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E-raamat: Introduction to Statistical Process Control [Taylor & Francis e-raamat]

(University of Florida, Gainesville, USA)
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Introduction to Statistical Process ControlSuurem pilt
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
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 authora leading researcher on SPCshows 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 authors 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.
List of Figures xv
List of Tables xxix
Preface xxxv
1 Introduction 1(10)
1.1 Quality and the Early History of Quality Improvement
1(2)
1.2 Quality Management
3(3)
1.3 Statistical Process Control
6(2)
1.4 Organization of the Book
8(1)
1.5 Exercises
9(2)
2 Basic Statistical Concepts and Methods 11(62)
2.1 Introduction
11(1)
2.2 Population and Population Distribution
11(3)
2.3 Important Continuous Distributions
14(5)
2.3.1 Normal distribution
15(1)
2.3.2 Chi-square distribution
15(1)
2.3.3 t distribution
16(2)
2.3.4 F distribution
18(1)
2.3.5 Weibull distribution and exponential distribution
18(1)
2.4 Important Discrete Distributions
19(2)
2.4.1 Binary variable and Bernoulli distribution
19(1)
2.4.2 Binomial and multinomial distributions
19(1)
2.4.3 Geometric distribution
20(1)
2.4.4 Hypergeometric distribution
20(1)
2.4.5 Poisson distribution
21(1)
2.5 Data and Data Description
21(4)
2.6 Tabular and Graphical Methods for Describing Data
25(8)
2.6.1 Frequency table, pie chart, and bar chart
25(1)
2.6.2 Dot plot, stem-and-leaf plot, and box plot
26(2)
2.6.3 Frequency histogram and density histogram
28(5)
2.7 Parametric Statistical Inferences
33(17)
2.7.1 Point estimation and sampling distribution
33(5)
2.7.2 Maximum likelihood estimation and least squares estimation
38(3)
2.7.3 Confidence intervals and hypothesis testing
41(8)
2.7.4 The delta method and the bootstrap method
49(1)
2.8 Nonparametric Statistical Inferences
50(17)
2.8.1 Order statistics and their properties
51(3)
2.8.2 Goodness-of-fit tests
54(2)
2.8.3 Rank tests
56(5)
2.8.4 Nonparametric density estimation
61(1)
2.8.5 Nonparametric regression
62(5)
2.9 Exercises
67(6)
3 Univariate Shewhart Charts and Process Capability 73(46)
3.1 Introduction
73(1)
3.2 Shewhart Charts for Numerical Variables
74(17)
3.2.1 The X and R charts
74(10)
3.2.2 The X and s charts
84(4)
3.2.3 The X and R charts for monitoring individual observations
88(3)
3.3 Shewhart Charts for Categorical Variables
91(11)
3.3.1 The p chart and mp chart
91(6)
3.3.2 The c chart, u chart, and D chart
97(5)
3.4 Process Capability Analysis
102(8)
3.4.1 Process capability and its measurement
102(1)
3.4.2 Process capability ratios
103(7)
3.5 Some Discussions
110(2)
3.6 Exercises
112(7)
4 Univariate CUSUM Charts 119(62)
4.1 Introduction
119(2)
4.2 Monitoring the Mean of a Normal Process
121(23)
4.2.1 The V-mask and decision interval forms of the CUSUM chart
121(5)
4.2.2 Design and implementation of the CUSUM chart
126(9)
4.2.3 Cases with correlated observations
135(6)
4.2.4 Optimality of the CUSUM chart
141(3)
4.3 Monitoring the Variance of a Normal Process
144(10)
4.3.1 Process variability and quality of products
144(2)
4.3.2 CUSUM charts for monitoring process variance
146(5)
4.3.3 Joint monitoring of process mean and variance
151(3)
4.4 CUSUM Charts for Distributions in Exponential Family
154(8)
4.4.1 Cases with some continuous distributions in the exponential family
155(3)
4.4.2 Cases with discrete distributions in the exponential family
158(4)
4.5 Self-Starting and Adaptive CUSUM Charts
162(7)
4.5.1 Self-Starting CUSUM charts
162(6)
4.5.2 Adaptive CUSUM charts
168(1)
4.6 Some Theory for Computing ARL Values
169(4)
4.6.1 The Markov chain approach
170(2)
4.6.2 The integral equations approach
172(1)
4.7 Some Discussions
173(1)
4.8 Exercises
174(7)
5 Univariate EWMA Charts 181(44)
5.1 Introduction
181(1)
