Course detail

# Statistics 2

The course deals with main ideas and methods of point and interval estimates, the most used parametric and nonparametric tests, good fit tests, an analysis of variance, a categorial analysis, linear and nonlinear multiple regression models and time series analysis.

Learning outcomes of the course unit

Students will be made familiar with the methods of mathematical statistics, regression analysis, and time series analysis and will learn how to use the respective methods when solving economics problems. After completion of this course students will be prepared to use these methods in economics courses.

Prerequisites

Fundamentals of a probability theory and a random variable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

BUDÍKOVÁ, M., T. LERCH a Š. MIKOLÁŠ. Základní statistické metody. Brno: Masarykova univerzita v Brně, 2005. ISBN 80-210-3886-1. hospodářství. Praha : Victoria Publishing, 1995. ISBN 80-7187-058-7.
GUJARATI, D. N. a D. C. PORTER. Basic econometrics. 5th ed. Boston: McGraw-Hill Irwin, 2009. ISBN 978-007-3375-779.Svoboda, 1977.
ARLT, J. a M. ARLTOVÁ. Ekonomické časové řady. Praha: Professional Publishing, 2009. ISBN 978-808-6946-85

Planned learning activities and teaching methods

Teaching consists of lectures that have an explanation of basic principles and methodology of the discipline, practical problems and their sample solutions.

Exercise promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved by answering theoretical questions,
- points achieved by computer-aided calculation of projects.
Student obtains the assessment after having a short talk with the tutor where his/her work is evaluated.
A (100-91), B (90-81), C (80-71), D (70-61), E (60-50), F (49-0).

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

Students will obtain basic knowledge and skills of point and interval estimates, the most used parametric and nonparametric tests, good fit tests, an analysis of variance, a categorial analysis, linear and nonlinear multiple regression models and time series analysis.

Topics lectures are as follows:
1. Basic concepts of statistical testing.
2. Parametric statistical tests – t-test.
3. Parametric statistical tests – two sample t-test and F-test.
4. Kolmogorov-Smirnov test, Pearson test and Shapiro-Wilk test.
5. Analysis of variance (ANOVA).
6. Nonparametric statistical tests – Sign test, Wilcoxon rank sum test.
7. Nonparametric statistical tests - Kruskal-Wallis test, Median test, Spearman's correlation coefficient.
8. Categorical analysis – contingency table and Chi square test.
9. Univariate regression model.
10. Multivariate regression models.
11. The release of the classical assumptions – heteroscedasticity, multicollinearity and autocorrelation of random components.
12. Nonlinear regression models.
13. Panel data analysis.

Aims

The objective of this course is to familiar students with ideas and methods of point and interval estimates, the most used parametric and nonparametric tests, good fit tests, an analysis of variance, a categorial analysis, linear and nonlinear multiple regression models and time series analysis.

Specification of controlled education, way of implementation and compensation for absences

Attendance at lectures is not compulsory, but is recommended.

Classification of course in study plans

• Programme MGR-UFRP-KS Master's, 1. year of study, winter semester, 5 credits, compulsory

#### Type of course unit

Guided consultation in combined form of studies

16 hours, optionally

Teacher / Lecturer