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Submitted by joaquim on 12 September 2022
Intended learning outcomes
  1. To recognize the matrix representation of multivariate data, knowing the multivariate normal distribution characteristics and applying the descriptive measures and the graphical representations on the multivariate data characterization.
  2. To apply methodologies that facilitate the understanding of the data, namely dimension reducing, and to identify its main features.
  3. To assess the need for the use of cluster analysis techniques and apply the methods of classification of objects in different groups according to a statistical distance function.
  4. To apply statistical methods of classification and determine functions of certain variables observed that enable to discriminate between these groups or classes.
  5. To apply multidimensional scaling (MDS) techniques. Use an MDS algorithm and interpret the graphical representation of the data.
  6. To implement multivariate methodologies through statistical software, in order to solve real problems.


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