1Blurb:: 2ANOVA 3Description:: 4The \c main_effects control prints Analysis-of-Variance 5main effects results (e.g. ANOVA tables with p-values per variable). 6The \c main_effects control is only operational with the 7orthogonal arrays or Latin Hypercube designs, not for Box Behnken or 8Central Composite designs. 9 10Main effects is a sensitivity analysis method which identifies the input 11variables that have the most influence on the output. In main effects, the idea is to look at the mean of the response function when variable A (for example) is at level 1 vs. when variable A is at level 2 or level 3. If these mean 12responses of the output are statistically significantly different at 13different levels of variable A, this is an indication 14that variable A has a significant effect on the response. 15The orthogonality of the columns is critical in performing 16main effects analysis, since the column orthogonality means that the effects of the other variables "cancel out" 17when looking at the overall effect from one variable at its different levels. 18Topics:: 19Examples:: 20Theory:: 21Faq:: 22See_Also:: 23