Overview
- Demonstrates how to incorporate Python into the industrial statistics curriculum
- Includes over 40 case studies to facilitate experiential learning
- An accompanying Python package is available for download, allowing students to engage directly with the material
Part of the book series: Statistics for Industry, Technology, and Engineering (SITE)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
The first chapters of the text focus on the basic tools and principles of process control, methods of statistical process control (SPC), and multivariate SPC. Next, the authors explore the design and analysis of experiments, quality control and the Quality by Design approach, computer experiments, and cyber manufacturing and digital twins. The text then goes on to cover reliability analysis, accelerated life testing, and Bayesian reliability estimation and prediction. A final chapter considers sampling techniques and measures of inspection effectiveness. Each chapter includes exercises, data sets, and applications to supplement learning.
Industrial Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. In addition, it can be used in focused workshops combining theory, applications, and Python implementations. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included.
A second, closely related textbook is titled Modern Statistics: A Computer-Based Approach with Python. It covers topics such as probability models and distribution functions, statistical inference and bootstrapping, time series analysis and predictions,and supervised and unsupervised learning. These texts can be used independently or for consecutive courses.
The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/IndustrialStatistics/.
"This book is part of an impressive and extensive write up enterprise (roughly 1,000 pages!) which led to two books published by Birkhäuser. This book is on Industrial Statistics, an area in which the authors are recognized as major experts. The book combines classical methods (never to be forgotten!) and "hot topics" like cyber manufacturing, digital twins, A/B testing and Bayesian reliability. It is written in a very accessible style, focusing not only on HOW the methods are used, but also on WHY. In particular, the use of Python, throughout the book is highly appreciated. Python is probably the most important programming language used in modern analytics. The authors are warmly thanked for providing such a state-of-the-art book. It provides a comprehensive illustration of methods and examples based on the authors longstanding experience, and accessible code for learning and reusing in classrooms and on-site applications."
Professor Fabrizio Ruggeri
Research Director at the National Research Council, Italy
President of the International Society for Business and Industrial Statistics (ISBIS)
Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)
Authors and Affiliations
About the authors
Shelemyahu Zacks is a Distinguished Professor emeritus in the Mathematical Sciences department of Binghamton University.
He is a Fellow of the IMS, ASA, AAAS and an elected member of the ISI. Professor Zacks has published eleven books and more than 170 journal articles on subjects of design of experiments, statistical process control, statistical decision theory, sequential analysis, reliability and sampling from finite populations. Professor Zacks has served as an Editor and Associate Editor of several Statistics and Probability journals.
Dr. Peter Gedeck, a Senior Data Scientist at Collaborative Drug Discovery, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. In addition, he teaches data science at the University of Virginia and at statistics.com.
Bibliographic Information
Book Title: Industrial Statistics
Book Subtitle: A Computer-Based Approach with Python
Authors: Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck
Series Title: Statistics for Industry, Technology, and Engineering
DOI: https://doi.org/10.1007/978-3-031-28482-3
Publisher: Birkhäuser Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-28481-6Published: 18 June 2023
Softcover ISBN: 978-3-031-28484-7Due: 18 July 2023
eBook ISBN: 978-3-031-28482-3Published: 16 June 2023
Series ISSN: 2662-5555
Series E-ISSN: 2662-5563
Edition Number: 1
Number of Pages: XXIII, 472
Number of Illustrations: 1 b/w illustrations
Topics: Statistics and Computing/Statistics Programs, Applied Statistics