You are previewing Statistical Methods for Quality Improvement, Third Edition.
O'Reilly logo
Statistical Methods for Quality Improvement, Third Edition

Book Description

Praise for the Second Edition

"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."

Technometrics

This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement

The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods.

In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include:

  • Updated coverage of control charts, with newly added tools

  • The latest research on the monitoring of linear profiles and other types of profiles

  • Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures

  • New discussions on design of experiments that include conditional effects and fraction of design space plots

  • New material on Lean Six Sigma and Six Sigma programs and training

Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic.

Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Contents
  5. Preface
  6. Preface to the Second Edition
  7. Preface to the First Edition
  8. PART I: Fundamental Quality Improvement and Statistical Concepts
    1. CHAPTER 1: Introduction
      1. 1.1 QUALITY AND PRODUCTIVITY
      2. 1.2 QUALITY COSTS (OR DOES IT?)
      3. 1.3 THE NEED FOR STATISTICAL METHODS
      4. 1.4 EARLY USE OF STATISTICAL METHODS FOR IMPROVING QUALITY
      5. 1.5 INFLUENTIAL QUALITY EXPERTS
      6. 1.6 SUMMARY
      7. REFERENCES
    2. CHAPTER 2: Basic Tools for Improving Quality
      1. 2.1 HISTOGRAM
      2. 2.2 PARETO CHARTS
      3. 2.3 SCATTER PLOTS
      4. 2.4 CONTROL CHART
      5. 2.5 CHECK SHEET
      6. 2.6 CAUSE-AND-EFFECT DIAGRAM
      7. 2.7 DEFECT CONCENTRATION DIAGRAM
      8. 2.8 THE SEVEN NEWER TOOLS
      9. 2.9 SOFTWARE
      10. 2.10 SUMMARY
      11. REFERENCES
      12. EXERCISES
    3. CHAPTER 3: Basic Concepts in Statistics and Probability
      1. 3.1 PROBABILITY
      2. 3.2 SAMPLE VERSUS POPULATION
      3. 3.3 LOCATION
      4. 3.4 VARIATION
      5. 3.5 DISCRETE DISTRIBUTIONS
      6. 3.6 CONTINUOUS DISTRIBUTIONS
      7. 3.7 CHOICE OF STATISTICAL DISTRIBUTION
      8. 3.8 STATISTICAL INFERENCE
      9. 3.9 ENUMERATIVE STUDIES VERSUS ANALYTIC STUDIES
      10. REFERENCES
      11. EXERCISES
  9. PART II: Control Charts and Process Capability
    1. CHAPTER 4: Control Charts for Measurements With Subgrouping (for One Variable)
      1. 4.1 BASIC CONTROL CHART PRINCIPLES
      2. 4.2 REAL-TIME CONTROL CHARTING VERSUS ANALYSIS OF PAST DATA
      3. 4.3 CONTROL CHARTS: WHEN TO USE, WHERE TO USE, HOW MANY TO USE
      4. 4.4 BENEFITS FROM THE USE OF CONTROL CHARTS
      5. 4.5 RATIONAL SUBGROUPS
