Search results for ""Author J. C. W. Rayner""

An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA Complete reference for applied statisticians and data analysts that uniquely covers the new statistical methodologies that enable deeper data analysis An Introduction to Cochran-Mantel-Haenszel Testing and Nonparametric ANOVA provides readers with powerful new statistical methodologies that enable deeper data analysis. The book offers applied statisticians an introduction to the latest topics in nonparametrics. The worked examples with supporting R code provide analysts the tools they need to apply these methods to their own problems. Co-authored by an internationally recognised expert in the field and an early career researcher with broad skills including data analysis and R programming, the book discusses key topics such as: NP ANOVA methodology Cochran-Mantel-Haenszel (CMH) methodology and design Latin squares and balanced incomplete block designs Parametric ANOVA F tests for continuous data Nonparametric rank tests (the Kruskal-Wallis and Friedman tests) CMH MS tests for the nonparametric analysis of categorical response data Applied statisticians and data analysts, as well as students and professors in data analysis, can use this book to gain a complete understanding of the modern statistical methodologies that are allowing for deeper data analysis.

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Sensory evaluation is the perception science of the food industry. Sensory data can be costly to obtain and so gleaning the most information possible from the data is key. Increasingly, value is added to sensory evaluation by the use of statistics, especially to improve the quality of product development and to make the most of market research. Nonparametrics for Sensory Science is written to complement existing parametric methodology. Nonparametric methods are appropriate when facts are only available in nominal or ordinal form, and when the model assumptions necessary for parametric procedures do not hold. Author Rayner and his colleagues consider problems including the most commonly occurring and important experimental designs: the one-sample, k-sample, blocked samples, samples with factorial structure and samples with correlation structure. Innovative new techniques are outlined and complemented with real examples. Techniques described may be applied to data where the traditional, most frequently applied nonparametric tests, such as the Kruskal-Wallis, the Friedman and the Spearman tests, are applied. Those familiar with traditional nonparametric testing will be able to update their knowledge, acquiring powerful new methods. Those without prior knowledge of nonparametric testing will be able to acquire that knowledge through this book. Aimed at sensory scientists and statisticians interested in nonparametrics, the techniques of Nonparametrics for Sensory Science are of broad general interest, but are of particular interest in sensory evaluation applications.

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