FI:MA012 Statistics II - Course Information
MA012 Statistics II
Faculty of InformaticsAutumn 2011
- 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)
- Mgr. Martin Řezáč, Ph.D. (lecturer)
Mgr. Kateřina Opršalová (seminar tutor) - Guaranteed by
- prof. RNDr. Ivanka Horová, CSc.
Faculty of Informatics - Timetable
- Wed 14:00–15:50 A107
- Timetable of Seminar Groups:
MA012/02: Thu 18:00–19:50 G123, K. Opršalová - Prerequisites
- Statistics II assume knowledges of fundamental statistical concepts in range Statistics 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 (eng.) (programme FI, D-IN4)
- Informatics (programme FI, D-IN4)
- 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 and Technologies (eng.) (programme FI, D-IN4)
- Computer Systems and Technologies (programme FI, D-IN4)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- 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. The main goals of this course are: to introduce the principles of statistical induction; to explain the fundamentals of selected statistical tests including computer implementation; the definition of preconditions of these tests; to learn to interpret the test results.
- 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
- BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Tomáš LERCH. Základní statistické metody. Vydání první. Brno: Masarykova univerzita, 2005, 180 pp. ISBN 80-210-3886. 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
- ANDĚL, Jiří. Statistické metody. 1. vyd. Praha: Matfyzpress, 1993, 246 s. info
- OSECKÝ, Pavel. Statistické vzorce a věty. 1. vyd. Brno: Masarykova univerzita, 1998, [29] list. ISBN 8021017589. info
- Teaching methods
- Lectures, Exercises
- Assessment methods
- The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises. Throughout semester, students elaborate a semester project. The examination is written, consisting of test part and exercises part.
- Language of instruction
- Czech
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (Autumn 2011, recent)
- Permalink: https://is.muni.cz/course/fi/autumn2011/MA012