• :

    Nonregular designs are a preferable alternative to regular resolution IV designs because they avoid confounding two factor interactions. As a result nonregular designs can estimate and identify a few active two factor interactions. However, due to the sometimes complex alias structure of nonregular designs, some classical screening strategies fail at identifying all active effects. In this paper we explore a specific no-confounding 6-factor 16 run nonregular design with orthogonal main effects. We propose an all-possible-models selection approach that selects the minimum mean square error model from all fifteen second-order four factor models. We show that by using our proposed method the probability of missing active effects is minimal for a large variety of hypothetical models. A simulation is performed to compare this method against a forward selection stepwise approach. 

  • : Carly Metcalfe, Bradley Jones, Douglas Montgomery
  • : Arizona State University, SAS Institute, Arizona State University
  • : Carly Metcalfe
  • : experimental_design
  • : intermediate
  • : carly.metcalfe@asu.edu
  • : 716-952-7118
Model Selection for 6-Factor No-Confounding Design