3D ANOVA takes an array of experimental observations made at various levels of three factors and performs a three-way analysis of variance. Details
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Levels is a cluster of three numeric values corresponding to number of levels in the A, B, and C factors, as well as the effects of the A, B, and C factors (fixed or random).
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X contains all the observational data. | ||||||||||||||
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Index A contains the level of factor A to which the corresponding observation belongs. | ||||||||||||||
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Index B contains the level of factor B to which the corresponding observation belongs. | ||||||||||||||
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Index C contains the level of factor C to which the corresponding observation belongs. | ||||||||||||||
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observations per cell is the number of observations in each cell. It is the same for all cells. | ||||||||||||||
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The output 2D array Info is an 8 by 4 matrix organized where the first column corresponds to the sums of squares associated with the respective factors (A, B, C), the respective interactions (AB, AC, BC, ABC), and residual error.
The second column corresponds to the respective degrees of freedom. The third column corresponds to the respective mean squares. The fourth column corresponds to the respective F values. |
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Significance is a cluster of seven numerical values corresponding to the significance levels.
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error returns any error or warning condition from the VI. |
In any ANOVA, you look for evidence that the factors or interactions among factors have a significant effect on experimental outcomes. What varies with each model is the method used to do this. Refer to Random and Fixed Effects, General Method, Statistical Model, Assumptions, Hypotheses, Testing The Hypotheses, and Formulas for more information.