| dc.description.abstract | This bachelor thesis provides an introduction to multivariate statistics, which is
the analysis of data with multiple variables using statistical methods. The thesis
focuses on the generalization of the normal distribution to random vectors, properties of the multivariate normal distribution, and non-parametric kernel estimation
methods for estimating densities. Additionally, the thesis presents methods for
separating populations and classifying new observations within these populations
using multivariate statistics. The applications of these methods is demonstrated
using a real data set, and the accuracy of the classification is evaluated. | |
