# ANOVA for Linear Mixture

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 a linear mixture model is selected. It includes
a section for testing component gradients rather than the linear coefficients.

## ANOVA Sections

**ANOVA**: This is the analysis of variance for the linear mixture model. Because
the linear coefficients in the Scheffé mixture model are a prediction rather
than an effect they are not tested independently. If only the linear terms are
present in the model, the individual component effect tests are moved to the
Gradients section.

**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.

**Gradients**: This section computes the gradients and
component effects. Hypothesis tests
for the gradients being different than 0
are shown. Look for low p-values to find the active components.

**Equations**: As many as three model equations will be presented. One for the
pseudo model taken from the coefficient table above, one in the real coding
scale, and one in the actual scale. The default analysis computes and tests the
pseudo model which is converted
to the real model and finally to the actual model.