Sinha, Bimal K.Borrego Garza, Paula Patricia2015-10-142015-10-142012-01-0110676http://hdl.handle.net/11603/1021In multivariate quality control, we are looking for monitoring p characteristics simultaneously. Consider the variance covariance matrix. This matrix is symmetric and has the variances of each characteristic on the diagonal elements in the order X1, X2, ...,Xn. Now, consider that the order of these characteristics is really arbi- trary. If two characteristics were considered, for instance, height and weight, there are actually two ways to arrange the variancecovariance matrix. If three characteristics are considered, six dierent orderings of the variance covariance matrices are possible. In this thesis we will study the consequences of rearranging the variance covariance matrix under a multivariate quality control setting; using the method of decomposition of the variance covariance matrix proposed by Tang and Barnett(1996) for p = 2; 3 and 4.application/pdfThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu.Multivariate Quality ControlText