Modeling and Multivariate Methods - SAS

Chapter 3 Fitting Standard Least Squares Models 89
Effect Screening
The last three Effect Screening options in the report’s red triangle menu show a plot and the Parameter
Estimate Population report. The Parameter Population report finds the correlation of the estimates and tells
you whether the estimates are uncorrelated and have equal variances.
• If the estimates are correlated, a normalizing transformation can be applied make them uncorrelated and
have equal variances.
• If an estimate of error variance is unavailable, the relative standard error for estimates is calculated by
setting the error variance to one.
• If the estimates are uncorrelated and have equal variances, then the following notes appear under the
Effect Screening report title:
– The parameter estimates have equal variances.
– The parameter estimates are not correlated.
If the estimates are correlated and/or have unequal variances, then each of these two notes can appear as list
items. The selected list item shows that JMP has transformed the estimates.
To undo both transformations, click these list items.
The transformation to uncorrelate the estimates is the same as that used to calculate sequential sums of
squares. The estimates measure the additional contribution of the variable after all previous variables have
been entered into the model.
Example of Data with Equal Variances and Uncorrelated Estimates
1. Open the Reactor.jmp sample data table.
2. Select Analyze > Fit Model.
3. Select Y and click Y.
4. Make sure that the Degree box has a 2 in it.
5. Select Ct, A, T, and Cn and click Macros > Factorial to Degree.
6. Click Run.
7. From the red triangle menu next to Response Y, select Effect Screening > Normal Plot.
8. Open the Parameter Estimate Population report.
Figure 3.28 Parameter Estimate Population Report for Reactor.jmp