Classical Charts :: SAS/QC(R) 12.3 User's Guide

Classical Charts

Classical charts are defined as follows. Assume that there are n observations for p variables, denoted by $\mb {X}_1,\ldots ,\mb {X}_ n$ , where $\mb {X}_ i$ is a p-dimensional vector. The statistic for observation i is

$T^2_ i = \left(\mb {X}_ i - \bar{\mb {X}}_ n \right)^{\prime } \mb {S}^{-1} \left(\mb {X}_ i - \bar{\mb {X}}_ n \right)$

where

$\begin{array}{lcr} \bar{X}_ j = \frac{1}{n} \sum _{i=1}^ n X_{ij}~ ~ , & \mb {X}_ i = \left[ \begin{array}{c} X_{i1} \\ X_{i2} \\ \vdots \\ X_{ip} \end{array} \right] , & \bar{\mb {X}}_ n = \left[ \begin{array}{c} \bar{X}_{1} \\ \bar{X}_{2} \\ \vdots \\ \bar{X}_{p} \end{array} \right] \end{array}$

and

$\mb {S} = \frac{1}{n-1} \sum _{i=1}^ n \left(\mb {X}_ i - \bar{\mb {X}}_ n\right) \left(\mb {X}_ i - \bar{\mb {X}}_ n\right)^{\prime }$

For purposes of deriving control limits for the chart, it is assumed that $\mb {X}_ i$ has a p-dimensional multivariate normal distribution with mean vector $\bmu =\left(\mu _1,\mu _2,\cdots ,\mu _ p\right)^\prime$ and covariance matrix $\bSigma$ for $i=1,2,\ldots ,n$ . The classical formulation of the chart does not involve a principal component model for the data, and it bases the computation of on the sample covariance matrix $\bS$ . See Alt (1985) for theoretical details and the section Multivariate Control Charts for an example.

A classical chart is equivalent to a chart based on a full principal component model (with p components), as discussed in the section Relationship of Principal Components to Multivariate Control Charts. See Example 12.2 for more information.

The MVPMODEL Procedure

Classical Charts