PřF:M6130 Computational statistics - Course Information

M6130 Computational statistics

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

prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science

Timetable

Fri 10:00–11:50 M1,01017

• Timetable of Seminar Groups:

M6130/01: Wed 14:00–14:50 M4,01024, Wed 15:00–15:50 MP1,01014, M. Budíková
M6130/02: Tue 8:00–8:50 M6,01011, Tue 9:00–9:50 MP1,01014, M. Budíková

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

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.

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:histogram, empirical distribution function, moments, description of time series, multivariate data samples, graphical representation of dependence two or more variables. Nonparametric statistics: rank and rank statistics. Rank tests for one sample. Statistical tests employed with two samples: t-test, F-test, Wilcoxon and sign tests, comparison samples from binomial distributions. Statistical tests employed with three and more samples: ONEWAY, F-test, Kruskal-Wallis test, test of homogeneity for binomial samples. Goodness-of-fit tests: Kolmogorov-Smirnov test, chi-square test. Statistical tests employed with multivariate samples: Pearson's correlation coefficient, Spearman's correlation coefficient.

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 one test. 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 2018, recent)
  
• Permalink: https://is.muni.cz/course/sci/spring2018/M6130