Course syllabus STA115C - Statistics (VŠPP - WS 2019/2020)

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Course code: STA115C
Course title in Czech: Statistics
Course title in English: Statistics
Mode of completion and number of credits: Exam (5 credits)
Mode of delivery: full-time, 2/2 (hours of lectures per week / hours of seminars per week)
part-time, 0/8 (lectures per period / seminars per period)
Language of instruction: Czech
Course supervisor: Ing. Tomáš Löster, Ph.D.
Name of lecturer: Ing. Ján Dvorský, PhD. (examiner, instructor, lecturer)
Ing. Tomáš Löster, Ph.D. (supervisor)
Prerequisites: none
This course focuses on statistical applications in Economics. Individual methods are presented based on quantitative data obtained in real socio-economic situations and are supplemented with examples to clarify both technical and managerial aspects. Students will acquire theoretical knowledge necessary for understanding a range of current economic software.
Course contents:
1.Statistics in Socio-Economics. Data gathering and presentation. Descriptive statistics, location parameters: arithmetic mean, median, mode, quantile. Dispersion: variance, standard deviation. Shape of the distribution. (allowance 0/0)
2.Regression and correlation. Correlation and dependence. Regression models for a simple dependence. Choice and calcualtion of a regression function. Linear regression function and its parameters interpretation. Assessing the quality of regression functions. (allowance 0/0)
3.Correlation analysis. Estimating the extent/degree of dependence. Measures of dependence based on variance function regression. Rank correlation. (allowance 0/0)
4.nalysis of time series. Continuous and discrete time series. Principles of time series decomposition: additive and multiplicative models. (allowance 0/0)
5.Methods for trend analysis. Describing a trend using absolute increments and growth coefficients. Using trend functions, using moving averages. (allowance 0/0)
6.Methods for analysis of seasonal time series. Using seasonal variance and seasonal indices. Time series analysis and prognostics. (allowance 0/0)
7.Statistical index numbers to quantify trends. Types of indices: extensive and intensive variables, simple, composite, cumulative; chain and base indices. (allowance 0/0)
8.Composite indices. Variable composition analysis. (allowance 0/0)
9.Cumulative indices of price and volume. Practical cumulative price indices: Laspeyres and Paasche price indices. Consumer price indices. Cost of living indices. Rate of inflation. (allowance 0/0)
10.Using cumulative indices in financial analysis. Statistical deflation for the estimation of real financial development indices. (allowance 0/0)
11.Sampling methods. Probability sampling. Statistical induction. Representative sample size. (allowance 0/0)
12.Theories of sampling parameters estimation. Point and interval estimation of mean, relative frequency and variation. Confidence level. Calculating a sample size. (allowance 0/0)
13.Testing statistical hypotheses. Test process. Significance level. Selected parametric tests. (allowance 0/0)
Learning outcomes:
Expert knowledge; you should be able to:

- select appropriate statistical methods to be used in economic analyses.

- determine data requirements for statistical calculations.

- interpret statistical results.

- expert skills; you should be able to: :

- use statistical algorithms and formulas.

- utilise computer-assisted data processing.

- present statistical results appropriately.

General competences; you should be able to:

- contribute to statistical analysis projects.

- use efficiently literature statistical data.
Input knowledge:
Knowledge of basic college Mathematics is assumed.
Learning activities and teaching methods:
The purpose of the course is to learn how to use individual statistical methods in everyday situations with the ultimate goal for the student to identify potential areas of use of statistics and to obtain useful results therewith. Tutorials will explore practical ways of data gathering, using statistical software, and presenting and interpreting the results and students will have the opportunity to check their own understanding and skills.
To successfully complete the course, students must obtain course credit and pass an exam (oral and written).
Rámcové podmínky zápočtu:
Full-time students: To acquire course credit, a minimum attendance of 50% and the successful completion of two coursework tests are required. The tests will involve both calculation algorithm and results interpretation. Part-time students: To obtain course credit, students must complete the assignments, thus demonstrating their ability to use statistics in practical applications and to evaluate and comment on results. Model problems for the student's reference are to be found in the recommended materials. Assignments will be assessed by way of a discussion.
Rámcové podmínky zkoušky:
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Study load:
Type of teaching methodDaily attendanceCombined form
Lecture/tutorial attendance30 h10 h
Seminar attendance40 h0 h
Preparation for lectures/tutorials0 h20 h
Preparation for seminars10 h0 h
Homework (self-study batches)0 h50 h
Preparation for a mid-term test10 h0 h
Preparation for the end-of-term credit test10 h0 h
Preparation for an exam30 h50 h
Total130 h130 h
Recommended reading:
SOUČEK, E. -- PECÁKOVÁ, I. Statistické praktikum. 1st ed. Praha: VŠMIE, 2010. 130 p. ISBN 978-80-86847-42-9.
SOUČEK, E. Základy statistiky. 1st ed. Praha: VŠMIE, 2006. 196 p. ISBN 80-86847-12-8.

MAREK, L. et al. Statistika v příkladech. Praha: Professional Publishing, 2013. 404 p. ISBN 978-80-7431-118-5.
HINDLS, R. -- HRONOVÁ, S. -- SEGER, J. -- FISCHER, J. Statistika pro ekonomy. 8th ed. Praha: Professional Publishing, 2007. 416 p. ISBN 978-80-86946-43-6.

Last modification made by Mgr. Nikola Najzarová on 08/20/2019.

Type of output: