Modern industrial statistics : with applications in R, MINITAB and JMP / Ron S. Kenett, Chairman and CEO, the KPA Group, Raanana, Israel Research Professor, University of Turin, Turin, Italy, and Senior Research Fellow, Samuel Neaman Institute for National Policy Research, Technion, Israel, Shelemyahu Zacks, Distinguished Professor, Binghamton University, Binghamton, USA ; with contributions from Daniele Amberti, Turin, Italy.

Includes bibliographical references and index.

Table of Contents
Preface to the third edition

Preface to the second edition (abbreviated)

Preface to the first edition (abbreviated)

List of abbreviations

Part A: Modern Statistics: A Computer Based Approach

1 Statistics and Analytics in Modern Industry

2 Analyzing Variability: Descriptive Statistics

3 Probability Models and Distribution Functions

4 Statistical Inference and Bootstrapping

5 Variability in Several Dimensions and Regression Models

6 Sampling for Estimation of Finite Population Quantities

7. Time Series Analysis and Prediction

8 Modern analytic methods

Part B: Modern Industrial Statistics: Design and Control of Quality and Reliability

9 The Role of Industrial Analytics in Modern Industry

10 Basic Tools and Principles of Process Control

11 Advanced Methods of Statistical Process Control

12 Multivariate Statistical Process Control

13 Classical Design and Analysis of Experiments

14 Quality by Design

15 Computer Experiments

16 Reliability Analysis

17 Bayesian Reliability Estimation and Prediction

18 Sampling Plans for Batch and Sequential Inspection

List of R packages

References

Author index

Subject index

Solution manual

Appendices (available on book’s website)

Appendix I Intro to R Appendix II Intro to MINITAB and Matrix Algebra Appendix III R scripts Appendix IV mistat Appendix V csv Files Appendix VI MINITAB macros Appendix VII JMP scripts

"Industrial Statistics is concerned with maintaining and improving the quality of goods and services. It involves a broad range of statistical tools but maintaining and improving quality is its main concern. Variability is inherent in all processes, whether they be manufacturing processes or service processes. This variability must be controlled to create high quality goods and services and must be reduced to improve quality. Industrial Statistics focuses on the use of statistical thinking, i.e., the appreciation of the inherent variability of all processes in order that all possible outcomes can be assessed. It also focuses on developing skills for modeling data and designing experiments that can lead to improvements in performance and reductions in variablity"-- Provided by publisher.

About the Author
Ron S. Kenett is Chairman of the KPA Group and Senior Research Fellow at the Samuel Neaman Institute, Israel. He is an applied statistician combining expertise in academic, consulting, and business domains. He is a former Professor of Operations Management at The State University of New York at Binghamton, Visiting Scholar at Stanford University, Member of Technical Staff at Bell Laboratories and Director of Statistical Methods for Tadiran Telecom. Ron is a past President of the Israel Statistical Association and of the European Network for Business and Industrial Statistics (ENBIS) and was awarded the 2013 Greenfield Medal by the Royal Statistical Society and the 2018 Box Medal by ENBIS for outstanding contributions to applied statistics. He has authored and co-authored over 250 papers and 14 books.

Shelemyahu Zacks is Distinguished Emeritus Professor of Mathematical Sciences at Binghamton University, Binghamton, New York, USA. He has published 10 books and close to 200 papers. Zacks is known for his groundbreaking articles on change-point problems, common mean problems, Bayes sequential strategies, and reliability analysis. His studies on survival probabilities in crossing minefields and his contributions in stochastic visibility in random fields are regarded as fundamental work in naval research and other defense related areas. He has served on the editorial boards of several prestigious journals including JASA, JSPI and Annals of Statistics, and is a Fellow of many associations including the AMS, ASA and AAAS.