Semester:WS 16/17
Art:Modul
Sprache:Englisch
ECTS-Credits:3.0
Plansemester:1
Lektionen / Semester:28.0 L / 21.0 h
Selbststudium:69.0 h
Art:Modul
Sprache:Englisch
ECTS-Credits:3.0
Plansemester:1
Lektionen / Semester:28.0 L / 21.0 h
Selbststudium:69.0 h
Modulleitung/Dozierende
- Dipl.-Phys. ETH Jochen Kalser
(Modulleitung)
Studiengang
Masterstudium Information Systems (01.09.2015)Lehrveranstaltungen
Beschreibung
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
Learning objectives
- 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.
Methods
- 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.
Entry requirements
We require basic knowledge of probability theory and statistics, which is usually presented in a basic course on these topics in any bachelor program. The module ''Statistik'' in the bachelor program at University of Liechtenstein serves as a guideline or benchmark for this previous knowledge.
This module is prerequisite for taking the Master’s thesis Module and writing the Master’s thesis
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.