Students know how to display economic data and how to recognize and describe data structures. They are familiar with fundamental aspects of data analysis and with standard economic applications like index numbers and concentration measures. Knowing basic (descriptive) time series methods, they are competent in working with
growth rates (transformation, aggregation, averaging etc.). As they can differentiate between correlation and causality and between stochastic and deterministic data patterns, they avoid (popular) misinterpretations in the analysis of economic data.
Students are familiar with basic methods of both descriptive and inferential statistics. They differentiate between concepts of central tendency, dispersion and dependence inherent to frequency- and probability distributions and they are able to formally analyse these phenomena. Students know the basic assumptions underlying statistical inference. Hence, they are able to solve economic problems by evaluating data samples using both appropriate methods and software (Microsoft Excel, statistical software pqrs). In particular, they are familiar with basic estimation and testing procedures. They are able to correctly interpret and to defend the results of their statistical analysis.
Students know what kind of data it takes to analyse economic problems empirically. They are able to set up a linear model, to evaluate the (multivariate) model by means of regression analysis and to check the underlying assumptions. They are used to calculate and interpret partial effects of the regressors, and they can test for significance of these effects. Hence, they can both carry out regression analyses themselves and also assess the validity of empirical studies as reported elsewhere. Information is the foundation of any “knowledge society”, and in this course students learn how to quantitatively evaluate information inherent in multivariate data. The statistical production process is reviewed starting from operationalization up to decision support aspects. This knowledge will turn out to be helpful in the students’ future occupational activities, in particular whenever economic problems have to be solved by means of multivariate data analysis. Moreover, the course provides a fundamental understanding of the quantitative topics covered in various courses in masters’ programs of any scientific discipline. In this course students get introduced to free statistic software GRETL, to manage their own data analysis.
Statistic as legal key competence - why?
In our everyday life statistical analyses play a more and more bigger role. So it is not a surprise, that quantitative data analysis in argumentations in lawsuits begin to get more common. Furthermore, current juridical research topics like "Knowledge generation in law and Big Data" or the actual principled debate about pros and cons of an "empirical turnover" in law sciences, require a deeper statistical knowledge of lawyers. Aim of the course is to make lawyers familiar with basic statistical methods for this.