Development of Robust Design Under Contaminated and Non-normal Data

Abstract

The usual assumptions behind robust design are that the distribution of experimental data is approximately normal and that there is no major contamination due to outliers in the data. Under these assumptions, sample mean and variance are often used to estimate process mean and variance. In this article, we first show simulation results indicating that sample mean and variance may not be the best choice when one or both assumptions are not met. The results further show that sample median and median absolute deviation or sample median and interquartile range are indeed more resistant to departures from normality and to contaminated data. We then show how to incorporate this observation into robust design modeling and optimization. A case study is presented.

Keywords:

• Robust design
• Outliers
• Non-normality
• Outlier-resistant estimators
• Optimum operating conditions

Acknowledgments