# FI:MV013 Statistics - Course Information

## MV013 Statistics for Computer Science

**Faculty of Informatics**

Spring 2018

**Extent and Intensity**- 2/2/0. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
**Teacher(s)**- doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)

Mgr. Markéta Janošová (seminar tutor) **Guaranteed by**- doc. PaedDr. RNDr. Stanislav Katina, Ph.D.

Faculty of Informatics

Supplier department: Faculty of Science **Timetable**- Tue 8:00–9:50 B204
- Timetable of Seminar Groups:

*M. Janošová*

MV013/02: Mon 16:00–17:50 A219,*M. Janošová* **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 22 fields of study the course is directly associated with, display
**Course objectives**- The main goal of the course is to become familiar with some basic principles of data science and 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 base on Wald principle, likelihood and score principle connecting the statistical theory with implementation in R, geometry, and statistical graphics; 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 discrete data;

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

- to implement statistical methods of for continuous and discrete data to R. **Syllabus**- Why computer scientists should study statistics?
- Computer science related problems with analysed data
- Why the thought study based on data is useful?
- Data types
- Sampling
- Parametric probabilistic and statistical models
- Likelihood principle and parameter estimation using numerical methods
- Descriptive statistics (tables, listings, figures)
- From description to statistical inference
- Hypothesis testing and parameters of a model
- Goodness-of-fit tests
- Testing hypotheses about one-sample
- Testing hypotheses about two-samples
- Testing hypotheses about more than to sample problems
- Interpretation of statistical findings

**Literature**- CASELLA, George and Roger L. BERGER.
*Statistical inference*. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002, xxviii, 66. ISBN 0534243126. info

- CASELLA, George and Roger L. BERGER.
**Teaching methods**- Lectures, practicals.
**Assessment methods**- Homework (project), oral exam.
**Language of instruction**- English
**Further Comments**- Study Materials

The course is taught annually.

- Enrolment Statistics (Spring 2018, recent)
- Permalink: https://is.muni.cz/course/fi/spring2018/MV013