Statistical Methods for Six Sigma. In R&D and Manufacturing

  • ID: 2245470
  • Book
  • 344 Pages
  • John Wiley and Sons Ltd
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A guide to achieving business successes through statistical methods

Statistical methods are a key ingredient in providing data–based guidance to research and development as well as to manufacturing. Understanding the concepts and specific steps involved in each statistical method is critical for achieving consistent and on–target performance.

Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and Manufacturing is specifically geared to engineers, scientists, technical managers, and other technical professionals in industry. Emphasizing practical learning, applications, and performance improvement, Dr. Joglekar’s text shows today’s industry professionals how to:

  • Summarize and interpret data to make decisions
  • Determine the amount of data to collect
  • Compare product and process designs
  • Build equations relating inputs and outputs
  • Establish specifications and validate processes
  • Reduce risk and cost–of–process control
  • Quantify and reduce economic loss due to variability
  • Estimate process capability and plan process improvements
  • Identify key causes and their contributions to variability
  • Analyze and improve measurement systems

This long–awaited guide for students and professionals in research, development, quality, and manufacturing does not presume any prior knowledge of statistics. It covers a large number of useful statistical methods compactly, in a language and depth necessary to make successful applications. Statistical methods in this book include: variance components analysis, variance transmission analysis, risk–based control charts, capability and performance indices, quality planning, regression analysis, comparative experiments, descriptive statistics, sample size determination, confidence intervals, tolerance intervals, and measurement systems analysis. The book also contains a wealth of case studies and examples, and features a unique test to evaluate the reader’s understanding of the subject.

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1. Introduction.

2. Basic Statistics.

2.1 Descriptive Statistics.

2.2 Statistical Distributions.

2.3 Confidence Intervals.

2.4 Sample Size.

2.5  Tolerance Intervals.

2.6 Normality, Independence and Homoscedasticity.

3. Comparative Experiments and Regression Analysis.

3.1 Hypothesis Testing Framework.

3.2 Comparing Single Population.

3.3 Comparing Two Populations.

3.4 Comparing Multiple Populations.

3.5 Correlation.

3.6 Regression Analysis.

4. Control Charts.

4.1 Role of Control Charts.

4.2 Logic of Control Limits.

4.3 Variable Control Charts.

4.4 Attribute Control Charts.

4.5 Interpreting Control Charts.

4.6 Key Success Factors.

5. Process Capability.

5.1 Capability and Performance Indices.

5.2 E stimating Capability and Performance Indices.

5.3 Six–Sigma Goal.

5.4 Planning for Improvement.

6. Other Useful Charts.

6.1 Risk–based Control Ch arts.

6.2 Modified Control Limit   Chart.

6.3 Moving Average Control Chart.

6.4 Short Run Control Charts

6.5 Charts for Non–Normal Distributions.

7. Variance Components Analysis.

7.1   Chart (Random Factor).

7.2 One–way Classification (Fixed Factor).

7.3 Structured Studies and Variance Components.

8. Quality Planning with Variance Components.

8.1 Typical Manufacturing Application.

8.2 Economic Loss Functions.

8.3 Planning for Quality Improvement.

8.4 Application to Multi–Lane Manufacturing Process.

8.5 Variance Transmission Analysis.

8.6 Application to a Factorial Design.

8.7 Variance Components and Specifications.

9. Measurement Systems Analysis.

9.1 Statistical Properties of Measurement Systems.

9.2 Acceptance Criteria.

9.3 Calibration Study.

9.4 Stability and Bias Study.

9.5 Repeatability and Reproducibility (R&R) Study.

9.6 Robustness and Intermediate Precision Studies.

9.7 Linearity Study.

9.8 Method Transfer Study.

9.9 Calculating Significant Figures.

10. What Color is Your Belt?

10.1 Test.

10.2 Answers.

Appendix A: Tail Area of Unit Normal Distribution.

Appendix B: Probability Points of the t Distribution with v Degrees of Freedom.

Appendix C: Probability Points of the x2 Distribution with v Degrees of Freedom.

Appendix D1. Values for Two–Sided Normal Tolerance Limits.

Appendix D2. Values for One–Sided Normal Tolerance Limits.

Appendix E1: Percentage Points of the F Distribution: Upper 5% Points.

Appendix E2: Percentage Points of the F Distribution: Upper 2.5% Points.

Appendix F: Critical Values of Hartley′s Maximum F Ratio Test for Homogeneity of Variances.

Appendix G: Table of Control Chart Constants.

Glossary Of Symbols.



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"I highly recommend this book to anyone interested in applying statistics to solve problems." (Journal of Food Quality, October 2004)

" an interesting collection of material in nice summary form " (Journal of the American Statistical Association, December 2004)

"Overall, Statistical Methods for Six Sigma in R & D and Manufacturingoffers some good insights and practical views of the statistical concepts covered." (Technometrics, August 2004, Vol. 46, No. 3)

"...covers a large number of useful statistical methods compactly...contains a wealth of case studies and examples..." (Food Trade Review, May 2004)

...can be used as a reference or as a self–study...also as a textbook for an engineering statistics course...recommended... (E–Streams, Vol. 7, No. 3)

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