5.2 Monitoring the Mean of a Normal Process
182(16)
5.2.1 Design and implementation of the EWMA chart
182(9)
5.2.2 Cases with correlated observations
191(2)
5.2.3 Comparison with CUSUM charts
193(5)
5.3 Monitoring the Variance of a Normal Process
198(13)
5.3.1 Monitoring the process variance
199(6)
5.3.2 Joint monitoring of the process mean and variance
205(6)
5.4 Self-Starting and Adaptive EWMA Charts
211(8)
5.4.1 Self-starting EWMA charts
211(3)
5.4.2 Adaptive EWMA charts
214(5)
5.5 Some Discussions
219(2)
5.6 Exercises
221(4)
6 Univariate Control Charts by Change-Point Detection 225(32)
6.1 Introduction
225(1)
6.2 Univariate Change-Point Detection
226(7)
6.2.1 Detection of a single change-point
226(4)
6.2.2 Detection of multiple change-points
230(3)
6.3 Control Charts by Change-Point Detection
233(19)
6.3.1 Monitoring of the process mean
234(7)
6.3.2 Monitoring of the process variance
241(6)
6.3.3 Monitoring of both the process mean and variance
247(5)
6.4 Some Discussions
252(2)
6.5 Exercises
254(3)
7 Multivariate Statistical Process Control 257(58)
7.1 Introduction
257(1)
7.2 Multivariate Shewhart Charts
258(13)
7.2.1 Multivariate normal distributions and some basic properties
258(6)
7.2.2 Some multivariate Shewhart charts
264(7)
7.3 Multivariate CUSUM Charts
271(13)
7.3.1 MCUSUM charts for monitoring the process mean
271(10)
7.3.2 MCUSUM charts for monitoring the process covariance matrix
281(3)
7.4 Multivariate EWMA Charts
284(10)
7.4.1 MEWMA charts for monitoring the process mean
284(5)
7.4.2 MEWMA charts for monitoring the process covariance matrix
289(5)
7.5 Multivariate Control Charts by Change-Point Detection
294(5)
7.6 Multivariate Control Charts by LASSO
299(7)
7.6.1 LASSO for regression variable selection
300(1)
7.6.2 A LASSO-based MEWMA chart
300(6)
7.7 Some Discussions
306(2)
7.8 Exercises
308(7)
8 Univariate Nonparametric Process Control 315(48)
8.1 Introduction
315(2)
8.2 Rank-Based Nonparametric Control Charts
317(24)
8.2.1 Nonparametric Shewhart charts
317(7)
8.2.2 Nonparametric CUSUM charts
324(6)
8.2.3 Nonparametric EWMA charts
330(8)
8.2.4 Nonparametric CPD charts
338(3)
8.3 Nonparametric SPC by Categorical Data Analysis
341(15)
8.3.1 Process monitoring by categorizing process observations
342(6)
8.3.2 Alternative control charts and some comparisons
348(8)
8.4 Some Discussions
356(2)
8.5 Exercises
358(5)
9 Multivariate Nonparametric Process Control 363(44)
9.1 Introduction
363(3)
9.2 Rank-Based Multivariate Nonparametric Control Charts
366(21)
9.2.1 Control charts based on longitudinal ranking
366(10)
9.2.2 Control charts based on cross-component ranking
376(11)
9.3 Multivariate Nonparametric SPC by Log-Linear Modeling
387(14)
9.3.1 Analyzing categorical data by log-linear modeling
389(3)
9.3.2 Nonparametric SPC by log-linear modeling
392(9)
9.4 Some Discussions
401(1)
9.5 Exercises
402(5)
10 Profile Monitoring 407(30)
10.1 Introduction
407(1)
10.2 Parametric Profile Monitoring
408(15)
10.2.1 Linear profile monitoring
408(10)
10.2.2 Nonlinear profile monitoring
418(5)
10.3 Nonparametric Profile Monitoring
423(10)
10.3.1 Nonparametric mixed-effects modeling
424(4)
10.3.2 Phase II nonparametric profile monitoring
428(5)
10.4 Some Discussions
433(1)
10.5 Exercises
434(3)
A R Functions for SPC 437(10)
A.1 Basic R Functions
437(3)
A.2 R Packages for SPC
440(1)
A.3 List of R Functions Used in the Book
440(7)
B Datasets Used in the Book 447(4)
Bibliography 451(26)
Index 477
Peihua Qiu, Ph.D., is the founding chair of the Department of Biostatistics at the University of Florida. He was previously a professor in the School of Statistics at the University of Minnesota. He is the editor of Technometrics, an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. His research focuses on jump regression analysis, medical image analysis, statistical methods for monitoring processes, and patient survival data analysis.
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