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.

Compulsory reading

• Wooldridge, J.M. (2013). Introductory Econometrics. (International Student Edition, 5th edition). Mason: South Western Cengage Learning.

Further reading

• Sweeney, D.J., Williams, T.A., David R. Anderson, D.R. (2009). Fundamentals of Business Statistics (International Student Edition, 5th edition). Manson: South-Western Cengange Learning.
• Berensen, M.L., Levine, D.M., Krehbiel, T.C. (2012). Basic Business Statistics (Global Edition, 12th edition), Essex: Pearson Education Limited.

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.

• Einführung in die Steuerwissenschaften
• Steuersysteme: Liechtenstein, Deutschland, Österreich, Schweiz
• Lösungsschema und Zusammenspiel: Nationales und Internationales Steuerrecht
• Nationales und internationales Steuerrecht: Liechtenstein, Deutschland, Österreich, Schweiz
• Fallstudien: Besteuerung natürlicher und juristischer Personen (Liechtenstein, Deutschland, Österreich, Schweiz)
• Vergleichende Analyse der Steuersysteme
• Anwendung von Doppelbesteuerungsabkommen

schriftliche Prüfung am Ende des Semesters

Beschreibende Statistik mit Kennzahlen (Lagemasse, Streuungsmasse, Zusammenhangsmasse) und graphischen Darstellungen (Stamm-Blatt-, Stab-, Balken-, Streudiagramme, Histogramme, Box-Plot) für uni- und bivariate Daten und einfache lineare Regression (Prinzip der kleinsten Quadrate, ANOVA-Tabelle, R^2-Koeffizient, Standardfehler der Residuen, Tukey-Anscombe-Plot)
Wahrscheinlichkeitsrechnung (Definition eines Wahrscheinlichkeitsraumes, Additionssätze, Laplace-Modelle, Kombinatorik, Pfadregeln, Satz von Bayes, Zufallsvariablen und ihre Verteilungen, Lage- und Streuungsmasse sowie Schiefe und Kurtosis, Rechenregeln für Erwartungswert und Varianz, Binomial-, Normal-, t-, Chi-Quadrat-, F-Verteilung, Grenzwertsätze)
Schliessende Statistik (Grundlagen des Testens von Hypothesen mit Hypothesen, Fehler 1. Art, Fehler 2. Art, Teststatistik, Entscheidungsregel, p-Wert, Anwendungen zu Binomial-, t- und F-Tests, Punkt- und Intervallschätzungen für Mittelwerte und Wahrscheinlichkeiten)