MB153 Statistics I

Faculty of Informatics
Spring 2025
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).
In-person direct teaching
Teacher(s)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Mgr. Michaela Marčeková (seminar tutor)
Mgr. Tomáš Pompa (seminar tutor)
Mgr. Jakub Záthurecký, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 20. 2. to Thu 15. 5. Thu 18:00–19:50 A,01026
  • Timetable of Seminar Groups:
MB153/01: Tue 18. 2. to Tue 13. 5. Tue 16:00–17:50 A320, J. Záthurecký
MB153/02: Tue 18. 2. to Tue 13. 5. Tue 18:00–19:50 A320, J. Záthurecký
MB153/03: Mon 17. 2. to Mon 12. 5. Mon 18:00–19:50 A320, J. Záthurecký
MB153/04: Mon 17. 2. to Mon 12. 5. Mon 14:00–15:50 A320, M. Marčeková
MB153/05: Mon 17. 2. to Mon 12. 5. Mon 16:00–17:50 A320, M. Marčeková
MB153/06: Tue 18. 2. to Tue 13. 5. Tue 8:00–9:50 C119, T. Pompa
Prerequisites
( MB151 Linear models || MB152 Calculus || PřF:M1110 Linear Algebra I || PřF:M1100 Mathematical Analysis I ) && !NOW( MB143 Des. and anal. of experiments )
Prerequisites: calculus in one and several variables, basics of linear algebra. MB143 is a lightweight version of MB153.
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
Course objectives
Introductory course to educate students in descriptive statistics, theory of probability, random values and probabilistic distributions, including the theory of hypothesis testing.
Learning outcomes
Upon completing this course, students will be able to perform basic computer aided statistical data set analysis in R language, resulting in tables, graphs and numerical characteristics; will understand basic probability concepts; will be able to solve probability tasks related to explained theory (in some cases using statistical software); will be able to generate realizations of selected types random variables using statistical software; has basic knowledge of statistical hypothesis testing, will be able carry out tests in statistical software and interpret the results.
Syllabus
  • Introduction to the probability theory.
  • Random variables and vectors. Probability distribution and distribution function.
  • Discrete and continuous random variables and vectors. Typical distribution laws. Simultaneous and marginal distributions.
  • Stochastic independence of random variables and vectors. The sequence of independent trials.
  • Quantiles, expectation, variance, covariance, correlation coefficient and their properties.
  • Weak law of large number and central limit theorem.
  • Data files, empirical characteristics and graphs, numerical characteristics. Descriptive statistics in R language.
  • Random sample, point and interval estimators, maximal likelihood estimators.
  • Basics of testing hypothesis. Testing hypothesis in R language.
  • Regression analysis in R language.
Literature
    recommended literature
  • FORBELSKÁ, Marie and Jan KOLÁČEK. Pravděpodobnost a statistika I. 1. vyd. Brno: Masarykova univerzita, 2013. Elportál. ISBN 978-80-210-6710-3. url info
  • FORBELSKÁ, Marie and Jan KOLÁČEK. Pravděpodobnost a statistika II. 1. vyd. Brno: Masarykova univerzita, 2013. Elportál. ISBN 978-80-210-6711-0. url info
    not specified
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 3rd ed. Brno: Masarykova univerzita, 2004, 127 pp. ISBN 80-210-3313-4. info
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 8131503941. info
  • HOGG, Robert V. and Allen T. CRAIG. Introduction to mathematical statistics. 3rd ed. New York: Macmillan Publishing, 1970, x, 415. info
Teaching methods
Lectures, Exercises
Assessment methods
The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students fill in question sets and solve practical task in R. The examination is written: theory and examples. Evaluation has 2 phases: 1.Filling sets of questions through the semester - 40% points. 2.Final exam - 60%. 50% of points is needed to pass.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2025/MB153