Textbook in PDF format

This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.
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 cybermanufacturing 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. Every chapter includes exercises, data sets, and Python applications.
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.
The Role of Statistical Methods in Modern Industry
Basic Tools and Principles of Process Control
Advanced Methods of Statistical Process Control
Multivariate Statistical Process Control
Classical Design and Analysis of Experiments
Quality by Design
Computer Experiments
Cybermanufacturing and Digital Twins
Reliability Analysis
Bayesian Reliability Estimation and Prediction
Sampling Plans for Batch and Sequential Inspection