The module is designed to provide a detailed treatment on different
multivariate statistical analysis techniques commonly used at present as
well as the statistical framework underpinning these methods allowing
to enhance your ability to discuss and apply
multivariate data analysis techniques in practice.
This
course begins with some visual representations / graphics for
multivariate data and a short recapitulation of matrix algebra.
Hereafter
multivariate distributions (in particuluar, the normal distribution)
and their characteristics as well as the estimate and test theory in its
expansion to the multivariate case are presented. Finally, different
supervised and unsupervised multivariate techniques including cluster
analysis, discriminant analysis, principal component analysis, factor
analysis, multidimensional scaling, canonical correlation and graphical
models are discussed.
- Kursverantwortliche/r: Matthias Eckardt (WiWi)