Modeling and Multivariate Methods - SAS

82 Fitting Standard Least Squares Models Chapter 3
Effect Screening
Figure 3.20 Correlation of Estimates Report
Effect Screening
The Effect Screening options on the Response red triangle menu examine the sizes of the effects.
Scaled Estimates
Normal Plot
Bayes Plot
Pareto Plot
Gives coefficients corresponding to factors that are scaled to have a mean of
zero and a range of two. See “Scaled Estimates and the Coding Of
Continuous Terms” on page 82.
Helps you identify effects that deviate from the normal lines. See “Plot
Options” on page 83.
Computes posterior probabilities using a Bayesian approach. See “Plot
Options” on page 83.
Shows the absolute values of the orthogonalized estimates showing their
composition relative to the sum of the absolute values.See “Plot Options” on
page 83.
Scaled Estimates and the Coding Of Continuous Terms
The parameter estimates are highly dependent on the scale of the factor. When you convert a factor from
grams to kilograms, the parameter estimates change by a multiple of a thousand. When you apply the same
change to a squared (quadratic) term, the scale changes by a multiple of a million.
To learn more about the effect size, examine the estimates in a more scale-invariant fashion. This means
converting from an arbitrary scale to a meaningful one. Then the sizes of the estimates relate to the size of
the effect on the response. There are many approaches to doing this. In JMP, the Effect Screening >
Scaled Estimates command on the report’s red triangle menu gives coefficients corresponding to factors
that are scaled to have a mean of zero and a range of two. If the factor is symmetrically distributed in the
data, then the scaled factor has a range from -1 to 1. This factor corresponds to the scaling used in the
design of experiments (DOE) tradition. Therefore, for a simple regressor, the scaled estimate is half of the
predicted response change as the regression factor travels its whole range.
Scaled estimates are important in assessing effect sizes for experimental data that contains uncoded values. If
you use coded values (–1 to 1), then the scaled estimates are no different from the regular estimates. Scaled
estimates also take care of the issues for polynomial (crossed continuous) models, even if they are not
centered by the Center Polynomials command on the launch window’s red triangle menu.