      6. 4.6 BASIC STATISTICAL ASPECTS OF CONTROL CHARTS
      7. 4.7 ILLUSTRATIVE EXAMPLE
      8. 4.8 ILLUSTRATIVE EXAMPLE WITH REAL DATA
      9. 4.9 DETERMINING THE POINT OF A PARAMETER CHANGE
      10. 4.10 ACCEPTANCE SAMPLING AND ACCEPTANCE CONTROL CHART
      11. 4.11 MODIFIED LIMITS
      12. 4.12 DIFFERENCE CONTROL CHARTS
      13. 4.13 OTHER CHARTS
      14. 4.14 AVERAGE RUN LENGTH (ARL)
      15. 4.15 DETERMINING THE SUBGROUP SIZE
      16. 4.16 OUT-OF-CONTROL ACTION PLANS
      17. 4.17 ASSUMPTIONS FOR THE CHARTS IN THIS CHAPTER
      18. 4.18 MEASUREMENT ERROR
      19. 4.19 SOFTWARE
      20. 4.20 SUMMARY
      21. APPENDIX
      22. REFERENCES
      23. EXERCISES
    2. CHAPTER 5: Control Charts for Measurements Without Subgrouping (for One Variable)
      1. 5.1 INDIVIDUAL OBSERVATIONS CHART
      2. 5.2 TRANSFORM THE DATA OR FIT A DISTRIBUTION?
      3. 5.3 MOVING AVERAGE CHART
      4. 5.4 CONTROLLING VARIABILITY WITH INDIVIDUAL OBSERVATIONS
      5. 5.5 SUMMARY
      6. 5.6 APPENDIX
      7. REFERENCES
      8. EXERCISES
    3. CHAPTER 6: Control Charts for Attributes
      1. 6.1 CHARTS FOR NONCONFORMING UNITS
      2. 6.2 CHARTS FOR NONCONFORMITIES
      3. 6.3 SUMMARY
      4. REFERENCES
      5. EXERCISES
    4. CHAPTER 7: Process Capability
      1. 7.1 DATA ACQUISITION FOR CAPABILITY INDICES
      2. 7.2 PROCESS CAPABILITY INDICES
      3. 7.3 ESTIMATING THE PARAMETERS IN PROCESS CAPABILITY INDICES
      4. 7.4 DISTRIBUTIONAL ASSUMPTION FOR CAPABILITY INDICES
      5. 7.5 CONFIDENCE INTERVALS FOR PROCESS CAPABILITY INDICES
      6. 7.6 ASYMMETRIC BILATERAL TOLERANCES
      7. 7.7 CAPABILITY INDICES THAT ARE A FUNCTION OF PERCENT NONCONFORMING
      8. 7.8 MODIFIED k INDEX
      9. 7.9 OTHER APPROACHES
      10. 7.10 PROCESS CAPABILITY PLOTS
      11. 7.11 PROCESS CAPABILITY INDICES VERSUS PROCESS PERFORMANCE INDICES
      12. 7.12 PROCESS CAPABILITY INDICES WITH AUTOCORRELATED DATA
      13. 7.13 SOFTWARE FOR PROCESS CAPABILITY INDICES
      14. 7.14 SUMMARY
      15. REFERENCES
      16. EXERCISES
    5. CHAPTER 8: Alternatives to Shewhart Charts
      1. 8.1 INTRODUCTION
      2. 8.2 CUMULATIVE SUM PROCEDURES: PRINCIPLES AND HISTORICAL DEVELOPMENT
      3. 8.3 CUSUM PROCEDURES FOR CONTROLLING PROCESS VARIABILITY
      4. 8.4 APPLICATIONS OF CUSUM PROCEDURES
      5. 8.5 GENERALIZED LIKELIHOOD RATIO CHARTS: COMPETITIVE ALTERNATIVE TO CUSUM CHARTS
      6. 8.6 CUSUM PROCEDURES FOR NONCONFORMING UNITS
      7. 8.7 CUSUM PROCEDURES FOR NONCONFORMITY DATA
      8. 8.8 EXPONENTIALLY WEIGHTED MOVING AVERAGE CHARTS
      9. 8.9 SOFTWARE
      10. 8.10 SUMMARY
      11. REFERENCES
      12. EXERCISES
