The ANOVA is where the descriptive statistics and statistical tests are presented. In general, look for low p-values to identify important terms in the model.
Select View > Annotated ANOVA to see the blue annotation text to help interpret the key elements in the ANOVA report.
Right-click in a cell on the report and select Help from the menu for details about that section.
The sections that are available depend on the type of model fit to the data.
The ML or Maximum Likelihood is the alternate method to calculate the statistical tests when there are hard to change factors creating a large split-plot design.
ML differs from REML in how the random effects are estimated and p-values computed for the fixed effects.
Fixed Effects: This is used similarly to a standard ANOVA except there are multiple parts. The whole-plot effects from hard to change factors are tested separately from the sub-plot effects that contain easy to change factors. Look at the p-values to determine if the model explains a significant portion of the variance.
Variance Components: Also called random effects, are estimated for each part of the model. See the topic on variance components for more details.
R-Squared: The R-Squareds and Likelihood statistics can be used to compare models from different analyses.
Coefficients: This section provides the confidence intervals around the estimated model coefficients. While most analyses do not require examining these intervals, they can help sort out issues when the analysis doesn’t make sense.
Equations: As many as two model equations will be presented. One for the coded model taken from the coefficient table above, and one for the actual model which is rescaled to include the factor units of measure.