Course: Statistical Methods in Economics

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Course title Statistical Methods in Economics
Course code KMI/STATE
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study 1
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction unspecified
Work placements unspecified
Recommended optional programme components None
Lecturer(s)
  • Rost Michael, doc. Ing. Ph.D.
  • Houda Michal, Mgr. Ph.D.
  • Mrkvička Tomáš, doc. RNDr. Ph.D.
Course content
Lectures: 1 - Introduction, sources of economical data, statistical software used for analysis; 2 - Some aspect of inductive statistical method; 3 - Programming environment R, data import to R; 4 - Two-factor diffusion analysis on how to perform such analysis in the R programming environment; 5 - Nonparametric tests; 6 - Some normality test and how to carry them in R; 7 - Introduction into categorical data analysis; 8 - Visualization of categorical data; 9 - Introduction into multivariate data analysis, matrix algebra, multivariate t-test; 10 - Distance, hierarchical cluster analysis; 11 - Issues of regression analysis, methodology of regression analysis; 12 - Some classification methods; 13 - Logistic regression.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with multi-media resources (texts, internet, IT technologies), Blended learning
  • Class attendance - 42 hours per semester
  • Preparation for credit - 24 hours per semester
  • Preparation for exam - 34 hours per semester
  • Preparation for classes - 40 hours per semester
Learning outcomes
The course has two main goals. The first one is to introduce students to some advanced statistical methods like analysis of categorical data, cluster analysis and logistic regression. Introduce terminology linked to these methods and show how to make some conclusion about data. The second goal consists in gaining the basic skill of working with programming environment R. We supposed that students have knowledge of statistic in extent of basic course of statistic.
Students understand the basic principles of advanced statistical methods. Students are able to communicate with programming environment R.
Prerequisites
Prerequisities: Teorie pravděpodobnosti a statistika 2/Theory of Probability and Statistics 2

Assessment methods and criteria
Combined exam, Test

Credits requirements To pass written part of exam you have to solve absolute majority of problems. Examination Requirements: The exam is oral. Students have to show theoretical knowledge of presented themes.
Recommended literature
  • Crawley, Michael J. The R book. 2nd ed. Hoboken : Wiley, 2013. ISBN 978-0-470-97392-9.
  • De Vries, Andrie; Meys, Joris. R for dummies. 2nd edition. Hoboken, NJ : John Wiley & Sons, Inc., 2015. ISBN 978-1-119-05580-8.
  • Hindls, R. a kol. Statistika v ekonomii. Praha: Professional Publishing, 2018. ISBN 978-80-88260-09.
  • Meloun,M. Statistická analýza vícerozměrných dat v příkladech. Academia: Praha, 2017.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Economics Study plan (Version): Management and Business Economics (3) Category: Economy 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Economics Study plan (Version): Accounting and Financial Management (3) Category: Economy 1 Recommended year of study:1, Recommended semester: Winter