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Statistical methods for quality improvement / Thomas P. Ryan.

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"Praise for the Second Edition"As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available."--TechnometricsThis new edition continues to provide the most current, proven statistical methods for quality control and quality impro...

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Bibliographic Details
Online Access: Full Text (via Skillsoft)
Main Author: Ryan, Thomas P., 1945-
Format: Electronic eBook
Language:English
Published: Hoboken, N.J. : Wiley, 2011.
Edition:3rd ed.
Series:Wiley series in probability and statistics.
Subjects:
Quality control > Statistical methods.
Process control > Statistical methods.
Process control > Statistical methods
Quality control > Statistical methods
Table of Contents:
  • Frontmatter
  • Fundamental Quality Improvement and Statistical Concepts. Introduction
  • Basic Tools for Improving Quality
  • Basic Concepts in Statistics and Probability
  • Control Charts and Process Capability. Control Charts for Measurements with Subgrouping (for One Variable)
  • Control Charts for Measurements without Subgrouping (for One Variable)
  • Control Charts for Attributes
  • Process Capability
  • Alternatives to Shewhart Charts
  • Multivariate Control Charts for Measurement and Attribute Data
  • Miscellaneous Control Chart Topics
  • Beyond Control Charts: Graphical and Statistical Methods. Graphical Methods
  • Linear Regression
  • Design of Experiments
  • Contributions of Genichi Taguchi and Alternative Approaches
  • Evolutionary Operation
  • Analysis of Means
  • Using Combinations of Quality Improvement Tools
  • Answers to Selected Exercises
  • Appendix: Statistical Tables
  • Author Index
  • Subject Index
  • Wiley Series in Probability and Statistics.
  • Contents note continued: 9.9. Effects of Parameter Estimation on ARLs
  • 9.10. Dimension-Reduction and Variable Selection Techniques
  • 9.11. Multivariate CUSUM Charts
  • 9.12. Multivariate EWMA Charts
  • 9.12.1. Design of a MEWMA Chart
  • 9.12.2. Searching for Assignable Causes
  • 9.12.3. Unequal Sample Sizes
  • 9.12.4. Self-Starting MEWMA Chart
  • 9.12.5. Combinations of MEWMA Charts and Multivariate Shewhart Charts
  • 9.12.6. MEWMA Chart with Sequential Sampling
  • 9.12.7. MEWMA Chart for Process Variability
  • 9.13. Effect of Measurement Error
  • 9.14. Applications of Multivariate Charts
  • 9.15. Multivariate Process Capability indexes
  • 9.16. Summary
  • Appendix
  • References
  • Exercises
  • 10. Miscellaneous Control Chart Topics
  • 10.1. Pre-control
  • 10.2. Short-Run SPC
  • 10.3. Charts for Autocorrelated Data
  • 10.3.1. Autocorrelated Attribute Data
  • 10.4. Charts for Batch Processes
  • 10.5. Charts for Multiple-Stream Processes
  • 10.6. Nonparametric Control Charts
  • 10.7. Bayesian Control Chart Methods
  • 10.8. Control Charts for Variance Components
  • 10.9. Control Charts for Highly Censored Data
  • 10.10. Neural Networks
  • 10.11. Economic Design of Control Charts
  • 10.11.1. Economic-Statistical Design
  • 10.12. Charts with Variable Sample Size and/or Variable Sampling Interval
  • 10.13. Users of Control Charts
  • 10.13.1. Control Chart Nonmanufacturing Applications
  • 10.13.1.1. Healthcare
  • 10.13.1.2. Financial
  • 10.13.1.3. Environmental
  • 10.13.1.4. Clinical Laboratories
  • 10.13.1.5. Analytical Laboratories
  • 10.13.1.6. Civil Engineering
  • 10.13.1.7. Education
  • 10.13.1.8. Law Enforcement/Investigative Work
  • 10.13.1.9. Lumber
  • 10.13.1.10. Forest Operations
  • 10.13.1.11. Athletic Performance
  • 10.13.1.12. Animal Production Systems
  • 10.14. Software for Control Charting
  • Bibliography
  • Exercises
  • pt. III BEYOND CONTROL CHARTS: GRAPHICAL AND STATISTICAL METHODS
  • 11. Graphical Methods
  • 11.1. Histogram
  • 11.2. Stem-and-Leaf Display
  • 11.3. Dot Diagrams
  • 11.3.1. Digidot Plot
  • 11.4. Boxplot
  • 11.5. Normal Probability Plot
  • 11.6. Plotting Three Variables
  • 11.7. Displaying More Than Three Variables
  • 11.8. Plots to Aid in Transforming Data
  • 11.9. Summary
  • References
  • Exercises
  • 12. Linear Regression
  • 12.1. Simple Linear Regression
  • 12.2. Worth of the Prediction Equation
  • 12.3. Assumptions
  • 12.4. Checking Assumptions Through Residual Plots
  • 12.5. Confidence Intervals and Hypothesis Test
  • 12.6. Prediction Interval for Y
  • 12.7. Regression Control Chart
  • 12.8. Cause-Selecting Control Charts
  • 12.9. Linear, Nonlinear, and Nonparametric Profiles
  • 12.10. Inverse Regression
  • 12.11. Multiple Linear Regression
  • 12.12. Issues in Multiple Regression
  • 12.12.1. Variable Selection
