Optimal confounding measures for two-level regular designs.

The aliased effect-number pattern is usually used to reveal the confounding distributions between various factorial effects in any two-level regular design. However, it does not reflect all the confounding information's properties of concentricity and variation. The article proposes the concepts of confounding average and variance to solve the problem and introduce optimal confounding measures. We further study the relationship between the confounding average, resolution, and word length pattern. Finally, compared with other criteria, optimal designs with 16, 32, and 64 runs are tabulated under confounding measures. [ABSTRACT FROM AUTHOR]