Modern Industrial Statistics: with applications in R, MINITAB and JMP

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 .

Preface to first edition Preface to second edition List of Abbreviations Part I: Principles of Statistical Thinking and Analysis 1. The Role of Statistical Methods in Modern Industry and Services 2. Analyzing Variability: Descriptive Statistics 3. Probability Models and Distribution Functions 4. Statistical Inference  and Bootstrapping 5. Variability in Several Dimensions and Regression Models Part II: Acceptance Sampling 6. Estimation in Finite Populations 7. Sampling Plans for Product Inspection Part III: Statistical Process Control 8. Basic Tools and Principles of Process Control 9. Advanced methods of Statistical Process Control 10. Multivariate Statistical Process Control Part IV: Design and Analysis of Experiments 11. Classical Design and Analysis of experiments 12. Quality by Design 13. Computer Experiments Part V: Reliability 14. Reliability Analysis 15. Bayesian Reliability Estimation and Prediction References Subject Index Author Index Also available on book’s website: www.wiley.com/go/modern—industrial—statistics Appendix I: Introduction to R, by Stefano Iacus Appendix II: Basic MINITAB commands and a review of matrix algebra for Statistics Appendix III: R code included in the book, also available on the R CRAN website as MistatMain.R Appendix IV: Source version of MistatMain.R  (mistat—1.0.tar.gz) Appendix V: Data sets as csv files (DatFiles.zip) Appendix VI: MINITAB macros Appendix VII: JMP scripts, by Ian Cox Appendix VIII: Solution manual