2nd Edition
by Ron S. Kenett (Author), Shelemyahu Zacks (Author), Daniele Amberti (Author)
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.
