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Social Competence
  • Listen attentively to the letures and internalise points made by their fellow students.
  • Discuss the solutions of case studies and ask questions in unclear situations
  • Assess the solutions presented, evaluate them in relation to their own solution
  • Use the opportunity to find an independent solution for case studies and support eachother mutually in the correction process
  • stand up for and defend their own solution against criticism
Methodological Competence
  • know doctrine and administrative practice when interpreting tax laws
  • understand the steering mechanism and allocation effect of taxes and the intention of the legislator when imposing the provisions
  • Apply the provision of the respective national and international tax law as well as doctrine and administrative practice to case studies
  • Analyse the impact of different tax provisions
  • work on problems about the taxation of natural and legal persons by means of tax law and business taxation and discuss the cases from different perspectives
  • Evaluate the calculated tax burdens
Professional Competence
  • know the basics of business taxation and the national and international tax laws of the four German-speaking countries
  • understand the differences between the various tax systems of Liechtenstein, Germany, Austria and Switzerland
  • solve cross-border cases under consideration of the relevant legal provisions of the respective national tax law and the provisions of the tax treaties
  • calculate the tax burdens of national and legal persons in accordance with the respective national tax law and under consideration of tax treaties
  • identify the links of personal and objective tax liability of natural and legal persons in the four national tax jurisdictions
  • determine the application of the tax treaties
  • Solve cross-border case studies based on a schema
  • Evaluate the different tax burdens in accordance with the respective national tax law and under consideration of the tax treaties
Methodological Competence
  • Know the central statistical techniques that are often used in business applications.
  • Understand the meaning of statistical notions.
  • Use the introduced concepts in a purposeful way, interpret the results in the context and formulate their conclusions correctly.
  • Use basic commands of the software package R to analyze data graphically and numerically.
  • Apply standard learning techniques in abstract contexts so that they get used to working with scientific publications on their own.
  • Analyze data to justify decisions in business applications.
  • Analyze business cases using methods of probability theory.
  • Can critically check the content of statistical results while planning economic actions.
  • Argue in a precise and rational way in their comments.
  • Strengthen their skills to argue rationally in a scientific environment.
  • Judge the relevance of statistical conclusions and their limitations correctly.
  • Judge arguments critically whether they are sound, reasonable and consistent.
  • Judge the uncertainties in statistical conclusions correctly.
Personal Competence
  • Internalize the use of standard learning and working techniques to learn on their own.
Social Competence
  • Cooperate while working out problems or while preparing themselves for the final exam.
  • Formulate the findings from the analyses of empirical data using the terminology made available to them, to indicate the degree of uncertainty in the conclusions correctly.
  • Are able to argue in a rational and controversial way in a scientific environment and include different points of view in their considerations.
Professional Competence
  • Know about the roles of quantiles, variances, standard deviations and correlations to measure risks.
  • Know the axioms of a discrete probability space.
  • Know the most important distributions and their properties.
  • Know the importance of the central limit theorem.
  • Can describe univariate and bivariate data according to the level of scale using numerical measures and graphical representations.
  • Can explain the content of the axioms of a discrete probability space while modelling a random experiment.
  • Use the law of large numbers to interpret a probability as a relative frequency in the long run.
  • Can explain why and when a certain distribution is used to model economic situations.
  • Can name the basic idea of testing hypotheses referring to the possible types of errors.
  • Name the basic ideas of standard testing procedures.
  • Calculate the critical values in the decision rules of binomial tests.
  • Can explain the meaning of confidence intervals and indicate the duality between confidence intervals and testing hypotheses.
  • Use the principle of ordinary least squares to estimate the parameters of a regression model.
  • Run simple linear regressions, set up the ANOVA-table and judge the residual plot.
  • Calculate probabilities using addition rules, decision trees and combinatorics.
  • Can explain the results of Bayes' theorem.
  • Use limits theorems to approximate distributions and probabilities.
  • Use calculations rules for expectations and variances correctly and can explain their meanings in the context of risk measuring.
  • Calculate the critical values of binomial tests and the resulting probability of a type 2 error.
  • Evaluate the test statistics of standard procedures, read the corresponding critical values from statistical tables and formulate the conclusion of the testing procedure correctly in the given context.
  • Calculate confidence intervals and interpret them correctly in a given context.
  • Interpret measures as quantiles, variances, standard deviations, correlations, skewness, curtosis correctly.
  • Use the vocabulary introduced to them to describe graphical representations correctly and include the advantages and disadvantages of such representations while interpreting them.
  • Judge the certainty or uncertainty of statistical conclusions and formulate their interpretations accordingly.
  • Judge the practical relevance of a linear regression in the given context.
  • Judge the uncertainty in the conclusions from statistical testing procedures correctly.
Personal Competence
  • organisieren ihren Forschungsprozess (Literatursuche, Sichten, Ordnen, Schreiben)
  • können sich in Phasen der Überforderung auf die wesentlichen nächsten Schritte konzentrieren.
Social Competence
  • vermitteln anderen Studierenden die wesentlichen Punkte ihres Thesisprojekts
  • geben Feedback und unterstützen damit andere Studierende bei deren Exposé-Entwicklung
  • nehmen kritisch Stellung zu anderen Arbeiten, ohne dabei abwertend zu agieren.
Methodological Competence
  • wählen für ihr Thesisprojekt passende Methoden aus und rechtfertigen diese Auswahl
  • führen eine gezielte Literaturrecherche zur Untermauerung ihres Forschungsvorhabens durch
  • formulieren ein Exposé, in dem das Forschungsvorhaben beschrieben und begründet wird
  • halten sich bei der Erstellung des Exposés an die vorgegebenen wissenschaftlichen Standards
  • integrieren widersprüchliche Rückmeldungen in die Entwicklung ihrer Arbeit
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