Course detail

Statistical Methods and Risk Analysis

FP-smarPAAcad. year: 2020/2021

The course deals with basic ideas and methods of mathematical statistic: methods of analysis of multidimensional data files, methods of regression and correlation analysis, methods of regression analysis for description of a trend in time series, and characteristics of time series describing economic and social events, index analysis.

Language of instruction

Czech

Number of ECTS credits

5

Guarantor

Mgr. Eva Michalíková, Ph.D.

Department

Institute of Informatics (ÚI)

Learning outcomes of the course unit

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

Prerequisites

Fundamentals of probability theory and fundamentals of mathematical statistics

(theory of probability: random events, classical probability, conditional probability;

discret and continuous random variables: distribution function, density, Binomical and Poisson distribution, normal distribution;

estimation of confidence interval , hypothesis testing)

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

COURSE-UNIT CREDIT: The course-unit credit is awarded on the following conditions:
- participation in seminars,
- submitting answers to excercises.

EXAM: The exam has a written form.
In the first part of the exam student solves 4 examples within 80 minutes. (It is allowed to use recomended literature.)
In the second part of the exam student works out answers to 3 theoretical questions within 15 minutes.
The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in control tests,
- points achieved by solving examples,

The grades and corresponding points:
A (100-90), B (89-83), C (82-76), D (75-69), E (68-60), F (59-0).

Course curriculum

week 1: Discrete and continuous random variables, distribution function, density, special types of probability distrubiutions
week 2: Construction of confidence intervals; hypothesis testing
week 3: Methods of measuring the relationship between quantitative variables: covariance coefficient, correlation coefficient
week 4: Methods of measuring the relationship between quantitative variables: test of independence
week 5: Methods of measuring the relationship between qualitative variables: test of independence
week 6: 2x2 tables, NcNemar's test
week 7: Regression and correlation analysis. Regression line.
week 8: Regression analysis: another types of regression functions.
week 9: Times series - introduction. Characteristics of time series. Decomposition of time series.
week 10: Time series - estimation of trend in time series. Seasonal and cyclical component.
week 11: Index analysis - simple and complex individual indexes, price and volume indexes. Decompozition of individual indexes.
week 12: Index analysis - agregate indexes. Decomposition of agregate indexes.
week 13: Index analysis - indexes of work productivity

Work placements

Not applicable.

Aims

The objective of the course is to make students familiar with the fundamental ideas and methods used by these mathematical disciplines which enable students to apply this knowledge when solving problems related to the economic areas where these methods are used.

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

Attendance at lectures is not compulsory but is recommended. Attendance at exercises is required and checked by the tutor. An excused absence of a student from seminars can be compensated for by submitting solution of alternate exercises.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Basic literature

KROPÁČ, J. Statistika B. 3. vyd. CERM, Brno: FP VUT, 2012. ISBN 978-80-7204-822-9.
HINDLS, R. aj. Statistika pro ekonomy. 8. vyd. Praha: Professional Publishing, 2012. ISBN 9788086946436. (CS)
WOOLDRIDGE, J.F. Introductory Econometrics: A Modern Approach. South-Western College Pub, 2015. ISBN: 978-1111531041 (EN)

Recommended reading

WONNACOT, T.H. aj. Statistika pro obchod a hospodářství. 1. vyd. Praha: Victoria Publishing, 1995. ISBN 80-85605-09-0.
KROPÁČ, J. Statistika. 5. vyd. CERM, Brno: 2013. ISBN 978-80-7204-835-9.
WOOLDRIDGE, J.F. Introductory Econometrics: A Modern Approach. South-Western College Pub, 2015. ISBN: 978-1111531041 (EN)

Type of course unit

 

Lecture

13 hours, optionally

Teacher / Lecturer

Mgr. Eva Michalíková, Ph.D.

Syllabus

Topics of lectures are the following:

week 1: Discrete and continuous random variables, distribution function, density, special types of probability distrubiutions
week 2: Construction of confidence intervals; hypothesis testing
week 3: Methods of measuring the relationship between quantitative variables: covariance coefficient, correlation coefficient
week 4: Methods of measuring the relationship between quantitative variables: test of independence
week 5: Methods of measuring the relationship between qualitative variables: test of independence
week 6: 2x2 tables, NcNemar's test
week 7: Regression and correlation analysis. Regression line.
week 8: Regression analysis: another types of regression functions.
week 9: Times series - introduction. Characteristics of time series. Decomposition of time series.
week 10: Time series - estimation of trend in time series. Seasonal and cyclical component.
week 11: Index analysis - simple and complex individual indexes, price and volume indexes. Decompozition of individual indexes.
week 12: Index analysis - agregate indexes. Decomposition of agregate indexes.
week 13: Index analysis - indexes of work productivity


The topics of exercise correspond to the topics delt with the lectures.



Guided consultation in combined form of studies

16 hours, optionally

Teacher / Lecturer

Mgr. Eva Michalíková, Ph.D.

Exercise

26 hours, compulsory

Teacher / Lecturer

Mgr. Eva Michalíková, Ph.D.