M012 Statistics II
Faculty of InformaticsAutumn 2001
- 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)
doc. RNDr. Jaroslav Michálek, CSc. (lecturer)
Mgr. Martin Ander, Ph.D. (seminar tutor) - Guaranteed by
- doc. RNDr. Jiří Kaďourek, CSc.
Departments – Faculty of Science
Contact Person: doc. RNDr. Jaroslav Michálek, CSc. - Timetable
- Mon 9:00–10:50 B204
- Timetable of Seminar Groups:
M012/02: Mon 13:00–14:50 B410, M. Budíková - Prerequisites (in Czech)
- M011 Statistics I &&( M001 Calculus II || M501 Calculus II )
- 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
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Information Technology (programme FI, B-IN)
- 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
- 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 (probably available only in Czech)
- The course is taught annually.
M012 Statistics II
Faculty of InformaticsAutumn 2000
- 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)
doc. RNDr. Jaroslav Michálek, CSc. (lecturer) - Guaranteed by
- doc. RNDr. Jiří Kaďourek, CSc.
Departments – Faculty of Science
Contact Person: doc. RNDr. Jaroslav Michálek, CSc. - Prerequisites (in Czech)
- M011 Statistics I && M001 Calculus II
- 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
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Information Technology (programme FI, B-IN)
- Syllabus
- Convergence in probability and in distribution. Weak law of large numbers and central limit theorem. Random simulation.
- Multidimensional normal distribution, exact distributions.
- Basic ideas of inferential statistics. Samples and sample characteristics.
- Properties of the normal and asymptotically normal samples.
- Properties of the point estimators.
- Interval estimators and predictors. Simultaneous decisions.
- Statistical hypotheses testing. The power function.
- Multidimensional linear regression.
- Statistical computation pacquets.
- Literature
- 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 (probably available only in Czech)
- The course is taught annually.
The course is taught: every week.
M012 Statistics II
Faculty of InformaticsAutumn 1999
- 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)
- doc. RNDr. Jaroslav Michálek, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Jan Slovák, DrSc.
Departments – Faculty of Science
Contact Person: doc. RNDr. Pavel Osecký, CSc. - Prerequisites (in Czech)
- M011 Statistics I && M001 Calculus II
- 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
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Information Technology (programme FI, B-IN)
- Syllabus
- Convergence in probability and in distribution. Weak law of large numbers and central limit theorem. Random simulation.
- Multidimensional normal distribution, exact distributions.
- Basic ideas of inferential statistics. Samples and sample characteristics.
- Properties of the normal and asymptotically normal samples.
- Properties of the point estimators.
- Interval estimators and predictors. Simultaneous decisions.
- Statistical hypotheses testing. The power function.
- Multidimensional linear regression.
- Statistical computation pacquets.
- Literature
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
The course is taught: every week.
M012 Statistics II
Faculty of InformaticsAutumn 1998
- Extent and Intensity
- 2/2. 4 credit(s). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
- Teacher(s)
- doc. RNDr. Pavel Osecký, CSc. (lecturer)
- Guaranteed by
- Contact Person: doc. RNDr. Pavel Osecký, CSc.
- Prerequisites (in Czech)
- M011 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
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Information Technology (programme FI, B-IN)
- Syllabus
- Convergence in probability and in distribution. Weak law of large numbers and central limit theorem. Random simulation.
- Multidimensional normal distribution, exact distributions.
- Basic ideas of inferential statistics. Samples and sample characteristics.
- Properties of the normal and asymptotically normal samples.
- Properties of the point estimators.
- Interval estimators and predictors. Simultaneous decisions.
- Statistical hypotheses testing. The power function.
- Multidimensional linear regression.
- Statistical computation pacquets.
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
The course is taught: every week.
M012 Statistics II
Faculty of InformaticsAutumn 1997
- Extent and Intensity
- 2/2. 4 credit(s). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
- Teacher(s)
- doc. RNDr. Pavel Osecký, CSc. (lecturer)
- Guaranteed by
- Contact Person: doc. RNDr. Pavel Osecký, CSc.
- Prerequisites (in Czech)
- M011 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
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Information Technology (programme FI, B-IN)
- Syllabus
- Convergence in probability and in distribution. Weak law of large numbers and central limit theorem. Random simulation.
- Multidimensional normal distribution, exact distributions.
- Basic ideas of inferential statistics. Samples and sample characteristics.
- Properties of the normal and asymptotically normal samples.
- Properties of the point estimators.
- Interval estimators and predictors. Simultaneous decisions.
- Statistical hypotheses testing. The power function.
- Multidimensional linear regression.
- Statistical computation pacquets.
- Language of instruction
- Czech
M012 Statistics II
Faculty of InformaticsAutumn 1996
- Extent and Intensity
- 2/2. 4 credit(s). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
- Teacher(s)
- doc. RNDr. Pavel Osecký, CSc. (lecturer)
- Guaranteed by
- Contact Person: doc. RNDr. Pavel Osecký, CSc.
- 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
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Information Technology (programme FI, B-IN)
- Syllabus
- Convergence in probability and in distribution. Weak law of large numbers and central limit theorem. Random simulation.
- Multidimensional normal distribution, exact distributions.
- Basic ideas of inferential statistics. Samples and sample characteristics.
- Properties of the normal and asymptotically normal samples.
- Properties of the point estimators.
- Interval estimators and predictors. Simultaneous decisions.
- Statistical hypotheses testing. The power function.
- Multidimensional linear regression.
- Statistical computation pacquets.
- Language of instruction
- Czech
- Enrolment Statistics (recent)