6. Process or Product Monitoring and Control
6.5. Tutorials
6.5.4. Elements of Multivariate Analysis

6.5.4.1.

Mean Vector and Covariance Matrix

The first step in analyzing multivariate data is computing the mean vector and the variance-covariance matrix.

Sample data matrix

Consider the following matrix: The set of 5 observations, measuring 3 variables, can be described by its mean vector and variance-covariance matrix. The three variables, from left to right are length, width, and height of a certain object, for example. Each row vector X_i is another observation of the three variables (or components).

Definition of mean vector and variance-
covariance matrix

The mean vector consists of the means of each variable and the variance-covariance matrix consists of the variances of the variables along the main diagonal and the covariances between each pair of variables in the other matrix positions.

The formula for computing the covariance of the variables X and Y is

with and denoting the means of X and Y, respectively.

Mean vector and variance-
covariance matrix for sample data matrix

The results are: where the mean vector contains the arithmetic averages of the three variables and the (unbiased) variance-covariance matrix S is calculated by

where n = 5 for this example.

Thus, 0.025 is the variance of the length variable, 0.0075 is the covariance between the length and the width variables, 0.00175 is the covariance between the length and the height variables, 0.007 is the variance of the width variable, 0.00135 is the covariance between the width and height variables and .00043 is the variance of the height variable.

Centroid, dispersion matix

The mean vector is often referred to as the centroid and the variance-covariance matrix as the dispersion or dispersion matrix. Also, the terms variance-covariance matrix and covariance matrix are used interchangeably.