ESF:BPM_STA2 Statistics 2 - Course Information

BPM_STA2 Statistics 2

Extent and Intensity

2/2/0. 5 credit(s). Type of Completion: zk (examination).

Teacher(s)

doc. Mgr. Maria Králová, Ph.D. (lecturer)
Mgr. Jan Böhm (seminar tutor)
Ing. Matouš Cabalka (seminar tutor)
Mgr. Terézia Černá (seminar tutor)
Mgr. Monika Filová (seminar tutor)
Mgr. Bc. Martin Chvátal, Ph.D. (seminar tutor)
Ing. Lukáš Kokrda (seminar tutor)
doc. Mgr. Maria Králová, Ph.D. (seminar tutor)
Ing. Mgr. Markéta Matulová, Ph.D. (seminar tutor)
Mgr. Petra Ráboňová, Ph.D. (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (seminar tutor)
Lenka Hráčková (assistant)

Guaranteed by

doc. Mgr. Maria Králová, Ph.D.
Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Department of Applied Mathematics and Computer Science – Faculty of Economics and Administration

Timetable

Tue 12:00–13:50 P101

• Timetable of Seminar Groups:

BPM_STA2/T01: Mon 17. 2. to Sun 24. 5. Mon 10:00–11:50 Knihovna ESF, box 2, V. Reichel, Nepřihlašuje se. Určeno pro studenty se zdravotním postižením.
BPM_STA2/01: Tue 16:00–17:50 VT202, T. Černá
BPM_STA2/02: Wed 8:00–9:50 VT204, J. Böhm
BPM_STA2/03: Thu 10:00–11:50 VT206, L. Kokrda
BPM_STA2/04: Thu 12:00–13:50 VT206, M. Matulová
BPM_STA2/05: Thu 8:00–9:50 VT105, P. Ráboňová
BPM_STA2/06: Tue 14:00–15:50 VT204, M. Filová, M. Králová
BPM_STA2/07: Thu 8:00–9:50 VT206
BPM_STA2/08: Tue 18:00–19:50 VT206, T. Černá
BPM_STA2/09: Thu 12:00–13:50 VT204, P. Ráboňová
BPM_STA2/10: Thu 14:00–15:50 VT204, P. Ráboňová
BPM_STA2/11: Wed 10:00–11:50 VT202, J. Böhm
BPM_STA2/13: Tue 16:00–17:50 VT204, M. Filová
BPM_STA2/14: Thu 10:00–11:50 VT105, P. Ráboňová
BPM_STA2/16: Thu 16:00–17:50 VT206, M. Cabalka

Prerequisites

( BPM_STA1 Statistics 1 )
The basic terms in calculus of probability.

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 22 fields of study the course is directly associated with, display

Course objectives

At the end of the course students should be able to:
- understand and explain the basics of statistical inference;
- use the basic testing procedures;
- operate the statistical software.

Learning outcomes

After graduation of the course student should be able to:
- distinguish between sample and population and properly interpret principles of inferential statistics
- determine statistical methods appropriate for particular application context
- solve tasks based on real data by means of sw. STATSTICA
- interpret properly outputs of analyses

Syllabus

• - Normal as well as derived exact distributions (Pearson distribution, Student distribution, F distribution) and their properties; quantile tables.
• - Law of large numbers, central limit theorem.
• - Basic concepts of mathematical statistics; inductive statistics, random sampling, sample statistic.
• - Point estimation and interval estimation of population parameters and parametric functions.
• - Introduction to hypotheses testing.
• - The statistical inferences based on a single sample from normal distribution.
• - The statistical inferences based on two independent samples from the normal distribution.
• - The statistical inferences based on one sample or two independent samples from Bernoulli (zero-one) distribution.
• - One-way analysis of variance.
• - Simple linear regression.
• - Introduction to correlation analysis.
• - The relationship between two variables on the nominal or ordinal scale
• - Nonparametric tests on medians

Literature

required 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

recommended literature
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika : sbírka příkladů. 2. vyd. Brno: Masarykova univerzita v Brně, 1998, viii, 116. ISBN 8021018321. info
• NOVÁK, Ilja, Richard HINDLS and Stanislava HRONOVÁ. Metody statistické analýzy pro ekonomy. 2. přepracované vyd. Praha: Management Press, 2000, 259 s. ISBN 80-7261-013-9. info
• OSECKÝ, Pavel. Statistické vzorce a věty (Statistical formulas). Druhé rozšířené. Brno (Czech Republic): Masarykova univerzita, Ekonomicko-správní fakulta, 1999, 53 pp. ISBN 80-210-2057-1. info
• BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika (Descriptive Statistics). 3., doplněné vyd. Brno: Masarykova univerzita, 1998, 52 pp. ISBN 80-210-1831-3. info

Teaching methods

Theoretical lectures; computer seminar sessions.

Assessment methods

The final grade is given by the score of the final test.
The requirements for taking the test are:
to be active at seminar sessions which are compulsory and to pass 2 Ropots.
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.

Language of instruction

Czech

Follow-Up Courses

• BPE_CARA Time Series

Further comments (probably available only in Czech)

Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
Information on course enrolment limitations: max. 30 cizích studentů; cvičení pouze pro studenty ESF

Listed among pre-requisites of other courses

• BPM_STAE Statistics for economists
  (!BPM_STA1)||(!BPM_STA2)  

• Enrolment Statistics (Spring 2020, recent)
  
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