Modeling and Multivariate Methods - SAS

104 Fitting Standard Least Squares Models Chapter 3
Save Columns
Figure 3.41 Leverage Plots for Simple Regression and Complex Effects
residual
residual
{
residual
{
{
to the
mean
{
residual
to the
mean
45 degree line
The influential points in all leverage plots are the ones far out on the x-axis. If two effects in a model are
closely related, then these effects as a whole do not have much leverage. This problem is called collinearity.
By scaling regressor axes by their original values, collinearity is shown by shrinkage of the points in the x
direction.
See the appendix “Statistical Details” on page 651 for the details of leverage plot construction.
Save Columns
Each Save Columns option generates one or more new columns in the current data table, where
is the name of the response variable.
Table 3.10 Description of the Save Columns Options
Prediction Formula
Predicted Values
Creates a new column called Pred Formula colname that contains the
predicted values computed by the specified model. It differs from the
Predicted colname column, because it contains the prediction formula.
This is useful for predicting values in new rows or for obtaining a picture of
the fitted model.
Use the Column Info option and click the Edit Formula button to see the
prediction formula. The prediction formula can require considerable space if
the model is large. If you do not need the formula with the column of
predicted values, use the Save Columns > Predicted Values option. For
information about formulas, see the Using JMP book.
Note: When using this option, an attempt is first made to find a Response
Limits property containing desirability functions. The desirability functions
are determined from the profiler, if that option has been used. Otherwise,
the desirability functions are determined from the response columns. If you
reset the desirabilities later, it affects only subsequent saves. (It does not
affect columns that has already been saved.)
Creates a new column called Predicted colname that contains the predicted
values computed by the specified model.