Modern Industrial Statistics
The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches
Modern Industrial Statistics is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications.
The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies–drawn from the electronics, metal work, pharmaceutical, and financial industries–are complemented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume:
* Explains the use of computer-based methods such as bootstrapping and data visualization
* Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts
* Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings
* Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable appendices
* Provides an author-created R package, mistat, that includes all data sets and statistical analysis applications used in the book
Part of the acclaimed Statistics in Practice series, Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields. The mistat R-package is available from the R CRAN repository.
Inhaltsverzeichnis
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
Über den Autor
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.
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