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Probability & Statistics

Description

The class provides an introduction to probability theory, descriptive statistics (data exploration and graphical inference; measures of central tendency, dispersion, and correlation; simple and multiple linear regression), and inferential statistics (sampling distributions, confidence intervals, significance testing). Theoretical concepts in the course will be illustrated with real-life examples and datasets, which students will analyse using the R software environment.

 

 

REFERENCES

  • Elements of information theory [Archivio elettronico] / Thomas M. Cover, Joy A. Thomas. - Hoboken, N.J. : Wiley-Interscience, 2006.
  • An introduction to probability theory and its applications / William Feller. -  New York [etc.] : J. Wiley, 1968-1971.
  • Hogg, Tanis, Zimmerman: Probability and Statistical Inference, Ninth Edition (2015)
  • Diez, Barr, Cetinkaya-Rundel: OpenIntro Statistics, Third edition (2017) https://www.openintro.org/stat/textbook.php?stat_book=os
  • Darrell Huff: How to lie with statistics (1954)
  • Lecture notes will be made available on the e-learning platform.

People

 

Eynard D.

Course director

Baumann V.

Assistant

Hansen A.

Assistant

Additional information

Semester
Fall
Academic year
2017-2018
ECTS
6
Education
Bachelor of Science in Informatics, Core course, Lecture, 2nd year

Master of Science in Management and Informatics, Management track, Lecture, 1st year