4308137: C15 Business Statistics II

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Semester:SS 17
Scheduled in semester:2
Semester Hours per Week / Contact Hours:28.0 L / 21.0 h
Self-directed study time:69.0 h

Module coordination/Lecturers


Master's degree programme in Information Systems (01.09.2015)


Short description
This course generalizes the concepts of simple linear regression discussed in Business Statistics I to the case of multiple linear regression.


  • Classical linear model assumptions
  • Parameter estimation in multiple linear regression
  • Model diagnostics
  • Inference in multiple linear regression
  • Model specification techniques
  • Model selection techniques
  • Introduction to the software package R

Learning objectives
  • Students explain the classical linear model assumptions, run multiple linear regressions, check the diagnostics plots and interpret the results correctly.
  • Students apply inference procedures in multiple linear regression models and compare the advantages and disadvantages of different inference procedures.
  • Students apply specification techniques to improve the quality of models and interpret such models correctly.
  • Students apply selection techniques to choose appropriate models.

  • The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
  • Students are usually asked in advance to read corresponding parts of the lecture notes or of the textbook in order to prepare for the upcoming lectures.
  • 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.

Recommended previous knowledge
Business Statistics I

Compulsory reading
  • Wooldridge, J.M. (2013). Introductory Econometrics. (International Student Edition, 5th 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. (2014). Linear Models with R. (2nd edition). Boca Raton: Chapman & Hall/CRC.