Modern industrial statistics : with applications in R, MINITAB and JMP / Ron S. Kenett and Shelemyahu Zacks.

Includes bibliographical references and index.

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. Sampling for estimation of finite population quantities--
7. Sampling plans for product inspection--

Part II 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 and survival analysis
14. Reliability analysis--
15. Bayesian reliability estimation and prediction--

Appendix I:
Appendix II:
Appendix III:
Appendix IV:
Appendix V:
Appendix VI:
Appendix VII:
Appendix VIII:
With contributions from Daniele Amberti

Covering industrial statistics tools used in business and industry, this book combines theoretical background with examples and references to R, MINITAB, and JMP to provide relevant material on both the foundation and implementation tools to support their work.