MPE_MOIR Modelling in R

Ekonomicko-správní fakulta
podzim 2026
Rozsah
0/2/0. 4 kr. Ukončení: z.
Vyučováno kontaktně
Vyučující
doc. Ing. Daniel Němec, Ph.D. (cvičící)
MgA. Tereza Janáková (pomocník)
Garance
doc. Ing. Daniel Němec, Ph.D.
Katedra ekonomie – Ekonomicko-správní fakulta
Kontaktní osoba: Mgr. Jarmila Šveňhová
Dodavatelské pracoviště: Katedra ekonomie – Ekonomicko-správní fakulta
Předpoklady
No prior knowledge is required. The course is designed to provide students with a common foundation in statistics, econometrics, and data analysis using the R programming language. The necessary concepts are introduced and reinforced throughout the course, making it suitable for students from diverse academic backgrounds.
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Předmět si smí zapsat nejvýše 20 stud.
Momentální stav registrace a zápisu: zapsáno: 0/20, pouze zareg.: 12/20, pouze zareg. s předností (mateřské obory): 3/20
Mateřské obory/plány
Anotace

The course provides students with practical experience in using the R programming language for data analysis and introduces the fundamental statistical and econometric methods commonly used in applied economics, health economics, business, and related disciplines. It is primarily intended to establish a common foundation in data analysis and empirical research methods for students entering the Master's programme from diverse academic backgrounds.


The course is based on the first two parts of the textbook, Data Analysis for Business, Economics, and Policy, by Békés and Kézdi, and combines independent study with hands-on seminar sessions. Students are expected to study the assigned chapters before each class, while seminar sessions focus on applying the covered concepts to real-world datasets using the R programming language.


The course covers the fundamentals of data manipulation, visualisation, and exploratory data analysis, followed by an introduction to statistical inference, hypothesis testing, and basic econometric modelling. Particular emphasis is placed on the interpretation of statistical and econometric results, reproducible data analysis, and the critical evaluation of empirical evidence reported in applied research.



Upon successful completion of the course, students will be able to use R for data analysis, apply basic statistical and econometric methods, interpret empirical results, and critically evaluate quantitative evidence in applied economics and related disciplines.

Výstupy z učení

Upon successful completion of the course, students will be able to:

  • use the R programming language for data manipulation, visualisation, and exploratory data analysis;
  • apply basic statistical and econometric methods to the analysis of real-world datasets, including hypothesis testing, estimation, and prediction;
  • interpret and communicate the results of statistical and econometric analyses in the context of applied empirical research;
  • critically evaluate empirical findings reported in applied economics and related disciplines with respect to the methods used and the validity of the conclusions;
  • select appropriate statistical and econometric methods for solving basic empirical research problems;
  • conduct a reproducible data analysis in R and present its results in a clear and well-structured manner;
  • recognise situations in which more advanced statistical, econometric, or data science methods are required and identify suitable approaches for further analysis.
Klíčová témata
  • introduction to R and reproducible data analysis
  • data preparation, transformation, and visualisation
  • exploratory data analysis and descriptive statistics
  • statistical inference and hypothesis testing
  • correlation analysis and simple linear regression
  • multiple linear regression, model interpretation, and inference
  • working with complex datasets and model specification
  • regression models for binary outcomes
  • critical evaluation of empirical research and reproducible reporting in R
  • introduction to time series data and regression with time series
Studijní zdroje a literatura
    povinná literatura
  • BÉKÉS, Gábor a Gábor KÉZDI. Data analysis for business, economics, and policy. First published. Cambridge: Cambridge University Press, 2021, xxiii, 714. ISBN 9781108483018. info
    doporučená literatura
  • MANN, Prem S. Introductory statistics. Ninth edition. Hoboken: Wiley, 2019, 171 stran. ISBN 9781119148296. info
  • HEISS, Florian. Using R for introductory econometrics. 2nd edition. Düsseldorf: Florian Heiss, 2020, 368 stran. ISBN 9788648424364. info
Přístupy, postupy a metody používané ve výuce
The course is delivered entirely through hands-on seminar sessions held in computer laboratories. Students are expected to study the assigned textbook chapters before each class, while seminar sessions focus on applying the covered concepts to real-world datasets using the R programming language. Emphasis is placed on active participation, practical problem-solving, interpretation of statistical and econometric results, and discussion of methodological issues encountered during data analysis. Throughout the course, students develop reproducible analytical workflows in R. The course concludes with an individual reproducible empirical project, in which students independently analyse a dataset of their choice, document their analysis, and communicate their findings.
Způsob ověření výstupů z učení a požadavky na ukončení
Achievement of the learning outcomes is assessed through an individual final empirical project. Students independently formulate a research question, select an appropriate real-world dataset, and conduct a reproducible statistical or econometric analysis using the R programming language. The project should demonstrate the student's ability to apply appropriate analytical methods, correctly interpret results, and communicate the findings in a clear, well-structured report. Successful completion of the project is required for the award of course credit.
Náhradní absolvování
Alternative course completion is not available, as the course requirements are sufficiently flexible to accommodate individual study plans.
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Angličtina
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Předmět je zařazen také v obdobích podzim 2025.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/econ/podzim2026/MPE_MOIR