Evaluation of Statistical Designs for Experiments Involving Noise Variables

Abstract

In process robustness studies, it is desirable to simultaneously minimize the influence of noise factors on the system and to determine the levels of controllable factors that will optimize the overall response or outcome. A methodology for evaluating designed experiments that involve both controllable and uncontrollable, or noise, factors is outlined and presented in this paper. Two variance expressions are developed for evaluating competing experimental design strategies. The maximum, average, and minimum scaled prediction error variances resulting from the models developed are displayed visually on variance dispersion graphs. The scaled prediction error variances account for mean model errors as well as variation transmitted to the process by noise variables.

• Central Composite Designs
• Design of Experiments
• Response Surface Methodology
• Robust Parameter Design

Additional information

Notes on contributors

Connie M. Borror

Dr. Borror is a Lecturer in the Industrial Engineering Department. She is a Member of ASQ. Her e-mail address is [email protected].

Douglas C. Montgomery

Dr. Montgomery is a Professor in the Industrial Engineering Department. He is a Fellow of ASQ.

Raymond H. Myers

Dr. Myers is Professor Emeritus in the Department of Statistics. He is a Member of ASQ.