A novel presentation of rank and permutation tests, with accessible guidance to applications in R
Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size.
Key Features:
Features a supporting website (www.wiley.com/go/hypothesis_testing) containing all of the data sets examined in the book along with ready to use R software codes.
Nonparametric Hypothesis Testing combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.
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Stefano Bonnini, Assistant Professor of Statistics, Faculty of Economics, Department of Economics, University of Ferrara, Italy.
Livio Corain, Assistant Professor of Statistics, Faculty of Engineering, Department of Management and Engineering, University of Padova, Italy.
Marco Marozzi, Associate Professor of Statistics, Faculty of Economics, Department of Economics and Statistics, University of Calabria, Italy.
Luigi Salmaso, Full Professor of Statistics, Faculty of Engineering, University of Padova, Italy.
“The book combines an up to date overview with useful practical guidance to applications in R, and will be a valuable resource for practitioners and researchers working in a wide range of scientific fields including engineering, biostatistics, psychology and medicine.” (Zentralblatt MATH, 1 October 2014)
"About this title" may belong to another edition of this title.
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