4108137: C15 Business Statistics II

Description

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

Topics

• 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

• 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.

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

• 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.