Assumptions in multivariate analysis of covariance (MANCOVA)

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In multivariate analysis of covariance (MANCOVA), all assumptions are the same as in MANCOVA, but one more additional assumption is related to covariate. The following are the assumptions of MANCOVA:

1. Normal distribution: In multivariate analysis of covariance (MANCOVA), the dependent variable should be normally distributed within each group.
2. Homogeneity of variances: In multivariate analysis of covariance (MANCOVA), homogeneity of variances is assumed or it is assumed that the variance of all groups is equal.
3. Multivariate normality: Multivariate analysis of covariance (MANCOVA) is very sensitive with the outlier. In the case of multivariate analysis of covariance (MANCOVA), multivariate normality is required.
4. Level and Measurement of the Variables: In multivariate analysis of covariance (MANCOVA), dependent and covariate variables should continue as metric variables. In multivariate analysis of covariance (MANCOVA), grouping variables should be nominal.

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