FI:MA012 Statistics II - Informace o předmětu

MA012 Statistics II

Rozsah

2/2/0. 3 kr. (plus ukončení). Doporučované ukončení: zk. Jiná možná ukončení: k, z.
Vyučováno kontaktně

Vyučující

Mgr. Ondřej Pokora, Ph.D. (přednášející)

Garance

Mgr. Ondřej Pokora, Ph.D.
Katedra teorie programování – Fakulta informatiky
Dodavatelské pracoviště: Ústav matematiky a statistiky – Ústavy – Přírodovědecká fakulta

Rozvrh

Čt 26. 9. až Čt 19. 12. Čt 16:00–17:50 A217

• Rozvrh seminárních/paralelních skupin:

MA012/01: Čt 26. 9. až Čt 19. 12. Čt 18:00–19:50 A215, O. Pokora
MA012/02: St 25. 9. až St 18. 12. St 8:00–9:50 A320, O. Pokora
MA012/03: St 25. 9. až St 18. 12. St 10:00–11:50 A320, O. Pokora

Předpoklady

Basic knowledge of calculus: function, derivative, definite integral.
Basic knowledge of linear algebra: matrix, determinant, eigenavlues, eigenvectors.
Knowledge of probability a and statistics and practice with statistical language R within the scope of course MB153 Statistics I or MB143 Design and analysis of statistical experiments. Students without these knowledges and without practice with R are adviced to complete the course MB153 first.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

• Informatika (program FI, B-INF) (2)

Cíle předmětu

This is an advanced course which introduces students to more complex methods of mathematical statistics. It expands the knowledge from a basic course of statistics and add further methods. The lectures explains the mathematical background, algorithms, computational procedures and conditions, seminars lead to practical use of the methods for the analysis of datasets in statistical software R and to interprete the results. After completing the course, the student will understand advanced statistical methods and inferential principles (estimations, hypothesis testing). The student will be able to use this methods in analyzing datasets and will be able to statistically interpret the achieved results.

Výstupy z učení

After completing the course the student will be able to:
- explain the principles and algorithms of advanced methods of mathematical statistics;
- perform a statistical analysis of a real dataset using tidyverse packages in software R;
- interpret the results obtained by the statistical analysis.

Osnova

• Analysis of variance (ANOVA).
• Nonparametric tests – rank tests.
• Goodness-of-fit tests.
• Correlation analysis, correlation coefficients.
• Multiple regression.
• Regression diagnostics.
• Autocorrelation and multicollinearity.
• Principal component Analysis (PCA).
• Logistic regression and other generalized linear models (GLM).
• Contingency tables and independence testing.
• Bootstrapping.

Literatura

• Navarro D. Learning Statistics with R. https://learningstatisticswithr.com/
• SCHUMACKER, Randall E. Learning statistics using R. Los Angeles: Sage, 2015, xxiii, 623. ISBN 9781452286297. info
• FIELD, Andy P., Jeremy MILES a Zoë FIELD. Discovering statistics using R. First published. Los Angeles: Sage, 2012, xxxiv, 957. ISBN 9781446200452. info
• DAVIES, Tilman M. The book of R : a first course in programming and statistics. San Francisco: No Starch Press, 2016, xxxi, 792. ISBN 9781593276515. info

Výukové metody

Lectures and practical classes with computers (using R language with tidyverse environment).

Metody hodnocení

Evaluation is based on: 1) ROPOTS and problem solving suring practical classes – weight = 40 %, 2) final written exam – weight = 60 %. At least 50 % of averall points is needed to pass.

Vyučovací jazyk

Angličtina

Informace učitele

https://is.muni.cz/auth/el/fi/podzim2024/MA012/index.qwarp
Detailed information, schedule of lectures and practical classes and study materials for the current period are posted in the Interactive syllabus in IS.

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