Influence of skewness and kurtosis on high sigma cause selecting control chart performance for bivariate cascade processes
by S. Lakshminarasimhan, S.M. Kannan
International Journal of Productivity and Quality Management (IJPQM), Vol. 4, No. 3, 2009

Abstract: Cause selecting control charts are used for monitoring two cascade production processes. They work on the principle of regression in which the regressor variate is adjusted for a response covariate. It is generally assumed that the regression models have an intercept term. In practice, the process data may belong to a regression model with or without an intercept term. When the two cascade processes are part of a high sigma environ, the geometric data requires a transformation to convert the process data into normality. In this work for a high sigma cascade process, the extent of normality achieved and measured in terms of skewness and kurtosis and its influence on lower control limit and Type II error respectively have been studied. The study is carried out within limits of four scenarios of the intercept models.

Online publication date: Sun, 08-Mar-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Productivity and Quality Management (IJPQM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com