.. _method-dace-main_effects:

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main_effects
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ANOVA


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**Specification**

- *Alias:* None

- *Arguments:* None

- *Default:* No main_effects


**Description**


The ``main_effects`` control prints Analysis-of-Variance
main effects results (e.g. ANOVA tables with p-values per variable).
The ``main_effects`` control is only operational with the
orthogonal arrays or Latin Hypercube designs, not for Box Behnken or
Central Composite designs.

Main effects is a sensitivity analysis method which identifies the input
variables 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
responses of the output are statistically significantly different at
different levels of variable A, this is an indication
that variable A has a significant effect on the response.
The orthogonality of the columns is critical in performing
main effects analysis, since the column orthogonality means that the effects of the other variables "cancel out"
when looking at the overall effect from one variable at its different levels.


