Advanced search
Publication Cover
Quality Engineering Volume 36, 2024 - Issue 3
Submit an article Journal homepage
68
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Verifying a dominant cause of output variation

Mahsa Panahia Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, CanadaCorrespondence[email protected]
View further author information
,
Stefan H. Steinera Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, CanadaView further author information
&
Jeroen De Masta Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada;b Amsterdam Business School, University of Amsterdam, Amsterdam, The NetherlandsView further author information
Pages 548-559 | Published online: 11 Sep 2023

References

  • Bhote, K. R., and A. K. Bhote. 2000. World class quality. 2nd ed. New York: American Management Association.
  • Cohen, J., P. Cohen, S. G. West, and L. S. Aiken. 2003. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
  • De Mast, J., K. C. Roes, and R. Does. 2001. The multi-vari chart: a systematic approach. Quality Engineering 13 (3):437–47. doi: 10.1080/08982110108918672.
  • De Mast, J., and J. Lokkerbol. 2012. An analysis of the six sigma DMAIC method from the perspective of problem solving. International Journal of Production Economics 139 (2):604–14. doi: 10.1016/j.ijpe.2012.05.035.
  • De Mast, J., S. H. Steiner, R. Kuijten, and E. Funken. 2019. Statistical reasoning in diagnostic problem solving – the case of flow-rate measurements. Quality Engineering 31 (3):484–98. doi: 10.1080/08982112.2018.1548022.
  • De Mast, J., S. H. Steiner, W. P. M. Nuijten, and D. Kapitan. 2023. Analytical problem solving based on causal, correlational and deductive models. The American Statistician 77 (1):51–61. doi: 10.1080/00031305.2021.2023633.
  • Gryna, F. M., and J. M. Juran. 1988. Juran’s quality control handbook. 4th ed. New York: McGraw-Hill.
  • Hernan, M. A., J. Hsu, and B. Healy. 2019. A second chance to get causal inference right: a classification of data science tasks. Chance 32 (1):42–9. doi: 10.1080/09332480.2019.1579578.
  • Holland, P. W. 1986. Statistics and causal inference. Journal of the American Statistical Association 81 (396):945–60. doi: 10.1080/01621459.1986.10478354.
  • Juran, J. M., and F. M. Gryna. Jr. 1980. Quality planning and analysis. 2nd ed. McGraw-Hill Book Company, New York.
  • Lederer, D. J., S. C. Bell, R. D. Branson, J. D. Chalmers, R. Marshall, D. M. Maslove, D. E. Ost, N. M. Punjabi, M. Schatz, A. R. Smyth, et al. 2019. Control of confounding and reporting of results in causal inference studies. Annals of the American Thoracic Society 16 (1):22–8. doi: 10.1513/AnnalsATS.201808-564PS.
  • MacKay, R. J., and S. H. Steiner. 1997. Strategies for variability reduction. Quality Engineering 10 (1):125–36. doi: 10.1080/08982119708919115.
  • Mooren, J., J. De Mast, and R. J. M. M. Does. 2012. Quality quandaries*: the case of premature drill wear out. Quality Engineering 24 (2):354–9. doi: 10.1080/08982112.2011.652584.
  • Panahi, M., J. De Mast, and S. H. Steiner. 2021. Identifying dominant causes using leveraged study designs. Quality Engineering 33 (4):581–93. doi: 10.1080/08982112.2021.1938118.
  • Pearl, J. 2009. Causality: models, reasoning, and inference. 2nd ed. Cambridge: Cambridge University Press.
  • Pearl, J., and D. Mackenzie. 2018. The Book of why: the new science of cause and effect. New York: Basic books.
  • Rubin, D. 2005. Causal inference using potential outcomes. Journal of the American Statistical Association 100 (469):322–31. doi: 10.1198/016214504000001880.
  • Searle, S. R., G. Casella, and C. E. McCulloch. 1996. Variance components. New York: Wiley.
  • Shainin, R. D. 1993. Strategies for technical problem solving. Quality Engineering 5 (3):433–48. doi: 10.1080/08982119308918984.
  • Steiner, S. H., and R. J. MacKay. 2005. Statistical engineering: an algorithm for reducing variation in manufacturing processes. Milwaukee: Quality Press.
  • Steiner, S. H., and R. J. MacKay. 2006. Statistical engineering: a case study. Quality Progress, June, 33–9.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.