Generalized Linear Model (GLM) Unique Features - Desirability Profiles and Response Optimization
STATISTICA GLM includes options for defining and optimizing desirability (response) profiles for a single or multiple dependent variables. A detailed discussion of desirability profiling and response optimization is also provided in the context of the Experimental Design module, under the topic Profiling predicted responses and response desirability. In short, after fitting a model it is often desirable to determine an optimum setting for the dependent variable, or combination of dependent variables. For example, in the manufacture of tires, one might be interested in the hardness of the tire, indices of abrasion, and effectiveness during braking. Each one of these characteristics may add to the desirability in particular (and often non-linear) ways (e.g., there may be an optimum level of hardness). The facilities available on the Profiler tab on the Results dialog allow you to define the desirability (function) for the dependent variables, and then to review the combined desirability for all dependent variables over the levels (or user-defined values) of the predictor variables; facilities for automatically finding the optimum desirability are also provided.
Some Unique Features and Facilities of GLM