Process or Product Monitoring and Control
6.5.4. Elements of Multivariate Analysis
|The first step in analyzing multivariate data is computing the mean vector and the variance-covariance matrix.|
|Sample data matrix||
Consider the following matrix:
Definition of mean vector and variance-
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
Mean vector and variance-
covariance matrix for sample data matrix
The results are:
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