Modeling and Multivariate Methods - SAS

88 Fitting Standard Least Squares Models Chapter 3
Effect Screening
Pareto Plot
The Pareto Plot selection gives plots of the absolute values of the orthogonalized estimates showing their
composition relative to the sum of the absolute values. The estimates are orthogonalized to be uncorrelated
and standardized to have equal variances by default. If your data set has estimates that are correlated and/or
have unequal variances, then your data is transformed, by default, to have equal variances and to be
uncorrelated. However, you have the option of undoing the transformations. (See “Parameter Estimate
Population Report” on page 88.) In this case, the Pareto Plot represents your selection of equal variances or
unequal variances and uncorrelated or correlated estimates.
Example of a Pareto Plot
1. Open the Reactor.jmp sample data table.
For this data set, the estimates have equal variances and are not correlated.
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 F, 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 > Pareto Plot.
Figure 3.27 Pareto Plot for Reactor.jmp
Parameter Estimate Population Report
Most inferences about effect size first assume that the estimates are uncorrelated and have equal variances.
This is true for fractional factorials and many classical experimental designs. However, these assumptions are
not true for some designs.