Robust Parameter Design
Subject: Business Statistics | Topics:

Robust Parameter Design is very similar to fractional factorial designs (FFDs) in that the optimal design can be found using Hadamard matrices, principles of effect hierarchy and factor sparsity are maintained, and aliasing is present when full RPDs are fractionated. It is primarily use in a simulation setting where uncontrollable noise variables are easily controlled. Whereas in the real world noise factors are hard to control, in an experimental setting control over these factors is easily maintained. Robust Parameter Design (RPD) approach initially proposed by Japanese engineer, Genichi Taguchi, seeks a combination of controllable factors such that two main objectives are achieved: The mean or average location of the response is at the desired level, and The variation or dispersion of the response is as small as possible.

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