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Main menu for Browse IS/STAG
Course info
UMKM / CSME
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Course description
Department/Unit / Abbreviation
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UMKM
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CSME
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Statistic Methods in Economics
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Form of course completion
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Examination
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Form of course completion
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Examination
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Accredited / Credits
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Yes,
5
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Konzultace
14
[Hours/Semester]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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95 / -
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0 / -
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4 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
14
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Evaluation scale |
A|B|C|D|E|F |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
Yes
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Fundamental theoretical course |
No
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Evaluation scale |
A|B|C|D|E|F |
Evaluation scale for credit before examination |
S|N |
Substituted course
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UMKM/KSME
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The aims of the subject is to acquaint student with other advanced methods of mathematical statistics, above all multidimensional statistics and with econometrics principles.
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Requirements on student
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Assignment-completion of all given tasks.
Examination is written (eventually oral), comprises of two parts, theoretical and practical.
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Content
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Hyphotheses testing
Analysis of variance.
Nonparametric ANOVA.
Analysis of dependence of random variables
Classical linear regression model.
Violation of the basic conditions of the linear model (heteroscedasticity, autocorrelation, multicolinearity).
Selected non-linear models not transferable to linear form.
Logictic regression and its aplications.
Discriminate analysis.
Cluster analysis.
Principal component analysis and Factor analysis
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
Cipra, Tomáš. Finanční ekonometrie. Praha: Ekopress, 2013. ISBN 978-80-86929-93-4.
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Basic:
HEBÁK, P. a kol. Vícerozměrné statistické metody (1). Praha: Informatorium, (2004), ISBN 80-7333-025-3..
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Basic:
HEBÁK, P. a kol. Vícerozměrné statistické metody (2). Praha: Informatorium, (2007), ISBN 978-80-73333-001-9..
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Basic:
HEBÁK, P. a kol. Vícerozměrné statistické metody (3). Praha: Informatorium, (2007), ISBN 978-80-73333-001-9..
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Further literature:
HATRÁK, M. Ekonometria.. Bratislava: Iura Edition, 2007.
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Further literature:
Hatrák M. Ekonometrické metódy I. EU Bratislava 1993, 1993.
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Further literature:
Anděl,J. Matematická statistika. SNTL Praha, 1978.
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Further literature:
Víšek,J.,A. Statistická analýza dat. ČVUT 1998, 1998.
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Recommended:
McCLAVE, J., BENSON, P., SINCICH, T. Statistics for Business and Economics. New York Prentice Hall, 2001.
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Recommended:
Newbold, P. Statistics for Business and Economics. London, Prentice-Hall Int. Lim. 1991, 1991. ISBN 0138506450.
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Recommended:
MENDEHALL, W., SINCICH, T. Statistics for Engineering and Sciences. New York, Maxmillan Publishing Company, 1992.
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Time requirements
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Part-time form of study
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Activities
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Time requirements for activity [h]
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Kontaktní výuka
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14
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Domácí příprava na výuku
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66
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Příprava na zápočet
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30
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Příprava na zkoušku
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40
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Total
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150
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Prerequisites - other information about course preconditions |
Prerequisite for successful mastering of this subject is knowledge of mathematics, probability theory and statistics within the range taught at universities. |
Competences acquired |
Student who has successfully completed the course can:
describe real processes using random variables,
explain the nature of advanced statistical methods.
Student who has successfully completed the course is skilled:
to decide correctly about the solution method, to evaluate correctly the conclusions of statistical analyzes, to apply statistical methods, to solve specific tasks,
to evaluate data and to interpret conclusions
to solve problems in economic and other sciences, including methodology of data processing and evaluation.
The student who has successfully completed the course is able to:
consider in intention of mathematical statistics,
independent solution of data-based problems, using appropriate statistical software to model and evaluate processes related to economic and social phenomena
communicate intelligently the results of statistical analysis. |
Teaching methods |
- Monologic (reading, lecture, briefing)
- Dialogic (discussion, interview, brainstorming)
- Work with text (with textbook, with book)
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Assessment methods |
- Oral examination
- Written examination
- Student performance assessment
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