Analysis of supersaturated designs

Supersaturated designs offer a potentially useful way to investigate many factors with very few experimental runs. These designs are used to investigate m factors with n experimental runs, where m > n - 1. We evaluate several methods for analyzing a broad range of supersaturated designs and provide a basic explanation of these procedures. We show that the contrasts of a supersaturated design follow a permuted multivariate hypergeometric distribution, which may be approximated with a normal distribution. The analysis methods presented are based on methods for unreplicated fractional factorial designs. Two contrast-based analysis methods are presented, and the assumptions of the underlying model are described for a wide range of supersaturated designs.