Multivariate analysis is increasingly used to include all dimensions of quality concept, in light of rapid development of customer\r\nrequirements. With the recent advances in information technology and in recording, large amounts of multivariate data are now\r\nneeded to be analyzed. Many charting procedures are based on Mahalanobis distance, but their applicability relies heavily on the\r\nrequirement of normality and their performance is related to the choice of a type I error rate. An alternative charting scheme based\r\non data depth is pursued and its performance is assessed through a real example. This performance and that of a T2 chart for\r\nindividual observations are discussed. Using the centre-outward ranking, this new method named DD-diagram is used to detect\r\nany multivariate quality datum that one of its components exceeds its limiting variation interval. For a given error-free sample, the\r\nDD-diagram can be used to signal out any point of another observed sample taken from a multivariate quality process. This new\r\nscheme based on data depth uses a properly chosen limiting variation line or Lvalue in order to evaluate the outlyingness of every\r\npoint in the observed sample in all directions of the considered P-variates of quality process.
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