    6. CHAPTER 9: Multivariate Control Charts for Measurement and Attribute Data
      1. 9.1 HOTELLING'S T2 DISTRIBUTION
      2. 9.2 A T2 CONTROL CHART
      3. 9.3 MULTIVARIATE CHART VERSUS INDIVIDUAL X-CHARTS
      4. 9.4 CHARTS FOR DETECTING VARIABILITY AND CORRELATION SHIFTS
      5. 9.5 CHARTS CONSTRUCTED USING INDIVIDUAL OBSERVATIONS
      6. 9.6 WHEN TO USE EACH CHART
      7. 9.7 ACTUAL ALPHA LEVELS FOR MULTIPLE POINTS
      8. 9.8 REQUISITE ASSUMPTIONS
      9. 9.9 EFFECTS OF PARAMETER ESTIMATION ON ARLs
      10. 9.10 DIMENSION-REDUCTION AND VARIABLE SELECTION TECHNIQUES
      11. 9.11 MULTIVARIATE CUSUM CHARTS
      12. 9.12 MULTIVARIATE EWMA CHARTS
      13. 9.13 EFFECT OF MEASUREMENT ERROR
      14. 9.14 APPLICATIONS OF MULTIVARIATE CHARTS
      15. 9.15 MULTIVARIATE PROCESS CAPABILITY INDICES
      16. 9.16 SUMMARY
      17. APPENDIX
      18. REFERENCES
      19. EXERCISES
    7. CHAPTER 10: Miscellaneous Control Chart Topics
      1. 10.1 PRE-CONTROL
      2. 10.2 SHORT-RUN SPC
      3. 10.3 CHARTS FOR AUTOCORRELATED DATA
      4. 10.4 CHARTS FOR BATCH PROCESSES
      5. 10.5 CHARTS FOR MULTIPLE-STREAM PROCESSES
      6. 10.6 NONPARAMETRIC CONTROL CHARTS
      7. 10.7 BAYESIAN CONTROL CHART METHODS
      8. 10.8 CONTROL CHARTS FOR VARIANCE COMPONENTS
      9. 10.9 CONTROL CHARTS FOR HIGHLY CENSORED DATA
      10. 10.10 NEURAL NETWORKS
      11. 10.11 ECONOMIC DESIGN OF CONTROL CHARTS
      12. 10.12 CHARTS WITH VARIABLE SAMPLE SIZE AND/OR VARIABLE SAMPLING INTERVAL
      13. 10.13 USERS OF CONTROL CHARTS
      14. 10.14 SOFTWARE FOR CONTROL CHARTING
      15. BIBLIOGRAPHY
      16. EXERCISES
  10. PART III: Beyond Control Charts: Graphical and Statistical Methods
    1. CHAPTER 11: Graphical Methods
      1. 11.1 HISTOGRAM
      2. 11.2 STEM-AND-LEAF DISPLAY
      3. 11.3 DOT DIAGRAMS
      4. 11.4 BOXPLOT
      5. 11.5 NORMAL PROBABILITY PLOT
      6. 11.6 PLOTTING THREE VARIABLES
      7. 11.7 DISPLAYING MORE THAN THREE VARIABLES
      8. 11.8 PLOTS TO AID IN TRANSFORMING DATA
      9. 11.9 SUMMARY
      10. REFERENCES
      11. EXERCISES
    2. CHAPTER 12: Linear Regression
      1. 12.1 SIMPLE LINEAR REGRESSION
      2. 12.2 WORTH OF THE PREDICTION EQUATION
      3. 12.3 ASSUMPTIONS
      4. 12.4 CHECKING ASSUMPTIONS THROUGH RESIDUAL PLOTS
      5. 12.5 CONFIDENCE INTERVALS AND HYPOTHESIS TEST
      6. 12.6 PREDICTION INTERVAL FOR Y
      7. 12.7 REGRESSION CONTROL CHART
      8. 12.8 CAUSE-SELECTING CONTROL CHARTS
      9. 12.9 LINEAR, NONLINEAR, AND NONPARAMETRIC PROFILES
      10. 12.10 INVERSE REGRESSION
      11. 12.11 MULTIPLE LINEAR REGRESSION
      12. 12.12 ISSUES IN MULTIPLE REGRESSION
      13. 12.13 SOFTWARE FOR REGRESSION
