Statistical problems that involve several or many variables. Descriptive methods and graphics, Covariance and correlation.Models for multivariate data, estimation of parameters.Discrimination analysis, Regression models, Principal components, biplot, factor analysis, unmixing.Similarity, scaling and cluster analysis.

Simple and multiple regression models, with emphasis on practical aspects and interpretation of results, analysis of residuals and model selection. Generalized linear models, in particular logistic regression.

Multivariate Statistics deals with joint distributions of several random variables. The course introduces the basic concepts and provides an overview over the diverse methods of multivariate statistics with emphasis on applications.

This course covers several generally useful statistical methods:Nonparametric tests, randomization tests, jackknife and bootstrap, as well as asymptotic approximations and robustness properties of estimators.

This course covers common numerical algorithms and statistical methods used by chemical engineers to solve typical problems arising in industrial and research practice.

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