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E-raamat: Nonparametric Statistical Process Control

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  • Ilmumisaeg: 12-Feb-2019
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118890677
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Nonparametric Statistical Process ControlSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 12-Feb-2019
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118890677
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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 distribution-free (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.

About the Authors xiii
Preface xv
About the companion website xix
1 Background/Review of Statistical Concepts
1(22)
Chapter Overview
1(1)
1.1 Basic Probability
1(2)
1.2 Random Variables and Their Distributions
3(9)
1.3 Random Sample
12(4)
1.4 Statistical Inference
16(6)
1.5 Role of the Computer
22(1)
2 Basics of Statistical Process Control
23(40)
Chapter Overview
23(1)
2.1 Basic Concepts
23(40)
2.1.1 Types of Variability
23(2)
2.1.2 The Control Chart
25(4)
2.1.3 Construction of Control Charts
29(1)
2.1.4 Variables and Attributes Control Charts
30(1)
2.1.5 Sample Size or Subgroup Size
31(1)
2.1.6 Rational Subgrouping
31(3)
2.1.7 Nonparametric or Distribution-free
34(2)
2.1.8 Monitoring Process Location and/or Process Scale
36(1)
2.1.9 Case K and Case U
37(1)
2.1.10 Control Charts and Hypothesis Testing
37(2)
2.1.11 General Steps in Designing a Control Chart
39(1)
2.1.12 Measures of Control Chart Performance
39(2)
2.1.12.1 False Alarm Probability {FAP)
41(2)
2.1.12.2 False Alarm Rate (FAR)
43(1)
2.1.12.3 The Average Run-length (ARL)
43(1)
2.1.12.4 Standard Deviation of Run-length (SDRL)
44(1)
2.1.12.5 Percentiles of Run-length
44(4)
2.1.12.6 Average Number of Samples to Signal (ANSS)
48(1)
2.1.12.7 Average Number of Observations to Signal (ANOS)
48(1)
2.1.12.8 Average Time to Signal (ATS)
48(1)
2.1.12.9 Number of Individual Items Inspected (I)
49(1)
2.1.13 Operating Characteristic Curves (OC-curves)
50(1)
2.1.14 Design of Control Charts
51(1)
2.1.14.1 Sample Size, Sampling Frequency, and Variable Sample Sizes
51(3)
2.1.14.2 Variable Control Limits
54(2)
2.1.14.3 Standardized Control Limits
56(1)
2.1.15 Size of a Shift
57(2)
2.1.16 Choice of Control Limits
59(1)
2.1.16.1 k-sigma Limits
59(1)
2.1.16.2 Probability Limits
60(3)
3 Parametric Univariate Variables Control Charts
63(124)
Chapter Overview
63(1)
3.1 Introduction
64(1)
3.2 Parametric Variables Control Charts in Case K
64(13)
3.2.1 Shewhart Control Charts
65(2)
3.2.2 CUSUM Control Charts
67(5)
3.2.3 EWMA Control Charts
72(5)
3.3 Types of Parametric Variables Charts in Case K: Illustrative Examples
77(13)
3.3.1 Shewhart Control Charts
77(1)
3.3.1.1 Shewhart Control Charts for Monitoring Process Mean
77(2)
3.3.1.2 Shewhart Control Charts for Monitoring Process Variation
79(5)
3.3.2 CUSUM Control Charts
84(3)
3.3.3 EWMA Control Charts
87(3)
3.4 Shewhart, EWMA, and CUSUM Charts: Which to Use When
90(1)
3.5 Control Chart Enhancements
91(19)
3.5.1 Sensitivity Rules
91(4)
3.5.2 Runs-type Signaling Rules
95(2)
3.5.2.1 Signaling Indicators
97(13)
3.6 Run-length Distribution in the Specified Parameter Case (Case K)
110(21)
3.6.1 Methods of Calculating the Run-length Distribution
110(1)