      14. 12.14 SUMMARY
      15. REFERENCES
      16. EXERCISES
    3. CHAPTER 13: Design of Experiments
      1. 13.1 A SIMPLE EXAMPLE OF EXPERIMENTAL DESIGN PRINCIPLES
      2. 13.2 PRINCIPLES OF EXPERIMENTAL DESIGN
      3. 13.3 STATISTICAL CONCEPTS IN EXPERIMENTAL DESIGN
      4. 13.4 t-TESTS
      5. 13.5 ANALYSIS OF VARIANCE FOR ONE FACTOR
      6. 13.6 REGRESSION ANALYSIS OF DATA FROM DESIGNED EXPERIMENTS
      7. 13.7 ANOVA FOR TWO FACTORS
      8. 13.8 THE 23 DESIGN
      9. 13.9 ASSESSMENT OF EFFECTS WITHOUT A RESIDUAL TERM
      10. 13.10 RESIDUAL PLOT
      11. 13.11 SEPARATE ANALYSES USING DESIGN UNITS AND UNCODED UNITS
      12. 13.12 TWO-LEVEL DESIGNS WITH MORE THAN THREE FACTORS
      13. 13.13 THREE-LEVEL FACTORIAL DESIGNS
      14. 13.14 MIXED FACTORIALS
      15. 13.15 FRACTIONAL FACTORIALS
      16. 13.16 OTHER TOPICS IN EXPERIMENTAL DESIGN AND THEIR APPLICATIONS
      17. 13.17 SUMMARY
      18. REFERENCES
      19. EXERCISES
    4. CHAPTER 14: Contributions of Genichi Taguchi and Alternative Approaches
      1. 14.1 “TAGUCHI METHODS”
      2. 14.2 QUALITY ENGINEERING
      3. 14.3 LOSS FUNCTIONS
      4. 14.4 DISTRIBUTION NOT CENTERED AT THE TARGET
      5. 14.5 LOSS FUNCTIONS AND SPECIFICATION LIMITS
      6. 14.6 ASYMMETRIC LOSS FUNCTIONS
      7. 14.7 SIGNAL-TO-NOISE RATIOS AND ALTERNATIVES
      8. 14.8 EXPERIMENTAL DESIGNS FOR STAGE ONE
      9. 14.9 TAGUCHI METHODS OF DESIGN
      10. 14.10 DETERMINING OPTIMUM CONDITIONS
      11. 14.11 SUMMARY
      12. REFERENCES
      13. EXERCISES
    5. CHAPTER 15: Evolutionary Operation
      1. 15.1 EVOP ILLUSTRATIONS
      2. 15.2 THREE VARIABLES
      3. 15.3 SIMPLEX EVOP
      4. 15.4 OTHER EVOP PROCEDURES
      5. 15.5 MISCELLANEOUS USES OF EVOP
      6. 15.6 SUMMARY
      7. APPENDIX
      8. REFERENCES
      9. EXERCISES
    6. CHAPTER 16: Analysis of Means
      1. 16.1 ANOM FOR ONE-WAY CLASSIFICATIONS
      2. 16.2 ANOM FOR ATTRIBUTE DATA
      3. 16.3 ANOM WHEN STANDARDS ARE GIVEN
      4. 16.4 ANOM FOR FACTORIAL DESIGNS
      5. 16.5 ANOM WHEN AT LEAST ONE FACTOR HAS MORE THAN TWO LEVELS
      6. 16.6 USE OF ANOM WITH OTHER DESIGNS
      7. 16.7 NONPARAMETRIC ANOM
      8. 16.8 SUMMARY
      9. APPENDIX
      10. REFERENCES
      11. EXERCISES
    7. CHAPTER 17: Using Combinations of Quality Improvement Tools
      1. 17.1 CONTROL CHARTS AND DESIGN OF EXPERIMENTS
      2. 17.2 CONTROL CHARTS AND CALIBRATION EXPERIMENTS
      3. 17.3 SIX SIGMA PROGRAMS
      4. 17.4 STATISTICAL PROCESS CONTROL AND ENGINEERING PROCESS CONTROL
      5. REFERENCES
  11. Answers to Selected Exercises
  12. APPENDIX: Statistical Tables
  13. Author Index
  14. Subject Index