# FI:MB153 Statistics I - Course Information

## 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. **Teacher(s)**- 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 **Prerequisites**- (
**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.
**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 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.

**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

*recommended literature*- 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

*not specified*- FORBELSKÁ, Marie and Jan KOLÁČEK.
**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)**- The course is taught annually.

The course is taught: every week.

- Enrolment Statistics (Spring 2024, recent)
- Permalink: https://is.muni.cz/course/fi/spring2024/MB153