The Effect of Measurement Error on the Power of X Charts with Known Coefficient of variation

Bhayare Unnati¹ , Singh Jais Raj²

1. School of Studies in Statistics, Vikram University, Ujjain, India.
2. School of Studies in Statistics, Vikram University, Ujjain, India.

Correspondence should be addressed to: unnati.b80@gmail.com .

Section:Research Paper, Product Type: Isroset-Journal
Vol.4 , Issue.5 , pp.33-39, Oct-2017

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v4i5.3339

Online published on Oct 30, 2017

Copyright © Bhayare Unnati, Singh Jais Raj . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Measurement error often exists in quality control applications. This error has an adverse effect on the chart’s performance. Measurement error (ME) is a source of variation that may considerably affect the performance of control charts applied within a statistical process control scheme. While the consequences of ME on the actual performance of various control charts has been studied in recent publications, the more important inverse problem of how to specify ME requirements to achieve desirable control chart performance characteristics has not been addressed in the literature. In this paper, we develop guidelines regarding the formulation of power function to distribution-related ME characteristics with known coefficient of variation and investigate the effect of ME and cv on power of mean chart.

Key-Words / Index Term :
Mean chart, Power, Measurement Error, Coefficient of Variation

References :
[1] Automotive Industry Action Group, Chrysler, Ford, GM, Measurement Systems Analysis, AIAG, Detroit, MI, 1995.
[2] K. Linna and W.H.Woodall, Effect of measurement error on shewhart control charts, J. Qual. Technol. 33(2) (2001), pp. 213–222.
[3] K. Linna and W.H.Woodall, The performance of multivariate control charts in the presence of measurement error, J. Qual. Technol. 33(3) (2001), pp. 349–355.
[4] P.E. Maravelakis, J. Panaretos, and S. Psarakis, EWMA chart and measurement error, J. Appl. Stat. 31(4) (2004), pp. 445–455.
[5] D.C. Montgomery, Statistical Process Control, JohnWiley & Sons, NewYork, 2009.
[6] D.C. Montgomery and G.C. Runger, Gauge capability and designed experiments. Part 1: Basic methods, Qual. Eng. 6(1) (1993), pp. 115–135.
[7] M.A. Rahim, Economic model of .X charts under non-normality and measurement errors, Int. J. Comput. Opera. Res. 9(3) (1985), pp. 291–299.
[8] D. T. Searls, The utilization of a known coefficient of variation in the estimation procedure, Journal of the American Statistical Association, 59,( 1964), pp. 1225-1226.