices give selected percentage points of the Gaussian, t, F chi-squared and studentized range distributions.
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Interaction Effects in Factorial Analysis of Variance

Author: James Jaccard,Jim Jaccard

Publisher: SAGE

ISBN: 9780761912217

Category: Mathematics

Page: 103

View: 9285

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Although factorial analysis is widely used in the social sciences, there is some confusion as to how to use the technique's most powerful feature - the evaluation of interaction effects. Written to remedy this situation, this book explores the issues underlying the effective analysis of interaction in factorial designs. It includes discussion of: different ways of characterizing interactions in ANOVA; interaction effects using traditional hypothesis testing approaches; and alternative analytic frameworks that focus on effect size methodology and interval estimation.
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Analysis of Variance for Random Models, Volume 2: Unbalanced Data

Theory, Methods, Applications, and Data Analysis

Author: Hardeo Sahai,Mario M. Ojeda

Publisher: Springer Science & Business Media

ISBN: 0817644253

Category: Mathematics

Page: 480

View: 2526

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Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs with a detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level. It also includes numerical examples to analyze data from a wide variety of disciplines as well as any worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example.
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