MDA401 Statistics

Faculty of Science
Spring 2026
Extent and Intensity
0/0/0. 12 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
prof. RNDr. Michal Veselý, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Michal Veselý, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
PROGRAM(B-DAE)
calculus in one and several variables, basics of linear algebra, probability theory
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Introductory course to educate students in principles of statistical inference, regression models, classification, clustering and forecasting
Learning outcomes
Students will understand statistics as the principled way of handling uncertainty due to limited knowledge; understand general principles of statistical thinking, main tasks of statistical inference and methods of solving them; be familiar with important regression and time series models and statistical learning methods; be able to formulate application problems in statistical terms and identify the right methods to solve them; implement the solution in R statistical software; interpret results and quantify uncertainty of findings.
Syllabus
Principles and methods of statistical inference (parameter estimation, confidence intervals, hypothesis testing, prediction), regression models (linear and generalized linear models, smoothing splines and generalized additive models, penalized regression and regularization), classification and clustering, time series analysis.
Literature
    recommended literature
  • JAMES, Gareth R.; Daniela WITTEN; Trevor HASTIE and Robert TIBSHIRANI. An introduction to statistical learning : with applications in R. Second edition. New York: Springer, 2021, xv, 607. ISBN 9781071614174. info
  • WASSERMAN, Larry. All of statistics : a concise course in statistical inference. New York: Springer, 2010, xi, 442. ISBN 9780387217369. info
Teaching methods
Essentially asynchronous approach; complemented by synchronous communication with the tutor upon agreement
Assessment methods
Written and oral exam. The written exam is for max 40 points. For successful examination (the grade at least E), the student needs in total 18 points or more.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught every week.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2026/MDA401