3.6.1.1 The Exact Approach (for Shewhart and some Shewhart-type Charts)
110(1)
3.6.1.2 The Markov Chain Approach
111(17)
3.6.1.3 The Integral Equation Approach
128(1)
3.6.1.4 The Computer Simulations (the Monte Carlo) Approach
128(3)
3.7 Parameter Estimation Problem and Its Effects on the Control Chart Performance
131(2)
3.8 Parametric Variables Control Charts in Case U
133(5)
3.8.1 Shewhart Control Charts in Case U
133(1)
3.8.1.1 Shewhart Control Charts for the Mean in Case U
133(1)
3.8.1.2 Shewhart Control Charts for the Standard Deviation in Case U
134(3)
3.8.2 CUSUM Chart for the Mean in Case U
137(1)
3.8.3 EWMA Chart for the Mean in Case U
137(1)
3.9 Types of Parametric Control Charts in Case U: Illustrative Examples
138(15)
3.9.1 Charts for the Mean
138(3)
3.9.2 Charts for the Standard Deviation
141(3)
3.9.2.1 Using the Estimator Sp
144(9)
3.10 Run-length Distribution in the unknown Parameter Case (Case U)
153(19)
3.10.1 Methods of Calculating the Run-length Distribution and Its Properties: The Conditioning/Unconditioning Method
153(1)
3.10.1.1 The Shewhart Chart for the Mean in Case U
153(16)
3.10.1.2 The Shewhart Chart for the Variance in Case U
169(1)
3.10.1.3 The CUSUM Chart for the Mean in Case U
170(1)
3.10.1.4 The EWMA Chart for the Mean in Case U
171(1)
3.11 Control Chart Enhancements
172(2)
3.11.1 Run-length Calculation for Runs-type Signaling Rules in Case U
172(2)
3.12 Phase I Control Charts
174(2)
3.12.1 Phase I X-chart
174(2)
3.13 Size of Phase I Data
176(1)
3.14 Robustness of Parametric Control Charts
177(1)
Appendix 3.1 Some Derivations for the EWMA Control Chart
178(2)
Appendix 3.2 Markov Chains
180(4)
Appendix 3.3 Some Derivations for the Shewhart Dispersion Charts
184(3)
4 Nonparametric (Distribution-free) Univariate Variables Control Charts
187(138)
Chapter Overview
187(1)
4.1 Introduction
187(2)
4.2 Distribution-free Variables Control Charts in Case K
189(30)
4.2.1 Shewhart Control Charts
189(1)
4.2.1.1 Shewhart Control Charts Based on Signs
189(7)
4.2.1.2 Shewhart Control Charts Based on Signed-ranks
196(6)
4.2.2 CUSUM Control Charts
202(1)
4.2.2.1 CUSUM Control Charts Based on Signs
202(1)
4.2.2.2 A CUSUM Sign Control Chart with Runs-type Signaling Rules
203(1)
4.2.2.3 Methods of Calculating the Run-length Distribution
203(2)
4.2.2.4 CUSUM Control Charts Based on Signed-ranks
205(3)
4.2.3 EWMA Control Charts
208(1)
4.2.3.1 EWMA Control Charts Based on Signs
208(2)
4.2.3.2 EWMA Control Charts Based on Signs with Runs-type Signaling Rules
210(1)
4.2.3.3 Methods of Calculating the Run-length Distribution
210(4)
4.2.3.4 EWMA Control Charts Based on Signed-ranks
214(2)
4.2.3.5 An EWMA-SR control chart with runs-type signaling rules
216(1)
4.2.3.6 Methods of Calculating the Run-length Distribution
216(3)
4.3 Distribution-free Control Charts in Case K: Illustrative Examples
219(34)
4.3.1 Shewhart Control Charts
219(10)
4.3.2 CUSUM Control Charts
229(14)
4.3.3 EWMA Control Charts
243(10)
4.4 Distribution-free Variables Control Charts in Case U
253(40)
4.4.1 Shewhart Control Charts
254(1)
4.4.1.1 Shewhart Control Charts Based on the Precedence Statistic
254(21)
4.4.1.2 Shewhart Control Charts Based on the Mann-Whitney Test Statistic
275(6)
4.4.2 CUSUM Control Charts
281(1)
4.4.2.1 CUSUM Control Charts Based on the Exceedance Statistic
281(4)
4.4.2.2 CUSUM Control Charts Based on the Wilcoxon Rank-sum Statistic
285(2)
