Use General MANOVA to perform multivariate analysis of variance (MANOVA) with balanced and unbalanced designs, or if you have covariates. This procedure takes advantage of the data covariance structure to simultaneously test the equality of means from different responses.

When you use multiple one-way ANOVAs to analyze data, you increase the probability of a Type I error. MANOVA controls the family error rate, thereby minimizing the probability of making one or more type I errors for the entire set of comparisons.

Calculations are done using regression. The factors and covariates form a "full rank" design matrix and each response variable is regressed on the columns of the design matrix.

Factors may be crossed or nested, but you cannot use random factors. You must use fixed factors, though it is possible to work around this restriction by specifying the error term to test model terms. Covariates may be crossed with each other or with factors, or nested within factors. You can analyze up to 50 response variables with up to 31 factors and 50 covariates at one time.

A car manufacturing company is interested in the usability and quality of one of their model's door locks, which are produced by two different methods at three plants. They want to determine if the production method and plant affect the final product. Products from each plant produced by each method undergo a series of tests to generate final usability and quality scores.

Data: DoorLock.MTW (available in the Sample Data folder).