Designing Experiments to Optimize Manufacturing Materials Selection 

See how to design experiments based on physical or chemical properties to significantly minimize the number of required experimental conditions and produce a model that can predict untested options. These efficient designs are based on QSAR (Quantitative Structure – Activity Relationship) and a  2017 JMP Discovery Summit presentation by Silvio Miccio, Procter and Gamble.

This webinar covers: Using Principal Components Analysis (PCA) to compress information describing properties of potential options; using covariate properties to select materials to use in the experimental design; and modeling and comparing results to choose the most extensible model.