Thoughts on Supersaturated Designs for Factor Screening Experiments

Oregon Convention Center 

For a designed experiment with many factors, when observations are expensive, it is common that the number of model effects is much larger than the number of observations. A design for such a problem is known as a supersaturated design. Experiments that use such designs are intended to differentiate between a few factors that can explain most of the differences in a response variable and the factors that are unimportant. Various methods of analysis have been proposed for such experiments, but correctly identifying the few important factors is very challenging because, typically, many models will fit the data approximately equally well. We will discuss some of the challenges and how identifying the important factors may be improved by considering multiple fitted models.