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The course covers a comprehensive range of topics in statistics. It begins with an exploration of descriptive statistics, delving into various types of data and their graphical representations, along with concepts like means, variability, contingency, correlation, and simple linear regression. An essential component of the course involves an introduction to the statistical software R, where students learn about its syntax, functions, and graphical procedures. Moving on, the course introduces probability theory, shedding light on elementary probability rules, random variables, common distribution families, and sampling distributions. Another crucial aspect covered is statistical inference, encompassing point estimation, confidence intervals, and hypothesis testing, as well as the application of multiple linear regression. Throughout the course, practical implementation and hands-on learning in R play a pivotal role in understanding and applying these statistical concepts effectively.


Alan Agresti, Christine Franklin, “Statistics: The Art and Science of Learning from Data” Pearson; 4th International Edition, ISBN 9781447964186.


W. N. Venables, D. M. Smith, “An Introduction to R”, Version 4.3.1 (2023-06-16), : https://cran.r-project.org/doc/manuals/R-intro.pdf

Teaching methods

Instruction within the classroom setting, interactive exercises, and analysis of real-world case studies.

Exam Rules

The final (oral) exam may consist of: theoretical questions, (oral) discussion of statistical exercises, implementations of functions and interpretation of some outputs in R.
The final exam of non-attending students will be covering all the topics of the course.