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Provides over 40 data sets representing real-life case studies. Includes exercises at the end of each chapter to aid learning and test knowledge. Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP. Modern Industrial Statistics: With applications in R, MINITAB and JMP: Combines a practical approach with theoretical foundations and computational support. 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.
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Select Chapter 9 - Design of Experiments. The primary use of a regression model is prediction. There are different forms of regression: simple linear, nonlinear, multiple, and others. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability. The technique of regression is one of the most popular statistical tools to study the dependence of one variable with respect to another. 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. with applications in R, MINITAB and JMP"įully 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.