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

  • Stephen B. Vardeman
  • J. Marcus Jobe
Part of the Springer Texts in Statistics book series (STS)


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.


Treatment Combination Process Variable Noise Variable Experimental Region Solder Layer 
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  1. [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. [2]
    Bisgaard, S. (1994). “Blocking generators for small 2kp designs.” Journal of Quality Technology, 26(4), 288–296.MathSciNetGoogle Scholar
  3. [3]
    Bisgaard, S., & Fuller, H. T. (1995–1996). “Reducing variation with two-level factorial experiments.” Quality Engineering, 8(2), 373–377.Google Scholar
  4. [4]
    Box, G. E. P., & Draper, N. R. (1969). Evolutionary operation. New York: Wiley.Google Scholar
  5. [5]
    Box, G. E. P., & Draper, N. R. (1986). Empirical model building and response surfaces. New York: Wiley.zbMATHGoogle Scholar
  6. [6]
    Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1978). Statistics for experimenters. New York: Wiley.zbMATHGoogle Scholar
  7. [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. [8]
    Brezler, P. (1986). “Statistical analysis: Mack Truck gear heat treat experiments.” Heat Treating, 18(11), 26–29.Google Scholar
  9. [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. [10]
    Burdick, R. K., & Graybill, F. A. (1992). Confidence intervals on variance components. New York: Marcel Dekker.zbMATHGoogle Scholar
  11. [11]
    Burns, W. L. (1989–90). “Quality control proves itself in assembly.” Quality Engineering, 2(1), 91–101.Google Scholar
  12. [12]
    Burr, I. W. (1953). Engineering statistics and quality control. New York: McGraw-Hill.zbMATHGoogle Scholar
  13. [13]
    Burr, I. W. (1979). Elementary statistical quality control. New York: Marcel Dekker.zbMATHGoogle Scholar
  14. [14]
    Champ, C. W., & Woodall, W. H. (1987). “Exact results for shewhart control charts with supplementary runs rules.” Technometrics, 29(4), 393–399.CrossRefzbMATHGoogle Scholar
  15. [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. [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. [17]
    Currie, L. A. (1968). “Limits for qualitative detection and quantitative determination.” Analytical Chemistry, 40(3), 586–593.CrossRefGoogle Scholar
  18. [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. [19]
    Duncan, A. J. (1986). Quality control and industrial statistics (5th ed.). Homewood, IL: Irwin.zbMATHGoogle Scholar
  20. [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. [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. [22]
    Grego, J. M. (1993). “Generalized linear models and process variation.” Journal of Quality Technology, 25(4), 288–295.Google Scholar
  23. [23]
    Hahn, J. G., & Meeker, W. Q. (1991). Statistical intervals: a guide for practitioners. New York: Wiley.CrossRefzbMATHGoogle Scholar
  24. [24]
    Hendrix, C. D. (1979). “What every technologist should know about experimental design.” Chemical Technology, 9(3), 167–174.Google Scholar
  25. [25]
    Hill, W. J., & Demler, W. R. (1970). “More on planning experiments to increase research efficiency.” Industrial and Engineering Chemistry, 62(10), 60–65.CrossRefGoogle Scholar
  26. [26]
    Kolarik, W. J. (1995). Creating quality: concepts, systems, strategies and tools. New York: McGraw-Hill.Google Scholar
  27. [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. [28]
    Lawson, J. S., & Madrigal, J. L. (1994). “Robust design through optimization techniques.” Quality Engineering, 6(4), 593–608.Google Scholar
  29. [29]
    Leigh, H. D., & Taylor, T. D. (1990). “Computer-generated experimental designs.” Ceramic Bulletin, 69(1), 100–106.Google Scholar
  30. [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. [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. [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. [33]
    Moen, R. D., Nolan, T. W., & Provost, L. P. (1991). Improving quality through planned experimentation. New York: McGraw-Hill.Google Scholar
  34. [34]
    Myers, R. H. (1976). Response surface methodology. Ann Arbor: Edwards Brothers.Google Scholar
  35. [35]
    Nair, V. N. (Ed.) (1992). “Taguchi’s parameter design: a panel discussion.” Technometrics, 34(2), 127–161.Google Scholar
  36. [36]
    Nelson, L. S. (1984). “The Shewhart control chart-tests for special causes.” Journal of Quality Technology, 16(4), 237–239.Google Scholar
  37. [37]
    Neter, J., Kutner, M. H., Nachtsheim, C. J., Wasserman, W. (1996). Applied linear statistical models (4th ed.). Chicago: Irwin.Google Scholar
  38. [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. [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. [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.CrossRefGoogle Scholar
  41. [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. [42]
    Sirvanci, M. B., & Durmaz, M. (1993). “Variation reduction by the use of designed experiments.” Quality Engineering, 5(4), 611–618.CrossRefGoogle Scholar
  43. [43]
    Snee, R. D. (1985). “Computer-aided design of experiments: some practical experiences.” Journal of Quality Technology, 17(4), 222–236.MathSciNetGoogle Scholar
  44. [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. [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. [46]
    Sutter, J. K., Jobe, J. M., & Crane, E. (1995). “Isothermal aging of polyimide resins.” Journal of Applied Polymer Science, 57(12), 1491–1499.CrossRefGoogle Scholar
  47. [47]
    Taguchi, G., & Wu, Y. (1980). Introduction to off-line quality control. Nagoya: Japan Quality Control Organization.Google Scholar
  48. [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.CrossRefGoogle Scholar
  49. [49]
    Vander Wiel, S. A., & Vardeman, S. B. (1994). “A discussion of all-or-none inspection policies.” Technometrics, 36(1), 102–109.CrossRefGoogle Scholar
  50. [50]
    Vardeman, S. B. (1986). “The legitimate role of inspection in modern SQC.” The American Statistician, 40(4), 325–328.Google Scholar
  51. [51]
    Vardeman, S. B. (1994). Statistics for engineering problem solving. Boston: PWS Publishing.Google Scholar
  52. [52]
    Vardeman, S. B., & Jobe, J. M. (2001). Basic engineering data collection and analysis. Pacific Gove, CA: Duxbury/Thomsan Learning.Google Scholar
  53. [53]
    Walpole, R. E., & Myers, R. H. (1993). Probability and statistics for engineers and scientists (5th ed.). New York: Macmillan.zbMATHGoogle Scholar
  54. [54]
    Wernimont, G. (1989–1990). “Statistical quality control in the chemical laboratory.” Quality Engineering, 2(1), 59–72.Google Scholar
  55. [55]
    Western Electric Company. (1984). Statistical quality control handbook (2nd ed.). New York: Western Electric Company.Google Scholar
  56. [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

Copyright information

© Springer-Verlag New York 2016

Authors and Affiliations

  • Stephen B. Vardeman
    • 1
  • J. Marcus Jobe
    • 2
  1. 1.Iowa State UniversityAmesUSA
  2. 2.Farmer School of BusinessMiami UniversityOxfordUSA

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