  • 12.12.2. Extrapolation
  • 12.12.3. Multicollinear Data
  • 12.12.4. Residual Plots
  • 12.12.5. Regression Diagnostics
  • 12.12.6. Transformations
  • 12.13. Software For Regression
  • 12.14. Summary
  • References
  • Exercises
  • 13. Design of Experiments
  • 13.1. Simple Example of Experimental Design Principles
  • 13.2. Principles of Experimental Design
  • 13.3. Statistical Concepts in Experimental Design
  • 13.4. t-Tests
  • 13.4.1. Exact t-Test
  • 13.4.2. Approximate t-Test
  • 13.4.3. Confidence Intervals for Differences
  • 13.5. Analysis of Variance for One Factor
  • 13.5.1. ANOVA for a Single Factor with More Than Two Levels
  • 13.5.2. Multiple Comparison Procedures
  • 13.5.3. Sample Size Determination
  • 13.5.4. Additional Terms and Concepts in One-Factor ANOVA
  • 13.6. Regression Analysis of Data from Designed Experiments
  • 13.7. ANOVA for Two Factors
  • 13.7.1. ANOVA with Two Factors: Factorial Designs
  • 13.7.1.1. Conditional Effects
  • 13.7.2. Effect Estimates
  • 13.7.3. ANOVA Table for Unreplicated Two-Factor Design
  • 13.7.4. Yates's Algorithm
  • 13.8. 23 Design
  • 13.9. Assessment of Effects Without a Residual Term
  • 13.10. Residual Plot
  • 13.11. Separate Analyses Using Design Units and Uncoded Units
  • 13.12. Two-Level Designs with More Than Three Factors
  • 13.13. Three-Level Factorial Designs
  • 13.14. Mixed Factorials
  • 13.15. Fractional Factorials
  • 13.15.1. 2k-1 Designs
  • 13.15.2. 2k-2 Designs
  • 13.15.3. More Highly Fractionated Two-Level Designs
  • 13.15.4. Fractions of Three-Level Factorials
  • 13.15.5. Incomplete Mixed Factorials
  • 13.15.6. Cautions
  • 13.16. Other Topics in Experimental Design and Their Applications
  • 13.16.1. Hard-to-Change Factors
  • 13.16.2. Split-Lot Designs
  • 13.16.3. Mixture Designs
  • 13.16.4. Response Surface Designs
  • 13.16.5. Designs for Measurement System Evaluation
  • 13.16.6. Fraction of Design Space Plots
  • 13.16.7. Computer-Aided Design and Expert Systems
  • 13.16.8. Sequential Experimentation
  • 13.16.9. Supersaturated Designs and Analyses
  • 13.16.10. Multiple Responses
  • 13.17. Summary
  • References
  • Exercises
  • 14. Contributions of Genichi Taguchi and Alternative Approaches
  • 14.1. "Taguchi Methods"
  • 14.2. Quality Engineering
  • 14.3. Loss Functions
  • 14.4. Distribution Not Centered at the Target
  • 14.5. Loss Functions and Specification Limits
  • 14.6. Asymmetric Loss Functions
  • 14.7. Signal-to-Noise Ratios and Alternatives
  • 14.8. Experimental Designs for Stage One
  • 14.9. Taguchi Methods of Design
  • 14.9.1. Inner Arrays and Outer Arrays
  • 14.9.2. Orthogonal Arrays as Fractional Factorials
  • 14.9.3. Other Orthogonal Arrays Versus Fractional Factorials
  • 14.9.4. Product Arrays Versus Combined Arrays
  • 14.9.5. Application of Product Array
  • 14.9.5.1. Cautions
  • 14.9.6. Desirable Robust Designs and Analyses
  • 14.9.6.1. Designs
  • 14.9.6.2. Analyses
  • 14.9.6.3. Experiment to Compare Product Array and Combined Array
  • 14.10. Determining Optimum Conditions
  • 14.11. Summary
  • References
  • Exercises
  • 15. Evolutionary Operation
  • 15.1. EVOP Illustrations
  • 15.2. Three Variables
  • 15.3. Simplex EVOP
  • 15.4. Other EVOP Procedures
  • 15.5. Miscellaneous Uses of EVOP
  • 15.6. Summary
  • Appendix
  • 15.A. Derivation of Formula for Estimating σ
  • References
  • Exercises
  • 16. Analysis of Means
  • 16.1. ANOM for One-Way Classifications
  • 16.2. ANOM for Attribute Data
  • 16.2.1. Proportions
  • 16.2.2. Count Data
  • 16.3. ANOM When Standards Are Given
  • 16.3.1. Nonconforming Units
  • 16.3.2. Nonconformities
  • 16.3.3. Measurement Data
  • 16.4. ANOM for Factorial Designs
  • 16.4.1. Assumptions
  • 16.4.2. Alternative Way of Displaying Interaction Effects
  • 16.5. ANOM When at Least One Factor Has More Than Two Levels
  • 16.5.1. Main Effects
  • 16.5.2. Interaction Effects
  • 16.6. Use of ANOM with Other Designs
  • 16.7. Nonparametric ANOM
  • 16.8. Summary
  • Appendix
  • References
  • Exercises
  • 17. Using Combinations of Quality Improvement Tools
  • 17.1. Control Charts and Design of Experiments
  • 17.2. Control Charts and Calibration Experiments
  • 17.3. Six Sigma Programs
  • 17.3.1. Components of a Six Sigma Program
  • 17.3.2. Six Sigma Applications and Programs
  • 17.3.3. Six Sigma Concept for Customer Satisfaction
  • 17.3.4. Six Sigma Training
  • 17.3.5. Lean Six Sigma
  • 17.3.6. Related Programs/Other Companies
  • 17.3.6.1. SEMATECH's Qual Plan
  • 17.3.6.2. AlliedSignal's Operational Excellence Program
  • 17.4. Statistical Process Control and Engineering Process Control
  • References.

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