# FI:MV013 Statistics for CS - Course Information

## MV013 Statistics for Computer Science

**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: z (credit).
**Teacher(s)**- prof. Mgr. Petr Hasil, Ph.D. (lecturer)

RNDr. Radim Navrátil, Ph.D. (lecturer)

Reza Dastranj, MSc (seminar tutor)

Mgr. Pavel Morcinek (seminar tutor) **Guaranteed by**- prof. Mgr. Petr Hasil, Ph.D.

Department of Mathematics and Statistics – Departments – Faculty of Science

Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science **Prerequisites**- Basic knowledge of mathematical analysis: functions, limits of sequences and functions, derivatives and integrals of real and multidimensional functions.

Basic knowledge of linear algebra: matrices and determinants, eigenvalues and eigenvectors.

Basic knowledge of probability theory: probability, random variables and vectors, limit theorems. **Course Enrolment Limitations**- The course is offered to students of any study field.
**Course objectives**- The main goal of the course is to become familiar with some basic principles of statistics, with writing about numbers (presenting data using basic characteristics and statistical graphics), some basic principles of likelihood and statistical inference; to understand basic probabilistic and statistical models; to understand and explain basic principles of parametric statistical inference for continuous and categorical data; to implement these techniques to R language; to be able to apply them to real data.
**Learning outcomes**- Student will be able:

- to understand principles of likelihood and statistical inference for continuous and discrete data;

- to select suitable probabilistic and statistical model for continous and discrete data;

- to use suitable basic characteristics and statistical graphics for continous and discrete data;

- to build up and explain suitable statistical test for continuous and discrete data;

- to apply statistical inference on real continuous and discrete data;

- to apply simple linear regression model including ANOVA on real continuous data;

- to implement statistical methods of continuous and discrete data to R. **Syllabus**- What is statistics? Motivation and examples.
- Exploratory data analysis
- Revision of probability theory
- Parametric models - methods for parameter estimation
- Confidence intervals and hypothesis testing
- Testing hypotheses about one-sample
- Testing hypotheses about two-samples
- ANOVA
- Testing for independence
- Nonparametric tests
- Linear regression models

**Literature****Teaching methods**- Lectures, practical exercise classes with computers.
**Assessment methods**- Homeworks and tests during the semester (40 points), final written exam (60 points). At least 50 % of averall points is needed to pass.
**Language of instruction**- English
**Further comments (probably available only in Czech)**- The course is taught annually.

The course is taught: every week. **Teacher's information**- Capacity of the course is limited. Registration is required.

- Enrolment Statistics (Spring 2025, recent)
- Permalink: https://is.muni.cz/course/fi/spring2025/MV013