Abstract
Experimenters should be aware of the possibility that some of their observations may be unavailable for analysis. This paper considers a criterion that assesses the robustness for missing data when running four and five levels designs in estimating a full second-order polynomial model. The criterion gives the maximum number of runs that can be missing and still allow the remaining runs to estimate a second-order model for four and five levels.
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