- Coverpage
- Titlepage
- Copyright
- About the Author
- Preface
- Contents
- PART 1 INTRODUCTION
- 1 QUALITY IMPROVEMENT IN THE MODERN BUSINESS ENVIRONMENT
- 2 THE DMAIC PROCESS
- PART 2 STATISTICAL METHODS USEFUL IN QUALITY CONTROL AND IMPROVEMENT
- 3 MODELING PROCESS QUALITY
- 4 INFERENCES ABOUT PROCESS QUALITY
- Chapter Overview and Learning Objectives
- 4.1 Statistics and Sampling Distributions
- 4.2 Point Estimation of Process Parameters
- 4.3 Statistical Inference for a Single Sample
- 4.3.1 Inference on the Mean of a Population, Variance Known
- 4.3.2 The Use of P-Values for Hypothesis Testing
- 4.3.3 Inference on the Mean of a Normal Distribution, Variance Unknown
- 4.3.4 Inference on the Variance of a Normal Distribution
- 4.3.5 Inference on a Population Proportion
- 4.3.6 The Probability of Type II Error and Sample Size Decisions
- 4.4 Statistical Inference for Two Samples
- 4.5 What If There Are More Than Two Populations? The Analysis of Variance
- 4.6 Linear Regression Models
- PART 3 BASIC METHODS OF STATISTICAL PROCESS CONTROL AND CAPABILITY ANALYSIS
- 5 METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL
- Chapter Overview and Learning Objectives
- 5.1 Introduction
- 5.2 Chance and Assignable Causes of Quality Variation
- 5.3 Statistical Basis of the Control Chart
- 5.4 The Rest of the Magnificent Seven
- 5.5 Implementing SPC in a Quality Improvement Program
- 5.6 An Application of SPC
- 5.7 Applications of Statistical Process Control and Quality Improvement Tools in Transactional and Service Businesses
- 6 CONTROL CHARTS FOR VARIABLES
- 7 CONTROL CHARTS FOR ATTRIBUTES
- 8 PROCESS AND MEASUREMENT SYSTEM CAPABILITY ANALYSIS
- Chapter Overview and Learning Objectives
- 8.1 Introduction
- 8.2 Process Capability Analysis Using a Histogram or a Probability Plot
- 8.3 Process Capability Ratios
- 8.4 Process Capability Analysis Using a Control Chart
- 8.5 Process Capability Analysis Using Designed Experiments
- 8.6 Process Capability Analysis with Attribute Data
- 8.7 Gauge and Measurement System Capability Studies
- 8.8 Setting Specification Limits on Discrete Components
- 8.9 Estimating the Natural Tolerance Limits of a Process
- 5 METHODS AND PHILOSOPHY OF STATISTICAL PROCESS CONTROL
- PART 4 OTHER STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES
- 9 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS
- Chapter Overview and Learning Objectives
- 9.1 The Cumulative Sum Control Chart
- 9.1.1 Basic Principles: The CUSUM Control Chart for Monitoring the Process Mean
- 9.1.2 The Tabular or Algorithmic CUSUM for Monitoring the Process Mean
- 9.1.3 Recommendations for CUSUM Design
- 9.1.4 The Standardized CUSUM
- 9.1.5 Improving CUSUM Responsiveness for Large Shifts
- 9.1.6 The Fast Initial Response or Headstart Feature
- 9.1.7 One-Sided CUSUMs
- 9.1.8 A CUSUM for Monitoring Process Variability
- 9.1.9 Rational Subgroups
- 9.1.10 CUSUMs for Other Sample Statistics
- 9.1.11 The V-Mask Procedure
- 9.1.12 The Self-Starting CUSUM
- 9.2 The Exponentially Weighted Moving Average Control Chart
- 9.3 The Moving Average Control Chart
- 10 OTHER UNIVARIATE STATISTICAL PROCESS-MONITORING AND CONTROL TECHNIQUES
- Chapter Overview and Learning Objectives
- 10.1 Statistical Process Control for Short Production Runs
- 10.2 Modified and Acceptance Control Charts
- 10.3 Control Charts for Multiple-Stream Processes
- 10.4 SPC With Autocorrelated Process Data
- 10.5 Adaptive Sampling Procedures
- 10.6 Economic Design of Control Charts
- 10.7 Cuscore Charts
- 10.8 The Changepoint Model for Process Monitoring
- 10.9 Profile Monitoring
- 10.10 Control Charts in Health Care Monitoring and Public Health Surveillance
- 10.11 Overview of Other Procedures
- 11 MULTIVARIATE PROCESS MONITORING AND CONTROL
- Chapter Overview and Learning Objectives
- 11.1 The Multivariate Quality-Control Problem
- 11.2 Description of Multivariate Data
- 11.3 The Hotelling T2 Control Chart
- 11.4 The Multivariate EWMA Control Chart
- 11.5 Regression Adjustment
- 11.6 Control Charts for Monitoring Variability
- 11.7 Latent Structure Methods
- 12 ENGINEERING PROCESS CONTROL AND SPC
- 9 CUMULATIVE SUM AND EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS
- PART 5 PROCESS DESIGN AND IMPROVEMENT WITH DESIGNED EXPERIMENTS
- 13 FACTORIAL AND FRACTIONAL FACTORIAL EXPERIMENTS FOR PROCESS DESIGN AND IMPROVEMENT
- 14 PROCESS OPTIMIZATION WITH DESIGNED EXPERIMENTS
- PART 6 ACCEPTANCE SAMPLING
- 15 LOT-BY-LOT ACCEPTANCE SAMPLING FOR ATTRIBUTES
- 16 OTHER ACCEPTANCE-SAMPLING TECHNIQUES
- APPENDIX
- I. Summary of Common Probability Distributions Often Used in Statistical Quality Control
- II. Cumulative Standard Normal Distribution
- III. Percentage Points of the X2 Distribution
- IV. Percentage Points of the t Distribution
- V. Percentage Points of the F Distribution
- VI. Factors for Constructing Variables Control Charts
- VII. Factors for Two-Sided Normal Tolerance Limits
- VIII. Factors for One-Sided Normal Tolerance Limits
- BIBLIOGRAPHY
- ANSWERS TO SELECTED EXERCISES
Appendix
I. Summary of Common Probability Distributions Often Used in Statistical Quality Control
II. Cumulative Standard Normal Distribution
III. Percentage Points of the χ2 Distribution
IV. Percentage Points of the t Distribution
V. Percentage Points of the F Distribution
VI. Factors for Constructing Variables Control Charts
VII. Factors for Two-Sided Normal Tolerance Limits
VIII. Factors for One-Sided Normal Tolerance Limits
APPENDIX I
Summary of Common Probability Distributions Often Used in Statistical Quality Control
APPENDIX II
Cumulative Standard Normal Distribution
APPENDIX III
Percentage Points of the χ2 Distribution
APPENDIX IV
Percentage Points of the t Distribution
APPENDIX V
Percentage Points of the F Distribution
APPENDIX VI
Factors for Constructing Variables Control Charts
APPENDIX VII
Factors for Two-Sided Normal Tolerance Limits
APPENDIX VIII
Factors for One-Sided Normal Tolerance Limits
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