Statistical Methods for Quality Assurance: Basics, Measurement, Control, Capability, and Improvement(Springer Texts in Statistics)

This undergraduate statistical quality assurance textbook clearly shows with real projects, cases and data sets how statistical quality control tools are used in practice.  Among the topics covered is a practical evaluation of measurement effectiveness for both continuous and discrete data.  Gauge Reproducibility and Repeatability methodology (including confidence intervals for Repeatability, Reproducibility and the Gauge Capability Ratio) is thoroughly developed. Process capability indices and corresponding confidence intervals are also explained.  In addition to process monitoring techniques, experimental design and analysis for process improvement are carefully presented. Factorial and Fractional Factorial arrangements of treatments and Response Surface methods are covered. Integrated throughout the book are rich sets of examples and problems that help readers gain a better understanding of where and how to apply statistical quality control tools.  These large and realistic problem sets in combination with the streamlined approach of the text and extensive supporting material facilitate reader understanding. Second Edition Improvements Extensive coverage of measurement quality evaluation (in addition to ANOVA Gauge R&R methodologies) New end-of-section exercises and revised-end-of-chapter exercises Two full sets of slides, one with audio to assist student preparation outside-of-class and another appropriate for professors’ lectures Substantial supporting material Supporting Material Seven R programs that support variables and attributes control chart construction and analyses, Gauge R&R methods, analyses of Fractional Factorial studies,  Propagation of Error analyses and Response Surface analyses Documentation for the R programs Excel data files associated with theend-of-chapter problem sets, most from real engineering settings

Table of Contents:
Introduction.- Statistics and Measurement.- Process Monitoring.- Process Characterization and Capability Analysis.- Experiment Design and Analysis for Process Improvement Part 1: Basics.- Experiment Design and Analysis for Process Improvement Part 2: Advanced Topics.- A Tables.

About the Author :
Stephen Vardeman is Professor of Statistics and Industrial Engineering at Iowa State University. He is a Fellow of the American Statistical Association and an (elected) Ordinary Member of the International Statistical Institute. His interests include physical science and engineering applications of statistics, statistics and metrology, quality assurance, business applications of statistics, modern ("big") data analytics, statistical machine learning, directional data analysis, reliability, statistics education, and the development of new statistical theory and methods. He has published textbooks in both quality assurance and engineering statistics and recently developed effective new graduate-level coursework in modern statistical machine learning.   J. Marcus Jobe is Professor of Information Systems and Analytics in the Farmer School of Business at Miami University (Ohio). His current research interests focus on measurement quality, multivariate process monitoring, pattern recognition, and oil exploration applications. Professor Jobe has taught and conducted research in Ukraine, and he has won numerous teaching awards at Miami University, including the all-university Outstanding Teaching award and the Richard T. Farmer Teaching Excellence award for senior professors in the school of business. The U.S. Dept. of State twice awarded him the Senior Fulbright Scholar award (1996-1997 and 2005-2006). Marcus Jobe also works as a consultant and has co-authored two statistics texts with Stephen Vardeman.

Review :
“This is a well-written book and provides a good number of worked examples to validate how the methods are actually used in real life situation using real datasets. … The main strength of the book is that it still offers a good number of applications that are based on real datasets emerging from an industrial sector. … I think this book can be successfully adopted for an undergraduate course on quality control and related topics.” (S. Ejaz Ahmed, Technometrics, Vol. 59 (1), January, 2017)