## 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)
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
• ANOVA
• Testing for independence
• Nonparametric tests
• Linear regression models
Literature
• WASSERMAN, Larry. All of statistics : a concise course in statistical inference. New York: Springer, 2004, xix, 442. ISBN 9780387402727. info
• CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info
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.
The course is also listed under the following terms Autumn 2015, Autumn 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
• Enrolment Statistics (Spring 2025, recent)