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Acceptance control charts for non-normal data

Author

Listed:
  • Chao-Yu Chou
  • CHung-Ho Chen
  • Hui-Rong Liu

Abstract

Control charts are one of the most important methods in industrial process control. The acceptance control chart is generally applied in situations when an X-super-¯ chart is used to control the fraction of conforming units produced by the process and where 6-sigma spread of the process is smaller than the spread in the specification limits. Traditionally, when designing control charts, one usually assumes that the data or measurements are normally distributed. However, this assumption may not be true in some processes. In this paper, we use the Burr distribution, which is employed to represent various non-normal distributions, to determine the appropriate control limits or sample size for the acceptance control chart under non-normality. Some numerical examples are given for illustration. From the presented examples, ignoring the effect of non-normality in the data leads to a higher type I or type II error probability.

Suggested Citation

  • Chao-Yu Chou & CHung-Ho Chen & Hui-Rong Liu, 2005. "Acceptance control charts for non-normal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(1), pages 25-36.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:25-36
    DOI: 10.1080/0266476042000305131
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    References listed on IDEAS

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    1. Chao-Yu Chou & Chung-Ho Chen & Hui-Rong Liu, 2000. "Economic-statistical design of X ¥ charts for non-normal data by considering quality loss," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(8), pages 939-951.
    2. David Hill, 1956. "Modified Control Limits," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 5(1), pages 12-19, March.
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    Cited by:

    1. Nandini Das, 2011. "Control Charts Based on the g-and-h Distribution," Stochastics and Quality Control, De Gruyter, vol. 26(1), pages 3-14, January.

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