ANOVA for One Categoric Factor
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.
This style of ANOVA is used when there is only a single categoric factor in the design.
ANOVA Sections
Anova: Look at the p-values to determine if the model explains a significant portion of the variance. When there are extra runs and replicates a lack-of-fit test will also be provided.
Summary Statistics: The descriptive statistics are used as a secondary check for the usefulness of the model.
Subtract the Predicted R-Squared from the Adjusted R-squared. If the difference is less than 0.2, then the model is fitting the data and can reliably be used to interpolate.
Check the Adequate Precision. If it is greater than 4, then the model has a strong enough signal to be used for optimization.
CV% is used in some industries to judge the capability of a process; lower is better.
Compare the standard deviation to the estimate used when sizing the design (power or FDS).
The mean is the average of the response, and the PRESS is used to calculate other statistics such as the predicted R-squared.
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.
At the bottom of this report is an additional section where the treatment means are tested to see if they are significantly different. All pairwise combinations are tested. Only use these comparisons if the Model F-Test in the ANOVA is significant.