Subject description
- Programming in the R programming environment and preparation of reproducible reports.
- Sampling, sampling distribution and generating a statistical sample.
- Monte Carlo simulations.
- Verification of methods for parameter estimation with simulations.
- Hypothesis testing, calculating test size and test power with simulations.
- Bootstrapping and resampling.
The subject is taught in programs
Objectives and competences
The course improves the knowledge of basic statistical concepts and their background by checking their validity with the help of computer capabilities. The student gets to know the key methods of statistical simulations and modeling. The course builds on student's independent use the R programming language and familiarizes the student with methods for preparing reproducible reports.
Teaching and learning methods
Lectures, practical work on computers, project work, individual projects.
Expected study results
Knowledge and understanding:
– programming in R,
– use of typsetting system markdown
– knowledge and use of state of the art computer tools for studying statistical concepts, methods and models.
Basic sources and literature
Knjige/ Books
- Rice, J.A. (2007) Mathematical Statistics and Data Analysis. 3rd ed., Duxbury Press.
Spletni viri/ Web sources:
- https://r4ds.had.co.nz/ (spletna knjiga: R for Data Science (G. Grolemund, H. Wickham))
- http://www.cookbook-r.com/ (spletna knjiga: Cookbook for R)
- https://bookdown.org/yihui/rmarkdown/ (spletna knjiga: RMarkdown: The Definitive Guide (Yihui Xie, J. J. Allaire, G. Grolemund))
Razpoložljiva literatura se letno spreminja in posodablja. Primerni viri so zbrani na spletni strani www.r-project.org, zato se bodo aktualni viri letno spreminjali.
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