Covariate regressors are added to a response surface model
because they are believed to account for a sizable yet
relatively uninteresting portion of the variation in the data.
What the experimenter is really interested in is the
response corrected for the effect of the covariates.
A common example is the block effect in a block design.
In the canonical and ridge analyses of a response surface,
which estimate responses at hypothetical levels of the
factor variables, the actual value of the predicted response
is computed using the average values of the covariates.
The estimated response values do optimize the estimated surface
of the response corrected for covariates, but true prediction
of the response requires actual values for the covariates.
You can use the COVAR= option in the MODEL statement to
include covariates in the response surface model.
Example 56.2 illustrates the use of this
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