Skip to main content
SpringerLink
Log in

Experimental Design and Analysis for Process Improvement Part 2: Advanced Topics

  • Chapter
  • First Online:
Statistical Methods for Quality Assurance

Part of the book series: Springer Texts in Statistics ((STS))

  • 3306 Accesses

Abstract

The basic tools of experimental design and analysis provided in Chap. 5 form a foundation for effective multifactor experimentation. This chapter builds on that and provides some of the superstructure of statistical methods for process-improvement experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  1. Anand, K. N., Bhadkamkar, S. M., & Moghe, R. (1994–1995). “Wet method of chemical analysis of cast iron: upgrading accuracy and precision through experimental design.” Quality Engineering, 7(1), 187–208.

    Google Scholar 

  2. Bisgaard, S. (1994). “Blocking generators for small 2k−p designs.” Journal of Quality Technology, 26(4), 288–296.

    MathSciNet  Google Scholar 

  3. Bisgaard, S., & Fuller, H. T. (1995–1996). “Reducing variation with two-level factorial experiments.” Quality Engineering, 8(2), 373–377.

    Google Scholar 

  4. Box, G. E. P., & Draper, N. R. (1969). Evolutionary operation. New York: Wiley.

    Google Scholar 

  5. Box, G. E. P., & Draper, N. R. (1986). Empirical model building and response surfaces. New York: Wiley.

    MATH  Google Scholar 

  6. Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1978). Statistics for experimenters. New York: Wiley.

    MATH  Google Scholar 

  7. Brassard, M. (Ed.). (1984). Proceedings–case study seminar–Dr. Deming’s management methods: How they are being implemented in the U.S. and abroad. Andover, MA: Growth Opportunity Alliance of Lawrence.

    Google Scholar 

  8. Brezler, P. (1986). “Statistical analysis: Mack Truck gear heat treat experiments.” Heat Treating, 18(11), 26–29.

    Google Scholar 

  9. Brown, D. S., Turner, W. R., & Smith, A. C. (1958). “Sealing strength of wax-polyethylene blends.” Tappi, 41(6), 295–300.

    Google Scholar 

  10. Burdick, R. K., & Graybill, F. A. (1992). Confidence intervals on variance components. New York: Marcel Dekker.

    MATH  Google Scholar 

  11. Burns, W. L. (1989–90). “Quality control proves itself in assembly.” Quality Engineering, 2(1), 91–101.

    Google Scholar 

  12. Burr, I. W. (1953). Engineering statistics and quality control. New York: McGraw-Hill.

    MATH  Google Scholar 

  13. Burr, I. W. (1979). Elementary statistical quality control. New York: Marcel Dekker.

    MATH  Google Scholar 

  14. Champ, C. W., & Woodall, W. H. (1987). “Exact results for shewhart control charts with supplementary runs rules.” Technometrics, 29(4), 393–399.

    Article  MATH  Google Scholar 

  15. Champ, C. W., & Woodall, W. H. (1990). “A program to evaluate the run length distribution of a shewhart control chart with supplementary runs rules.” Journal of Quality Technology, 22(1), 68–73.

    Google Scholar 

  16. Chau, K. W., & Kelley, W. R. (1993). “Formulating printable coatings via d-optimality.” Journal of Coatings Technology, 65(821), 71–78.

    Google Scholar 

  17. Currie, L. A. (1968). “Limits for qualitative detection and quantitative determination.” Analytical Chemistry, 40(3), 586–593.

    Article  Google Scholar 

  18. Crowder, S. V., Jensen, K. L., Stephenson, W. R., & Vardeman, S. B. (1988). “An interactive program for the analysis of data from two-level factorial experiments via probability plotting.” Journal of Quality Technology, 20(2), 140–148.

    Google Scholar 

  19. Duncan, A. J. (1986). Quality control and industrial statistics (5th ed.). Homewood, IL: Irwin.

    MATH  Google Scholar 

  20. Eibl, S., Kess, U., & Pukelsheim, F. (1992). “Achieving a target value for a manufacturing process: a case study.” Journal of Quality Technology, 24(1), 22–26.

    Google Scholar 

  21. Ermer, D. S., & Hurtis, G. M. (1995–1996). “Advanced SPC for higher-quality electronic card manufacturing.” Quality Engineering, 8(2), 283–299.

    Google Scholar 

  22. Grego, J. M. (1993). “Generalized linear models and process variation.” Journal of Quality Technology, 25(4), 288–295.

    Google Scholar 

  23. Hahn, J. G., & Meeker, W. Q. (1991). Statistical intervals: a guide for practitioners. New York: Wiley.

    Book  MATH  Google Scholar 

  24. Hendrix, C. D. (1979). “What every technologist should know about experimental design.” Chemical Technology, 9(3), 167–174.

    Google Scholar 

  25. Hill, W. J., & Demler, W. R. (1970). “More on planning experiments to increase research efficiency.” Industrial and Engineering Chemistry, 62(10), 60–65.

    Article  Google Scholar 

  26. Kolarik, W. J. (1995). Creating quality: concepts, systems, strategies and tools. New York: McGraw-Hill.

    Google Scholar 

  27. Lawson, J. S. (1990–1991). “Improving a chemical process through use of a designed experiment.” Quality Engineering, 3(2), 215–235.

    Google Scholar 

  28. Lawson, J. S., & Madrigal, J. L. (1994). “Robust design through optimization techniques.” Quality Engineering, 6(4), 593–608.

    Google Scholar 

  29. Leigh, H. D., & Taylor, T. D. (1990). “Computer-generated experimental designs.” Ceramic Bulletin, 69(1), 100–106.

    Google Scholar 

  30. Lochner, R. H., & Matar, J. E. (1990). Designing for quality: an introduction to the best of Taguchi and Western methods of statistical experimental design. London and New York: Chapman and Hall.

