The Confounding Measure of Effects in Two-level Regular Designs under Linear Model

In design of experiments, the confounding of effects can cause the bias of parameter estimator in linear model. For two-level regular design, the paper introduces a confounding index pattern to measure such bias. A new method is proposed to study the properties of classification pattern for minimum N aberration criterion. We obtain the formula of confounding among lower-order effects, and provide some necessary conditions for optimal N designs. Some examples are given to illustrate the theoretical results.