3803767: Business statistics

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Semester:WS 14/15
Scheduled in semester:1
Semester Hours per Week / Contact Hours:60.0 L / 45.0 h
Self-directed study time:105.0 h

Module coordination/Lecturers


Master's degree programme in IT and Business Process Management (01.10.2008)


In this course we discuss some statistical methods that can help to take decisions in business using data. After reviewing the basic concepts of ''Testing and Estimating'', usually known from an introductory course on probability theory and statistics in any bachelor program, we introduce and discuss some aspects of ''Multiple Linear Regression Analysis'', which can be regarded as one of the practically most relevant statistical techniques.


  • 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
  • parameter estimation in multiple linear regression
  • classical linear model assumptions and model diagnostics
  • inference in multiple linear regression
  • model specification techniques
  • model selection techniques
  • introduction to the software package R


  • Lecture Series ''Testing and Estimating''

    • represent the distributions of random variables graphically.
    • calculate moments of random variables and interpret them in a given context.
    • explain the framework of testing hypotheses and estimating parameters.
    • apply basic testing and estimating procedures and generalize the conclusions correctly.
    • criticize the assumptions of basic testing and estimating procedures.
    • derive the minimal sample size for basic testing and estimating procedures.

    Lecture Series ''Multiple Linear Regression''

    • apply the ordinary least squares method to derive estimators.
    • analyze and compare the statistical properties of estimators.
    • explain the classical linear model assumptions.
    • run the calculations of a multiple linear regression for toy examples with small data sets by hand.
    • interpret the software outputs of multiple linear regression for application examples in the given context.
    • use model diagnostics to check the assumptions and to judge the quality of adapted models.
    • apply inference procedures in multiple linear regression models.
    • compare the advantages and disadvantages of different inference procedures.
    • construct testing procedures for multiple linear constraints in multiple linear regression models.
    • apply specification techniques to improve the quality of models.
    • apply selection techniques to choose appropriate models.
    • explain the framework of statistical reasoning.
    • judge the benefits and limits of statistical methods and conclusions.
    • summarize the results and conclusions of statistical analyses in a precise way.
    • select statistical procedures according to given situations and questions.
    • apply standard techniques in new situations and adapt the procedures.
    • appraise the content and the limits of statistical analyses in publications.

Lectures Method

Students are usually asked in advance to read corresponding parts of the textbook (Wooldridge, 2009) in order to prepare for the upcoming lectures. In the interactive lectures, we then introduce the statistical concepts and motivate them by discussing examples in detail. Assignments are then offered to train these skills. During the office hours, individual problems may finally be discussed with the lecturer. In order to analyze realistic data, the software package R will be used.

The same teaching methods will be used in the two different lecture series ''Testing and Estimating'' and ''Multiple Linear Regression Analysis''.

The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Admission 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: random variables and their distributions, expectation, median, mode, variance and covariance, standard deviation, skewness, kurtosis, null hypothesis, alternative, test statistic, decision rule, critical value, rejection area, one-sided versus two-sided test, p-value, confidence interval. The module ''Statistik'' in the bachelor program at University of Liechtenstein serves as a guideline or benchmark for this previous knowledge. Some of these aspects will be repeated in the lecture series ''Testing and Estimating''.


The students will be provided with the lecture slides and supporting materials (literature and exercises) throughout the course.

Compulsory reading:

  • Wooldridge, J.M. (2009). Introductory Econometrics. (International Student Edition, 4th edition). Mason: South Western Cengage Learning.

Further reading:
  • Montgomery, D.C., Peck, A.E. & Vining, G.G. (2012). Introduction to Linear Regression Analysis. (5th edition). New York: John Wiley & Sons.
  • Faraway, J.J. (2005). Linear Models with R. Boca Raton: Chapman & Hall/CRC.


  • PWW-MA_Business Statistics (WS 14/15, bewertet)
  • PWW-MA_Business Statistics (SS 15, bewertet)
  • PWW-MA_Business Statistics (SS 15, bewertet)