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Introduction to statistical process control

By: Qiu, Peihua.
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC texts in statistical science.Publisher: Boca Raton CRC Press 2014Description: xxxvii, 482 p.ISBN: 9781439847992.Subject(s): Process control | Statistical methods | Statistics - General | Mathematics - Probability | Quality Control | Technology and engineeringDDC classification: 658.5 Summary: Features • Explores the major advantages and limitations of traditional and state-of-the-art SPC methods • Offers practical guidelines on implementing the techniques • Examines the most recent research results in various areas, including univariate and multivariate nonparametric SPC, SPC based on change-point detection, and profile monitoring • Keeps the mathematical and statistical prerequisites to a minimum, only requiring basic linear algebra, some calculus, and introductory statistics • Provides more advanced or technical material in discussions at the end of each chapter, along with exercises that encourage hands-on practice with the methods • Presents pseudo codes for important methods • Includes all R functions and datasets on the author’s website Summary A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon the more established techniques. The author—a leading researcher on SPC—shows how these methods can handle new applications. After exploring the role of SPC and other statistical methods in quality control and management, the book covers basic statistical concepts and methods useful in SPC. It then systematically describes traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. The text also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring. All computations in the examples are solved using R, with R functions and datasets available for download on the author’s website. Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques. https://www.crcpress.com/Introduction-to-Statistical-Process-Control/Qiu/p/book/9781439847992
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Table of Contents

Introduction
Quality and the Early History of Quality Improvement
Quality Management
Statistical Process Control
Organization of the Book

Basic Statistical Concepts and Methods
Introduction
Population and Population Distribution
Important Continuous Distributions
Important Discrete Distributions
Data and Data Description
Tabular and Graphical Methods for Describing Data
Parametric Statistical Inferences
Nonparametric Statistical Inferences

Univariate Shewhart Charts and Process Capability
Introduction
Shewhart Charts for Numerical Variables
Shewhart Charts for Categorical Variables
Process Capability Analysis
Some Discussions

Univariate CUSUM Charts
Introduction
Monitoring the Mean of a Normal Process
Monitoring the Variance of a Normal Process
CUSUM Charts for Distributions in Exponential Family
Self-Starting and Adaptive CUSUM Charts
Some Theory for Computing ARL Values
Some Discussions

Univariate EWMA Charts
Introduction
Monitoring the Mean of a Normal Process
Monitoring the Variance of a Normal Process
Self-Starting and Adaptive EWMA Charts
Some Discussions

Univariate Control Charts by Change-Point Detection
Introduction
Univariate Change-Point Detection
Control Charts by Change-Point Detection
Some Discussions

Multivariate Statistical Process Control
Introduction
Multivariate Shewhart Charts
Multivariate CUSUM Charts
Multivariate EWMA Charts
Multivariate Control Charts by Change-Point Detection
Multivariate Control Charts by LASSO
Some Discussions

Univariate Nonparametric Process Control
Introduction
Rank-Based Nonparametric Control Charts
Nonparametric SPC by Categorical Data Analysis
Some Discussions

Multivariate Nonparametric Process Control
Introduction
Rank-Based Multivariate Nonparametric Control Charts
Multivariate Nonparametric SPC by Log-Linear Modeling
Some Discussions

Profile Monitoring
Introduction
Parametric Profile Monitoring
Nonparametric Profile Monitoring
Some Discussions

Appendix A: R Functions for SPC
Appendix B: Datasets Used in the Book
Bibliography
Index


Features
• Explores the major advantages and limitations of traditional and state-of-the-art SPC methods
• Offers practical guidelines on implementing the techniques
• Examines the most recent research results in various areas, including univariate and multivariate nonparametric SPC, SPC based on change-point detection, and profile monitoring
• Keeps the mathematical and statistical prerequisites to a minimum, only requiring basic linear algebra, some calculus, and introductory statistics
• Provides more advanced or technical material in discussions at the end of each chapter, along with exercises that encourage hands-on practice with the methods
• Presents pseudo codes for important methods
• Includes all R functions and datasets on the author’s website
Summary
A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon the more established techniques. The author—a leading researcher on SPC—shows how these methods can handle new applications. After exploring the role of SPC and other statistical methods in quality control and management, the book covers basic statistical concepts and methods useful in SPC. It then systematically describes traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. The text also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring. All computations in the examples are solved using R, with R functions and datasets available for download on the author’s website. Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques.


https://www.crcpress.com/Introduction-to-Statistical-Process-Control/Qiu/p/book/9781439847992

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