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Hierarchical Response Models for Design of Experiments

Bertram Schäfer, Owner, STATCON

Sebastian Hoffmeister, Trainer and Statistical Consultant, STATCON

Textbook applications of design of experiments (DOE) often present problems with one single response variable. While this might be enough to present important DOE concepts, reality is often more complex. The presented case study is on the other extreme. It shows the analysis of the relationships between different process parameters of a spring and its torque profile by using DOE for a response that is not one single-value measurement, but a complete curve. The presented solution covers nonlinear fits to model the response curve of each individual experiment. Afterward, the estimated model parameters of these nonlinear fits are used as response variables for the analysis of the DOE. Finally, a custom, JSL-based profiler will be presented, which allows us to interpret the effect of the different process parameters on the complete response curve.

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