A comparison of two-level designs to estimate all main effects and two-factor interactions

Abstract

ABSTRACTWe compare cost-efficient alternatives for the full factorial 24 design, the regular 25-1 fractional factorial design, and the regular 26-1 fractional factorial design that can fit the model consisting of all the main effects as well as all the two-factor interactions. For 4 and 5 factors we examine orthogonal arrays with 12 and 20 runs, respectively. For 6 factors we consider orthogonal arrays with 24 as well as 28 runs. We consult complete catalogs of two-level orthogonal arrays to find the ones that provide the most efficient estimation of all the effects in the model. We compare these arrays with D-optimal designs found using a coordinate exchange algorithm. The D-optimal designs are always preferable to the most efficient orthogonal arrays for fitting the full model in all the factors.