MA012 Statistics II

Fakulta informatiky
podzim 2023
Rozsah
2/2/0. 3 kr. (plus ukončení). Doporučované ukončení: zk. Jiná možná ukončení: k, z.
Vyučující
Mgr. Ondřej Pokora, Ph.D. (přednášející)
RNDr. Radim Navrátil, Ph.D. (cvičící)
Garance
Mgr. Ondřej Pokora, Ph.D.
Katedra teorie programování – Fakulta informatiky
Dodavatelské pracoviště: Ústav matematiky a statistiky – Ústavy – Přírodovědecká fakulta
Rozvrh
Út 8:00–9:50 A318
  • Rozvrh seminárních/paralelních skupin:
MA012/01: St 18:00–19:50 B011, O. Pokora
MA012/02: St 16:00–17:50 B011, O. Pokora
MA012/03: Út 16:00–17:50 A215, R. Navrátil
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
předmět má 25 mateřských oborů, zobrazit
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
Classes are in full-time form: 2 hours of lectures, 2 hours of practical classes a week.
Practical classes consist of work in statistical software R using tidyverse packages.
Metody hodnocení
Exercises: attendance and active involvement in problem solving and homeworks, working with ROPOTs, solving interim and final problems. Final examination: full-time form – written exam. ROPOTs, final problem solving and the exam are evaluated in points. For successful completion of the course, to achieve at least 50 % of total sum of points is necessary.
Vyučovací jazyk
Angličtina
Informace učitele
https://is.muni.cz/auth/el/fi/podzim2023/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.
Další komentáře
Studijní materiály
Předmět je vyučován každoročně.
Předmět je zařazen také v obdobích podzim 2002, podzim 2003, podzim 2004, podzim 2005, podzim 2006, podzim 2007, podzim 2008, podzim 2009, podzim 2010, podzim 2011, podzim 2012, podzim 2013, podzim 2014, podzim 2015, podzim 2016, podzim 2017, podzim 2018, podzim 2019, podzim 2020, podzim 2021, podzim 2022, podzim 2024.