Controlling IER and EER in replicated regular two-level factorial experiments

Abstract

Replicated regular two-level factorial experiments are very useful for industry. The goal of these experiments is to identify active effects that affect the mean and variance of the response. Hypothesis testing procedures are widely used for this purpose. However, the existing methods give results that are either too anticonservative or conservative in controlling the individual and experimentwise error rates (IER and EER). In this paper, we propose {a Monte Carlo method} and an exact-variance method to identify active effects for the mean and variance, respectively, of the response. Simulation studies show that our methods control the IER and EER extremely well. Real data are used to illustrate the performance of the methods.