Chapter Contents |
Previous |
Next |

Multivariate Analyses |

Select at least one **Y** variable.
With canonical correlation analysis and maximum redundancy analysis,
you need to select a set of **X** variables.
With canonical discriminant analysis, you need to select
a nominal **Y** variable and a set of **X** variables.

Without **X** variables, sums of squares and crossproducts,
corrected sums of squares and crossproducts, covariances,
and correlations are displayed as symmetric matrices with
**Y** variables as both the row variables and the column variables.
With a nominal **Y** variable, these statistics are displayed as symmetric
matrices with **X** variables as both the row variables and the
column variables.
When both interval **Y** variables and interval **X** variables
are selected, these statistics are displayed as rectangular matrices
with **Y** variables as the row variables
and **X** variables as the column variables.

You can select one or more **Partial** variables.
The multivariate analysis analyzes **Y** and **X** variables
using their residuals after partialling out the **Partial** variables.

You can select one or more **Group**
variables, if you have grouped data.
This creates one multivariate analysis for each group.
You can select a **Label** variable
to label observations in the plots.
You can select a **Freq** variable.
If you select a **Freq** variable, each observation
is assumed to represent *n*_{i} observations,
where *n*_{i} is the value of the **Freq** variable.
You can select a **Weight** variable to specify relative
weights for each observation in the analysis.
The details of weighted analyses are explained in the
"Method" section, which follows, and the "Weighted Analyses"
section at the end of this chapter.

Chapter Contents |
Previous |
Next |
Top |

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.