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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.
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