MA012 Statistics II

Faculty of Informatics
Autumn 2022
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Mgr. Markéta Zoubková (seminar tutor)
Guaranteed by
Mgr. Ondřej Pokora, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 16:00–17:50 A217
  • Timetable of Seminar Groups:
MA012/01: Mon 18:00–19:50 A215, O. Pokora
MA012/02: Thu 16:00–17:50 A215, M. Zoubková
MA012/03: Thu 14:00–15:50 A215, M. Zoubková
Prerequisites
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.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 25 fields of study the course is directly associated with, display
Course objectives
The course introduces students to advanced methods of mathematical statistics – explains the algorithms, computational procedures, conditions, interpretation of results and practical use of these methods for the analysis of datasets in statistical software R. 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.
Learning outcomes
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.
Syllabus
  • 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.
Literature
  • 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 and 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
Teaching methods
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 and of discussions.
Assessment methods
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.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/fi/podzim2022/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.
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2022, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2022/MA012