MB153 Statistics I

Faculty of Informatics
Spring 2024
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).
Taught in person.
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Bc. Tomáš Macháček (seminar tutor)
Mgr. Markéta Zoubková (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Faculty of Informatics
Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science
( MB151 Linear models || MB101 Mathematics I || MB201 Linear models B || MB152 Calculus || MB102 Calculus || MB202 Calculus B || PřF:M1110 Linear Algebra I || PřF:M1100 Mathematical Analysis I ) && ( ! MB103 Cont. models and statistics && ! MB203 Cont. models, statistics B && ! MV011 Statistics I )
Prerequisites: calculus in one and several variables, basics of linear algebra.
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 74 fields of study the course is directly associated with, display
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.
  • 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 coeficient 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.
  • Basics of testing hypothesis. Testing hypothesis in R language.
  • Regression analysis in R language.
    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
  • BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika (Descriptive Statistics). 3., doplněné vyd. Brno: Masarykova univerzita, 1998. 52 pp. ISBN 80-210-1831-3. info
  • ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993. 246 s. 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
Follow-Up Courses
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2021, Spring 2022, Spring 2023.
  • Enrolment Statistics (Spring 2024, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2024/MB153