    Google Scholar 

  31. Mielnik, E. M. (1993–1994). “Design of a metal-cutting drilling experiment: a discrete two-variable problem.” Quality Engineering, 6(1), 71–98.

    Google Scholar 

  32. Miller, A., Sitter, R. R., Wu, C. F. J., & Long, D. (1993–1994). “Are large taguchi-style experiments necessary? A reanalysis of gear and pinion data.” Quality Engineering, 6(1), 21–37.

    Google Scholar 

  33. Moen, R. D., Nolan, T. W., & Provost, L. P. (1991). Improving quality through planned experimentation. New York: McGraw-Hill.

    Google Scholar 

  34. Myers, R. H. (1976). Response surface methodology. Ann Arbor: Edwards Brothers.

    Google Scholar 

  35. Nair, V. N. (Ed.) (1992). “Taguchi’s parameter design: a panel discussion.” Technometrics, 34(2), 127–161.

    Google Scholar 

  36. Nelson, L. S. (1984). “The Shewhart control chart-tests for special causes.” Journal of Quality Technology, 16(4), 237–239.

    Google Scholar 

  37. Neter, J., Kutner, M. H., Nachtsheim, C. J., Wasserman, W. (1996). Applied linear statistical models (4th ed.). Chicago: Irwin.

    Google Scholar 

  38. Ophir, S., El-Gad, U., & Snyder, M. (1988). “A case study of the use of an experimental design in preventing shorts in nickel-cadmium cells.” Journal of Quality Technology, 20(1), 44–50.

    Google Scholar 

  39. Quinlan, J. (1985). “Product improvement by application of Taguchi methods.” American Supplier Institute News (special symposium ed., pp. 11–16). Dearborn, MI: American Supplier Institute.

    Google Scholar 

  40. Ranganathan, R., Chowdhury, K. K., & Seksaria, A. (1992). “Design evaluation for reduction in performance variation of TV electron guns.” Quality Engineering, 4(3), 357–369.

    Article  Google Scholar 

  41. Schneider, H., Kasperski, W. J., & Weissfeld, L. (1993). “Finding significant effects for unreplicated fractional factorials using the n smallest contrasts.” Journal of Quality Technology, 25(1), 18–27.

    Google Scholar 

  42. Sirvanci, M. B., & Durmaz, M. (1993). “Variation reduction by the use of designed experiments.” Quality Engineering, 5(4), 611–618.

    Article  Google Scholar 

  43. Snee, R. D. (1985). “Computer-aided design of experiments: some practical experiences.” Journal of Quality Technology, 17(4), 222–236.

    MathSciNet  Google Scholar 

  44. Snee, R. D. (1985). “Experimenting with a large number of variables,” in Experiments in industry: design, analysis and interpretation of results (pp. 25–35). Milwaukee: American Society for Quality Control.

    Google Scholar 

  45. Snee, R. D., Hare, L. B., & Trout, J. R. (Eds.) (1985). Experiments in industry: design, analysis and interpretation of results. Milwaukee: American Society for Quality Control.

    Google Scholar 

  46. Sutter, J. K., Jobe, J. M., & Crane, E. (1995). “Isothermal aging of polyimide resins.” Journal of Applied Polymer Science, 57(12), 1491–1499.

    Article  Google Scholar 

  47. Taguchi, G., & Wu, Y. (1980). Introduction to off-line quality control. Nagoya: Japan Quality Control Organization.

    Google Scholar 

  48. Tomlinson, W. J., & Cooper, G. A. (1986). “Fracture mechanism of Brass/Sn-Pb-Sb solder joints and the effect of production variables on the joint strength.” Journal of Materials Science, 21(5), 1730–1734.

    Article  Google Scholar 

  49. Vander Wiel, S. A., & Vardeman, S. B. (1994). “A discussion of all-or-none inspection policies.” Technometrics, 36(1), 102–109.

    Article  Google Scholar 

  50. Vardeman, S. B. (1986). “The legitimate role of inspection in modern SQC.” The American Statistician, 40(4), 325–328.

    Google Scholar 

  51. Vardeman, S. B. (1994). Statistics for engineering problem solving. Boston: PWS Publishing.

    Google Scholar 

  52. Vardeman, S. B., & Jobe, J. M. (2001). Basic engineering data collection and analysis. Pacific Gove, CA: Duxbury/Thomsan Learning.

    Google Scholar 

  53. Walpole, R. E., & Myers, R. H. (1993). Probability and statistics for engineers and scientists (5th ed.). New York: Macmillan.

    MATH  Google Scholar 

  54. Wernimont, G. (1989–1990). “Statistical quality control in the chemical laboratory.” Quality Engineering, 2(1), 59–72.

    Google Scholar 

  55. Western Electric Company. (1984). Statistical quality control handbook (2nd ed.). New York: Western Electric Company.

    Google Scholar 

  56. Zwickl, R. D. (1985). “An example of analysis of means for attribute data applied to a 24 factorial design.” ASQC electronics division technical supplement, Issue 4. Milwaukee: American Society for Quality Control.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Iowa State University, Ames, Iowa, USA

    Stephen B. Vardeman

  2. Farmer School of Business, Miami University, Oxford, Ohio, USA

    J. Marcus Jobe

Authors
  1. Stephen B. Vardeman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Marcus Jobe
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag New York

About this chapter

Cite this chapter

Vardeman, S.B., Jobe, J.M. (2016). Experimental Design and Analysis for Process Improvement Part 2: Advanced Topics. In: Statistical Methods for Quality Assurance. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-79106-7_6

Download citation

Publish with us

Policies and ethics

Search

Navigation