Enumeration and Evaluation of Small Orthogonal Latin Hypercube Designs for Polynomial Regression Models

In this paper, we construct and evaluate all nonisomorphic Latin hypercube designs with n≤16 runs, the use of which guarantee that the estimates of the first‐order effects are uncorrelated with each other and also uncorrelated with the estimates of the second‐order effects, in polynomial regression models. The produced designs are evaluated using well‐known and popular criteria, and optimal designs are presented in every case studied. An effort to construct nonisomorphic small Latin hypercubes in which only the estimates of the first‐order effects are required to be uncorrelated with each other has also been made, and new designs are presented. All the constructed designs, besides their stand‐alone properties, are useful for the construction of bigger orthogonal Latin hypercubes with desirable properties, using well‐known techniques proposed in the literature.