Optimizing Factors in Experimental Design

In many designed experiments, factors with a known high influence on the response(s) are varied. As an example, the factor throughput of a combine harvester is used. The response considered are the loss of grain material or its quality. Other factors are adjustments to the machine and / or technical variants which usually have only a smaller effect on the response compared to the dominating factor throughput. In Cornerstone, regression models can be used to define targets for response variables that are to be achieved by varying the factors. In the example considered here, however, the factor throughput is to be maximized for a defined level of the response variable. Levers for this goal are the remaining factors.

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