Short description
This course covers some statistical methods that can help to take decisions in business using data. These basic concepts of the statistical testing and estimating theory should – to a large extent - be known from an introductory course on probability theory and statistics in any bachelor program.

Topics

• Graphical and numerical characterizations of random variables and their distributions
• Framework and basic applications of testing hypotheses and estimating parameters
• Ordinary least squares method and its properties
• Simple linear regression including parameter estimation, diagnostic plots, hypothesis testing, predictions and model specifications using log-transformations
• Introduction to the software package R

• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
• Students are usually asked to read corresponding parts of the lecture notes or of the textbook in order to prepare for the upcoming lectures in advance.
• In the interactive lectures, statistical concepts will be introduced and motivated by discussing examples in detail. Assignments are offered to train these skills.
• During office hours, individual problems may be discussed with the lecturer.
• In order to analyse realistic data, the software package R will be used.

• Students present the distributions of random variables graphically, calculate and interpret their moments.
• Students can explain the framework of testing hypotheses and estimating parameters and apply basic procedures.
• Students criticize the assumptions of basic testing and estimating procedures and generalize the conclusions correctly.
• Students derive the minimal sample size for basic testing and estimating procedures.
• Students apply the ordinary least squares method to derive estimators and compare the statistical properties of different estimators.
• Students explain the classical linear model assumptions, run simple linear regressions, check the diagnostics plots, use log-transformations to specify models and interpret the results correctly.

Abstraktes Modul zur Anerkennung von Leistungen im Auslandssemester.

The module aims at understanding technology and innovation management in companies, answering questions such as the definition of innovation and how it can be promoted or the connection between strategy and innovation. In addition to inputs in general lecture style, the students will develop and prototype a product or service based on a given technology.

Further core topics include:
- Importance and impact of technology and innovation management on society and industry, as well as the importance of according strategies
- Industry dynamics of technological innovation (sources of innovation, types and patterns, standards and design dominance, timing
- Formulating of technological innovation strategy (definition of the organization's strategic direction, choosing innovation projects, collaboration strategies, protecting of innovation)
- Implementing technological innovation strategy (organization for innovation, managing the new product development process, managing the new product development teams, crafting a deployment strategy)

The following conditions need to be met prior to registering for the module:

• successful completion of English I
• successful completion of additional first year programme modules amounting to at least 45 credit points.

• Introduction into Taxation
• Tax Systems: Liechtenstein, Austria, Germany and Switzerland
• Solution scheme and interaction: National and international tax law
• National and international tax laws: Liechtenstein, Germany, Austria, - Switzerland
• Case Studies: Taxation of natural and legal persons (Liechtenstein, Germany, Austria, Switzerland)
• Comparative Analysis of tax systems
• Application of double tax agreements

schriftliche Prüfung am Ende des Semesters

Descriptive statistics with numerical measures, graphical and tabular representations (measures of location, measures of variability, measures of association between two variables, pie charts, dot plots, bar charts, histograms, box plots, scatter plots, contingency tables), simple linear regression (ordinary least squares, ANOVA-table, R-squared, residual standard error, Tukey-Anscombe plot)
Probability theory (definition of a probability space, general addition rules, Laplace models, combinatorics, decision trees, Bayes' theorem, random variables and their distributions, measures of location, variability, skewness and curtosis as measures for the shape of distribution, calculation rules for expectations, variances and covariances, binomial, normal, t-, chi-square and F-distributions, central limit theorem), Statistical inference (basic notions of statistical testing procedures like hypotheses, type 1 error, type 2 error, test statistic, decision rule, p-value, power; applications of binomial, t-, F- and chi-square-tests; point and interval estimates for probabilities and means)