Buch, Englisch, 368 Seiten, Format (B × H): 155 mm x 231 mm, Gewicht: 499 g
ISBN: 978-0-470-14832-7
Verlag: Wiley
Praise for the First Edition
"This book. is a significant addition to the literature on statistical practice. should be of considerable interest to those interested in these topics."
—International Journal of Forecasting
Recent research has shown that monitoring techniques alone are inadequate for modern Statistical Process Control (SPC), and there exists a need for these techniques to be augmented by methods that indicate when occasional process adjustment is necessary. Statistical Control by Monitoring and Adjustment, Second Edition presents the relationship among these concepts and elementary ideas from Engineering Process Control (EPC), demonstrating how the powerful synergistic association between SPC and EPC can solve numerous problems that are frequently encountered in process monitoring and adjustment.
The book begins with a discussion of SPC as it was originally conceived by Dr. Walter A. Shewhart and Dr. W. Edwards Deming. Subsequent chapters outline the basics of the new integration of SPC and EPC, which is not available in other related books. Thorough coverage of time series analysis for forecasting, process dynamics, and non-stationary models is also provided, and these sections have been carefully written so as to require only an elementary understanding of mathematics. Extensive graphical explanations and computational tables accompany the numerous examples that are provided throughout each chapter, and a helpful selection of problems and solutions further facilitates understanding.
Statistical Control by Monitoring and Adjustment, Second Edition is an excellent book for courses on applied statistics and industrial engineering at the upper-undergraduate and graduate levels. It also serves as a valuable reference for statisticians and quality control practitioners working in industry.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Preface xi
1 Introduction and Revision of Some Statistical Ideas 1
1.1 Necessity for Process Control 1
1.2 SPC and EPC 1
1.3 Process Monitoring Without a Model 3
1.4 Detecting a Signal in Noise 4
1.5 Measurement Data 4
1.6 Two Important Characteristics of a Probability Distribution 5
1.7 Normal Distribution 6
1.8 Normal Distribution Defined by µ and s 6
1.9 Probabilities Associated with Normal Distribution 7
1.10 Estimating Mean and Standard Deviation from Data 8
1.11 Combining Estimates of s2 9
1.12 Data on Frequencies (Events): Poisson Distribution 10
1.13 Normal Approximation to Poisson Distribution 12
1.14 Data on Proportion Defective: Binomial Distribution 12
1.15 Normal Approximation to Binomial Distribution 14
Appendix 1A: Central Limit Effect 15
Problems 17
2 Standard Control Charts Under Ideal Conditions As a First Approximation 21
2.1 Control Charts for Process Monitoring 21
2.2 Control Chart for Measurement (Variables) Data 22
2.3 Shewhart Charts for Sample Average and Range 24
2.4 Shewhart Chart for Sample Range 26
2.5 Process Monitoring With Control Charts for Frequencies 29
2.6 Data on Frequencies (Counts): Poisson Distribution 30
2.7 Common Causes and Special Causes 34
2.8 For What Kinds of Data Has the c Chart Been Used? 36
2.9 Quality Control Charts for Proportions: p Chart 37
2.10 EWMA Chart 40
2.11 Process Monitoring Using Cumulative Sums 46
2.12 Specification Limits, Target Accuracy, and Process Capability 53
2.13 How Successful Process Monitoring can Improve Quality 56
Problems 57
3 What Can Go Wrong and What Can We Do About It? 61
3.1 Introduction 61
3.2 Measurement Charts 64
3.3 Need for Time Series Models 65
3.4 Types of Variation 65
3.5 Nonstationary