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This computational study investigates the use of response surfaces to model theoutput of the real life processes. In real life, the experimenter does not know the exactnature of the output of the process. So the output has to be approximated using amodel. This work proposes the generation of a new response surface, Compromise ResponseSurface (CRS). The experimenter initially decides on all the possible models to beconsidered and the number of design points to be considered per model.Then the design is generated such that they are equally good for all the models considered. This design is called compromise design. The use of compromise design gives a better design than anaugmented design. Then the methodology for the compromise design generation for linear through cubic for more than two variables is presented. The effectiveness of compromise design for more than two variables is tested on a problem with three variables.
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