FI:MA012 Statistics II - Course Information

MA012 Statistics II

Extent and Intensity

2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).

Teacher(s)

RNDr. Marie Budíková, Dr. (lecturer)
RNDr. Ivo Moll, CSc. (lecturer)
Mgr. Tomáš Lerch (seminar tutor)
prof. RNDr. Luboš Brim, CSc. (assistant)

Guaranteed by

prof. RNDr. Ivanka Horová, CSc.
Departments – Faculty of Science

Timetable

Tue 12:00–13:50 B204

• Timetable of Seminar Groups:

MA012/01: Mon 8:00–9:50 B116, Mon 8:00–9:50 B007, T. Lerch
MA012/02: Mon 10:00–11:50 B116, Mon 10:00–11:50 B007, T. Lerch

Prerequisites (in Czech)

Statistika II předpokládá znalost základů statistiky získaných např. po absolvování předmětu Statistika I.

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

• Applied Informatics (programme FI, N-AP)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, N-AP)
• Information Systems (programme FI, N-IN)
• Informatics (programme FI, M-IN)
• Informatics (programme FI, N-IN)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Systems (programme FI, N-IN)
• Embedded Systems (eng.) (programme FI, N-IN)
• Theoretical Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Image Processing (programme FI, N-AP)

Course objectives

Random samples, point and interval estimators of parametrs and parametrical functions, statistical hypotheses testing, correlation and regression analysis.

Syllabus

• Basic ideas of inferential statistics. Samples and sample characteristics.
• Properties of the point estimators.
• Properties of the normal and asymptotically normal samples.
• Interval estimators.
• Statistical hypotheses testing.
• Analysis of correlation.
• Multidimensional linear regression.
• Statistical computation pacquets.

Literature

• ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 2.,přepracované vyd. Brno: Masarykova univerzita Brno, 1998, 127 pp. ISBN 80-210-1832-1. info
• OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
• http://home.zcu.cz/~friesl/hpsb/

Assessment methods (in Czech)

Výuka probíhá každý týden v rozsahu 2 hodiny přednášek, 2 hodiny cvičení. Nutnou podmínkou udělení zápočtu je vypracování zápočtového příkladu. Zkouška písemná, sestává z testové části a části s příklady.

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

• Enrolment Statistics (Autumn 2007, recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2007/MA012