Book description
A unique approach to understanding the foundations of statistical quality control with a focus on the latest developments in nonparametric control charting methodologies
Statistical Process Control (SPC) methods have a long and successful history and have revolutionized many facets of industrial production around the world. This book addresses recent developments in statistical process control bringing the modern use of computers and simulations along with theory within the reach of both the researchers and practitioners. The emphasis is on the burgeoning field of nonparametric SPC (NSPC) and the many new methodologies developed by researchers worldwide that are revolutionizing SPC.
Over the last several years research in SPC, particularly on control charts, has seen phenomenal growth. Control charts are no longer confined to manufacturing and are now applied for process control and monitoring in a wide array of applications, from education, to environmental monitoring, to disease mapping, to crime prevention. This book addresses quality control methodology, especially control charts, from a statistician’s viewpoint, striking a careful balance between theory and practice. Although the focus is on the newer nonparametric control charts, the reader is first introduced to the main classes of the parametric control charts and the associated theory, so that the proper foundational background can be laid.
 Reviews basic SPC theory and terminology, the different types of control charts, control chart design, sample size, sampling frequency, control limits, and more
 Focuses on the distributionfree (nonparametric) charts for the cases in which the underlying process distribution is unknown
 Provides guidance on control chart selection, choosing control limits and other quality related matters, along with all relevant formulas and tables
 Uses computer simulations and graphics to illustrate concepts and explore the latest research in SPC
Offering a uniquely balanced presentation of both theory and practice, Nonparametric Methods for Statistical Quality Control is a vital resource for students, interested practitioners, researchers, and anyone with an appropriate background in statistics interested in learning about the foundations of SPC and latest developments in NSPC.
Table of contents
 Cover
 About the Authors
 Preface
 About the companion website
 1 Background/Review of Statistical Concepts
 2 Basics of Statistical Process Control

3 Parametric Univariate Variables Control Charts
 Chapter Overview
 3.1 Introduction
 3.2 Parametric Variables Control Charts in Case K
 3.3 Types of Parametric Variables Charts in Case K: Illustrative Examples
 3.4 Shewhart, EWMA, and CUSUM Charts: Which to Use When
 3.5 Control Chart Enhancements
 3.6 Run‐length Distribution in the Specified Parameter Case (Case K)
 3.7 Parameter Estimation Problem and Its Effects on the Control Chart Performance
 3.8 Parametric Variables Control Charts in Case U
 3.9 Types of Parametric Control Charts in Case U: Illustrative Examples
 3.10 Run‐length Distribution in the unknown Parameter Case (Case U)
 3.11 Control Chart Enhancements
 3.12 Phase I Control Charts
 3.13 Size of Phase I Data
 3.14 Robustness of Parametric Control Charts
 Some Derivations for the EWMA Control Chart
 Markov Chains
 Some Derivations for the Shewhart Dispersion Charts

4 Nonparametric (Distribution‐free) Univariate Variables Control Charts
 Chapter Overview
 4.1 Introduction
 4.2 Distribution‐free Variables Control Charts in Case K
 4.3 Distribution‐free Control Charts in Case K: Illustrative Examples
 4.4 Distribution‐free Variables Control Charts in Case U
 4.5 Distribution‐free Control Charts in Case U: Illustrative Examples
 4.6 Effects of Parameter Estimation
 4.7 Size of Phase I Data
 4.8 Control Chart Enhancements
 Shewhart Control Charts
 \hbox {Appendix 4.2 CUSUM Control Charts
 EWMA Control Charts
 5 Miscellaneous Univariate Distribution‐free (Nonparametric) Variables Control Charts

Appendix A: Tables
 Table A: Binomial distribution – probabilities for the in‐control case
 Table B: Probabilities for the Wilcoxon signed‐rank statistic
 Table C: Unbiasing charting constants for the construction of normal‐theory variables control charts
 Table D1: Cumulative probabilities for the standard normal distribution
 Table D2: Cumulative probabilities for the standard normal distribution continued
 Table E: Upper tail probabilities for the t distribution
 Table F: Upper tail probabilities for the Chi‐square distribution
 Table G: Charting constants for the Phase II Shewhart control chart in Case UU for n = 5, varying m, and ARL IC = 370 and 500
 Table H: Charting constants for Phase II Shewhart R and S control charts in Case UU with three Phase I estimators of standard deviation for nominal ARL IC values of 370 and 500 with varying m and n = 5, 10
 Appendix BProgrammes
 References
 Index
 End User License Agreement
Product information
 Title: Nonparametric Statistical Process Control
 Author(s):
 Release date: April 2019
 Publisher(s): Wiley
 ISBN: 9781118456033
You might also like
book
Statistics for Process Control Engineers
The first statistics guide focussing on practical application to process control design and maintenance Statistics for …
book
Storytelling with Data
Influence action through data! This is not a book. It is a oneofakind immersive learning experience …
book
Multivariate Time Series Analysis and Applications
An essential guide on high dimensional multivariate time series including all the latest topics from one …
book
Computational Thinking  A beginner's guide to problemsolving and programming
Computational thinking (CT) is a timeless, transferable skill that enables you to think more clearly and …