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Cumulative correspondence analysis of ordered categorical data from industrial experiments

Author

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  • Luigi D'Ambra
  • Onur Koksoy
  • Biagio Simonetti

Abstract

Most studies of quality improvement deal with ordered categorical data from industrial experiments. Accounting for the ordering of such data plays an important role in effectively determining the optimal factor level of combination. This paper utilizes the correspondence analysis to develop a procedure to improve the ordered categorical response in a multifactor state system based on Taguchi's statistic. Users may find the proposed procedure in this paper to be attractive because we suggest a simple and also popular statistical tool for graphically identifying the really important factors and determining the levels to improve process quality. A case study for optimizing the polysilicon deposition process in a very large-scale integrated circuit is provided to demonstrate the effectiveness of the proposed procedure.

Suggested Citation

  • Luigi D'Ambra & Onur Koksoy & Biagio Simonetti, 2009. "Cumulative correspondence analysis of ordered categorical data from industrial experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(12), pages 1315-1328.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1315-1328
    DOI: 10.1080/02664760802638090
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    Citations

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    Cited by:

    1. Antonello D’Ambra & Pietro Amenta & Anna Crisci & Antonio Lucadamo, 2022. "The generalized Taguchi’s statistic: a passenger satisfaction evaluation," METRON, Springer;Sapienza Università di Roma, vol. 80(1), pages 41-60, April.
    2. Antonello D’Ambra & Pietro Amenta & Antonio Lucadamo, 2019. "Analyzing Customer Requirements to Select a Suitable Service Configuration Both for Users and for Company Provider," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 383-394, November.
    3. Luigi D’Ambra & Pietro Amenta & Antonello D’Ambra, 2018. "Decomposition of cumulative chi-squared statistics, with some new tools for their interpretation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 297-318, June.

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