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Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their work. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability.
Graduate and post-graduate students in the areas of statistical quality and engineering, as well as industrial statisticians, researchers and practitioners in these fields will all benefit from the comprehensive combination of theoretical and practical information provided in this single volume.
Modern Industrial Statistics: With applications in R, MINITAB and JMP:
- Combines a practical approach with theoretical foundations and computational support.
- Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP.
- Includes exercises at the end of each chapter to aid learning and test knowledge.
- Provides over 40 data sets representing real-life case studies.
- Is complemented by a comprehensive website providing an introduction to R, and installations of JMP scripts and MINITAB macros, including effective tutorials with introductory material: www.wiley.com/go/modern_industrial_statistics.
- Sales Rank: #1836635 in eBooks
- Published on: 2013-12-16
- Released on: 2013-12-16
- Format: Kindle eBook
Review
“This book delivers on its promise of providing a theoretical, practical, and computer-based approach to industrial statistics." (Journal of Quality Technology, 1 October 2014)
From the Back Cover
Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their work. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability.
Graduate and post-graduate students in the areas of statistical quality and engineering, as well as industrial statisticians, researchers and practitioners in these fields will all benefit from the comprehensive combination of theoretical and practical information provided in this single volume.
Modern Industrial Statistics: With applications in R, MINITAB and JMP:
- Combines a practical approach with theoretical foundations and computational support.
- Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP.
- Includes exercises at the end of each chapter to aid learning and test knowledge.
- Provides over 40 data sets representing real-life case studies.
- Is complemented by a comprehensive website providing an introduction to R, and installations of JMP scripts and MINITAB macros, including effective tutorials with introductory material: www.wiley.com/go/modern_industrial_statistics.
About the Author
RON S. KENETT, The KPA Group, Israel, University of Turin, Italy and NYU Center for Risk Engineering, New York, USA
SHELEMYAHU ZACKS, Binghamton University, Binghamton, USA
With contributions from DANIELE AMBERTI, Turin, Italy
Most helpful customer reviews
4 of 4 people found the following review helpful.
Exceptional second edition
By Yehuda Blumenfeld
This second edition to a groundbreaking book originally published in 1998 is providing an unusual platform for teaching and studying modern industrial statistics using R, MINITAB or JMP. The R applications are packaged in the mistat R application downloadable from CRAN, a couple of JMP add ins provided added functionality to JMP for teaching statistical process control, design of experiments and bootstrapping. As mentioned in the preface, the book combines a strong theoretical foundation with practical examples and software based analysis. The book reflects the wide experience of the authors in the many examples and problems it treats. The book web site provides access to all appendices including R scripts, by chapter, an introduction to R by one of the R core team members, an introduction to MINITAB and a solution manual. This is probably the most comprehensive text on modern industrial statistics available on the market.
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