Experimental design for product improvement
Fractional factorial designs have been used successfullyin industry and elsewhere todetect and estimate sparse factor effects , The effectsusually evisioned measure changes in location associated with the experimental factors . Here we consider the possibility of detecting and estimating sparse dispersion effects measuring changes in variance associated with the factors . ( 2 ) In industrial experimentation it is frequently true thata large proportion of process v ariation is associated with a smallproportion of the process variables . In such circum stancs of“effect sparsity”unreplicated fractional designs have frequently been effectivein islolating preponderant factors.A very useful graphical analysis for such experiments due to Cuthbert Daniel(1959)employs normal probability plotting.A more formal analysis is presented here which might be used to supplement such plots.