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• Aspects of multivariate analysis, Assumptions behind multivariate structure.
• Organization of Data, Difference between univariate and multivariate statistical techniques.
• Matrix algebra and vector algebra
• Mean vector and covariance matrix
• maximum inequalities and maximization methods.
• Multivariate normal distribution, inferences about mean vector
• Estimation of the mean vector and covariance matrix.
• comparisons of several multivariate means
• Multivariate analysis of variance (MANOVA)
• Principal components analysis,
• Factor analysis,
• Discriminant analysis,
• Cluster analysis.
• Multidimensional scaling.
• Canonical Correlation
• software application