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G. Taguchi and his school have made significant advances in the use of experimental design and analysis in industry. Of particular significance is their promotion of the use of statistical methods to achieve certain objectives in a mean response while simultaneously minimizing the variance. Unfortunately, many statisticians criticize the Taguchi techniques of analysis, particularly those based on the signal-to-noise ratio. This paper shows how the dual response approach of optimizing a “primary response” function while satisfying conditions on a “secondary response” function can be used to achieve the goals of the Taguchi philosophy within a more rigorous statistical methodology. An example illustrates this point.
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Vining et al. (1990) studied this question.
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