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PřF:M6130 Computational statistics - Course Information

## M6130 Computational statistics

**Faculty of Science**

Spring 2022

**Extent and Intensity**- 2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).

Taught in person. **Teacher(s)**- RNDr. Marie Budíková, Dr. (lecturer)
**Guaranteed by**- doc. PaedDr. RNDr. Stanislav Katina, Ph.D.

Department of Mathematics and Statistics - Departments - Faculty of Science

Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science **Prerequisites**-
**M7521**Probability and Statistics ||**M3121**Probability and Statistics I

M7521 or M3121 **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 11 fields of study the course is directly associated with, display
**Course objectives**- The aim of the course is to teach students

perform exploratory analysis of one-dimensional and multidimensional data;

use parametric and nonparametric tests of one, two or more populations;

analyze data dependencies;

perform goodness–of-fit tests. **Learning outcomes**- At the end of this course, students

will have a good knowledge of STATISTICA system;

would be able to describe real data sets using tables, statistical graphs and numerical characteristics;

would be able to testing statistical hypothesis using parametrics and nonparametrics tests. **Syllabus**- Exploratory data analysis: table of frequencies, contingency tables, functional and numerical characteristics of the data set, diagnostic graphs.
- Tests for normal distribution parameters: t-test, paired samples t-test, two-tailed-test, F-test, one-way ANOVA.
- Nonparametric Statistics: rank and rank Statistics, Wilcoxon and sign test, Kruskal - Wallis and median test.
- Goodness-of-fit tests: Kolmogorov's - Smirnov test, Liliefors test, chi-square test
- Tests of hypotheses on independence in multivariate samples: Pearson's correlation coefficient and its testing, Spearman's correlation coefficient, analysis of contingency tables.

**Literature**- BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH.
*Základní statistické metody*. Vydání první. Brno: Masarykova univerzita, 2005. 180 pp. ISBN 80-210-3886. info

*required literature*- 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 - ZVÁRA, Karel.
*Biostatistika*. 1. vyd. Praha: Karolinum, 1998. 210 s. ISBN 8071847739. info - ANDĚL, Jiří.
*Statistické metody*. 1. vydání. Praha: MATFYZPRESS, 1993. 246 s. info - CLEVELAND, William S.
*Visualizing data*. Murray Hill: AT & T Bell Laboratories, 1993. 360 s. ISBN 0-9634884-0-6. info

*recommended literature*- BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH.
**Teaching methods**- The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software STATISTICA in computer classroom.
**Assessment methods**- During the semester, students write two tests. The examination is written with "open book" and is complemented by practical computer aided data analysis. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
**Language of instruction**- Czech
**Further comments (probably available only in Czech)**- The course is taught annually.

The course is taught: every week.

General note: Jedná se o inovovaný předmět Základní statistické metody.

- Enrolment Statistics (Spring 2022, recent)
- Permalink: https://is.muni.cz/course/sci/spring2022/M6130