Modeling and Multivariate Methods - SAS

Chapter 11 Modeling Relationships With Gaussian Processes 301
Borehole Hypercube Example
When you click OK, the following Actual by Predicted plot appears.
Since the points are close to the 45 degree diagonal line, we can be confident that the Gaussian process
prediction model is a good approximation to the true function that generated the data.
The Model Report shows us that this is mainly due to one factor, log10 Rw. The main effect explains 87.5%
of the variation, with 90.5% explained when all second-order interactions are included.
Most of the variation is
explained by log10Rw
Factors with a theta value of 0 do not impact the prediction formula at all. It is as if they have been dropped
from the model.
The Marginal Model plots confirm that log10 Rw is a highly involved participant in Y’s variation.