4.4.3 EWMA Control Charts
287(1)
4.4.3.1 EWMA Control Charts Based on the Exceedance Statistic
287(3)
4.4.3.2 EWMA Control Charts Based on the Wilcoxon Rank-sum Statistic
290(3)
4.5 Distribution-free Control Charts in Case U: Illustrative Examples
293(14)
4.5.1 Shewhart Control Charts
293(2)
4.5.2 CUSUM Control Charts
295(7)
4.5.3 EWMA Control Charts
302(5)
4.6 Effects of Parameter Estimation
307(1)
4.7 Size of Phase I Data
307(1)
4.8 Control Chart Enhancements
308(3)
4.8.1 Sensitivity and Runs-type Signaling Rules
308(3)
Appendix 4.1 Shewhart Control Charts
311(1)
Appendix 4.1.1 The Shewhart-Prec Control Chart
311(1)
Appendix 4.2 CUSUM Control Charts
312(1)
Appendix 4.2.1 The CUSUM-EX Control Chart
312(2)
Appendix 4.2.2 The CUSUM-rank Control Chart
314(3)
Appendix 4.3 EWMA Control Charts
317(1)
Appendix 4.3.1 The EWMA-SN Control Chart
317(1)
Appendix 4.3.2 The EWMA-SR Control Chart
318(1)
Appendix 4.3.3 The EWMA-EX Control Chart
319(4)
Appendix 4.3.4 The EWMA-rank Control Chart
323(2)
5 Miscellaneous Univariate Distribution-free (Nonparametric) Variables Control Charts
325(88)
Chapter Overview
325(1)
5.1 Introduction
325(1)
5.2 Other Univariate Distribution-free (Nonparametric) Variables Control Charts
326(43)
5.2.1 Phase I Control Charts
326(1)
5.2.1.1 Introduction
326(5)
5.2.1.2 Phase I Control Charts for Location
331(12)
5.2.2 Special Cases of Precedence Charts
343(1)
5.2.2.1 The Min Chart
343(3)
5.2.2.2 The CUMIN Chart
346(2)
5.2.3 Control Charts Based on Bootstrapping
348(3)
5.2.3.1 Methodology
351(2)
5.2.4 Change-point Models
353(4)
5.2.5 Some Adaptive Charts
357(1)
5.2.5.1 Introduction
357(1)
5.2.5.2 Variable Sampling Interval (VSI) and Variable Sample Size (VSS) Charts
358(1)
5.2.5.3 Other Adaptive Schemes
359(1)
5.2.5.4 Properties and Performance Measures of Adaptive Charts
360(2)
5.2.5.5 Adaptive Nonparametric Control Charts
362(7)
Appendix A Tables
369(12)
Appendix B Programmes
381(32)
References 413(12)
Index 425
SUBHABRATA CHAKRABORTI, PHD is Professor of Statistics and Morrow Faculty Excellence Fellow at the University of Alabama, Tuscaloosa, AL , USA. He is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute. Professor Chakraborti has contributed in a number of research areas, including censored data analysis and income inference. His current research interests include development of statistical methods in general and nonparametric methods in particular for statistical process control. He has been a Fulbright Senior Scholar to South Africa and a visiting professor in several countries, including India, Holland and Brazil. Cited for his mentoring and collaborative work with students and scholars from around the world, Professor Chakraborti has presented seminars, delivered keynote/plenary addresses and conducted research workshops at various conferences.

MARIEN ALET GRAHAM, PHD is a senior lecturer at the Department of Science, Mathematics and Technology Education at the University of Pretoria, Pretoria, South Africa. She holds an Y1 rating from the South African National Research Foundation (NRF). Her current research interests are in Statistical Process Control, Nonparametric Statistics and Statistical Education. She has published several articles in international peer review journals and presented her work at various conferences.
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