Statistics

This course aims to extend students' practical experience of statistical software environments; to extend students' abilities in applying ideas and methods already taught in a practical context, and to enable students to perform computer-assisted statistical analyses. It is intended for second and third year students registered on the undergraduate degree program offered by the Department of Statistical Science, or jointly with other departments. On successful completion of the course, a student should be able to independently perform a systematic analysis with the statistical software suites R and SAS to answer data-based or methodological questions, and report on it according to the scientific state-of-the-art.

This course provides an introduction to statistics. The underlying rigorous mathematical framework is provided for analyzing different forms of data and the interpretation of the corresponding results discussed in detail.

This course provides the probability theoretical basis for calculating the reliability of a system. This means the likelihood that a system functions when the reliability of the individual components the system consists of is known. Examples of systems are energy systems and networks. The course also deals with various examples of risk analysis in industrial applications. The course subject is illustrated by various simulation techniques. The course discusses topics including what coherent systems are, and how to represent such systems by paths and cuts; how to compute the reliability for systems of components; what discrete event simulations is, and how to apply different simulation techniques to do computations related to risk and reliability analysis; how to compute the reliability importance of components; and how to perform a risk and reliability analysis in various practical situations. This course is an introduction to more advanced studies, but is also suitable as an aid subject in other professional circles and as further and postgraduate education for realists and engineers.

This course provides an introduction to the concepts and use of statistics for economics and business. Topics include: statistical terms; types of variables; analysis of univariate data; analysis of bivariate data; probability and probability models; introduction to statistical inference. Classes are focused on problem solving and practical computing using statistical software.

In this course students develop the skills to formulate research questions on social inequality and to answer them quantitatively and empirically. The course conveys theoretical perspectives on social structure analysis on the basis of currently relevant empirical studies on inequality dynamics in Germany. At the end of the course, students are able to answer empirical questions on inequality dynamics. The focus is on the development of practical skills. Students examine in detail the evaluation of the data of the socio-economic panel, which are particularly relevant in this context. Basic knowledge in statistical procedures and in dealing with Stata or R is required.

This course teaches how to analyze a model by using a simulation study. Topics: pseudorandom number generation, generating discrete and continuous random variables, simulating discrete events; statistical analysis of simulated data, variance reduction, Markov Chain Monte Carlo methods. The course also covers topics in stochastic optimization such as simulated annealing.

Biostatistics (statistics applied in the health sciences) is a vital tool in the mission to improve health and well-being for all people. This course provides an introduction to the core principles and methods of biostatistics. The course provides understanding of how statistics is used to answer research questions: how to look for patterns in data and how to test hypotheses about disease causation and prevention and improvement in well-being. Topics covered include the nature of random variation, the concepts of bias and confounding, study design, data description including risks and odds, binomial and normal distributions, estimation, hypothesis testing, regression, the control of confounders, critical appraisal, and the analysis of variance.

This course aims to explore advanced topics in finance via mathematical and statistical methods in order to gain a better understanding of optimal decision making, risk management and derivative pricing techniques. It is primarily intended for third and fourth year undergraduate students and taught postgraduate students registered on the degree programs offered by the Department of Statistical Science, or jointly with other departments. The academic prerequisite for such students is STAT0013. On successful completion of the course, a student should be able to: define the concepts of risk aversion and stochastic dominance, and apply them to manage risk in, and rank capital projects; understand how dynamic programming can be used to make optimal decisions under uncertainty; understand how to apply mathematical and statistical modelling techniques to credit risk modelling, value-at-risk measurements and capital adequacy assessments; understand a range of modelling techniques used in derivative pricing, and the concepts and assumptions that underpin them; criticize and understand the limitations of these techniques as they are used in the modern finance industry.