Modern Industrial Statistics With Applications in R, MINITAB, and JMP

by Kenett, Ron S.; Zacks, Shelemyahu

• ISBN13: 9781119714903
• ISBN10: 1119714907
• Edition: 3rd
• Format: Hardcover
• Copyright: 2021-05-10
• Publisher: Wiley

Summary

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.

Author Biography

Shelemyahu Zacks is Distinguished Emeritus Professor of Mathematical Sciences, at Binghamton University, Binghamton New York. He has published ten books and close to two-hundred 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 mine fields 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. 

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 Appendix VIII Solution manual