Interpret the key results for General MANOVA

Use the eigen analysis to assess how the response means differ between the levels of the different model terms. You should focus on the eigenvectors that correspond to high eigenvalues. To display the eigen analysis, go to Stat > ANOVA > General MANOVA > Results and select Eigen analysis under Display of Results.

Use the Means table to understand the statistically significant differences between the factor levels in your data. The mean of each group provides an estimate of each population mean. Look for differences between group means for terms that are statistically significant.

For main effects, the table displays the groups within each factor and their means. For interaction effects, the table displays all possible combinations of the groups. If an interaction term is statistically significant, do not interpret the main effects without considering the interaction effects.

To display the means, go to Stat > ANOVA > General MANOVA > Results, select Univariate analysis of variance, and enter the terms in Display least squares means corresponding to the terms.

When you perform General MANOVA, you can choose to calculate the univariate statistics to examine the individual responses. The univariate results can provide a more intuitive understanding of the relationships in your data. However, the univariate results can differ from the multivariate results.

To display the univariate results, go to Stat > ANOVA > General MANOVA > Results and select Univariate analysis of variance under Display of Results.

Use the residual plots to help you determine whether the model is adequate and meets the assumptions of the analysis. If the assumptions are not met, the model may not fit the data well and you should use caution when you interpret the results.

When you perform General MANOVA, Minitab displays residual plots for all response variables that are in your model. You must determine whether the residual plots for all response variables indicate that the model meets the assumptions.

For more information on how to handle patterns in the residual plots, go to Residual plots for General MANOVA and click the name of the residual plot in the list at the top of the page.

Residuals versus fits plot

Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed and have constant variance. Ideally, the points should fall randomly on both sides of 0, with no recognizable patterns in the points.

The patterns in the following table may indicate that the model does not meet the model assumptions.

Pattern	What the pattern may indicate
Fanning or uneven spreading of residuals across fitted values	Nonconstant variance
Curvilinear	A missing higher-order term
A point that is far away from zero	An outlier
A point that is far away from the other points in the x-direction	An influential point

In this residuals versus fits plot, the data appear to be randomly distributed about zero. There is no evidence that the value of the residual depends on the fitted value.

Residuals versus order plot

Use the residuals versus order plot to verify the assumption that the residuals are independent from one another. Independent residuals show no trends or patterns when displayed in time order. Patterns in the points may indicate that residuals near each other may be correlated, and thus, not independent. Ideally, the residuals on the plot should fall randomly around the center line:

If you see a pattern, investigate the cause. The following types of patterns may indicate that the residuals are dependent.

Trend

Shift

Cycle

In this residuals versus order plot, the residuals appear to fall randomly around the centerline. There is no evidence that the residuals are not independent.

Normal probability plot of the residuals

Use the normal probability plot of the residuals to verify the assumption that the residuals are normally distributed. The normal probability plot of the residuals should approximately follow a straight line.

The patterns in the following table may indicate that the model does not meet the model assumptions.

Pattern	What the pattern may indicate
Not a straight line	Nonnormality
A point that is far away from the line	An outlier
Changing slope	An unidentified variable

In this normal probability plot, the points generally follow a straight line. There is no evidence of nonnormality, outliers, or unidentified variables.