PřF:M6130 Computational statistics - Course Information
M6130 Computational statistics
Faculty of ScienceSpring 2024
- Extent and Intensity
- 2/2/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
- Teacher(s)
- RNDr. Marie Budíková, Dr. (lecturer)
- Guaranteed by
- RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable
- Mon 19. 2. to Sun 26. 5. Thu 12:00–13:50 M4,01024
- Timetable of Seminar Groups:
M6130/02: Mon 19. 2. to Sun 26. 5. Tue 10:00–11:50 M4,01024, M. Budíková - Prerequisites
- M7521 Probability and Statistics || M3121 Probability and Statistics I || MUC51 Probability and Statistics
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
- Mathematics with a view to Education (programme PřF, B-EB)
- Mathematics with a view to Education (programme PřF, B-FY)
- Mathematics with a view to Education (programme PřF, B-GE)
- Mathematics with a view to Education (programme PřF, B-GK)
- Mathematics with a view to Education (programme PřF, B-CH)
- Mathematics with a view to Education (programme PřF, B-IO)
- Mathematics with a view to Education (programme PřF, B-MA)
- Upper Secondary School Teacher Training in Mathematics (programme PřF, N-EB)
- Upper Secondary School Teacher Training in Mathematics (programme PřF, N-FY)
- Upper Secondary School Teacher Training in Mathematics (programme PřF, N-CH)
- Upper Secondary School Teacher Training in Mathematics (programme PřF, N-MA)
- 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
- required literature
- BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. info
- recommended 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
- 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)
- Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Základní statistické metody.
- Enrolment Statistics (Spring 2024, recent)
- Permalink: https://is.muni.cz/course/sci/spring2024/M6130