Course detail

Statistics

FP-statPAAcad. year: 2020/2021

Pro používání statistických dat je nezbytné pochopení vyjadřovacích prostředků statistiky. Bez zvládnutí principů zkoumání závislostí a měření ukazatelů nelze správně navrhovat ani používat výsledky statistických šetření. V předmětu studenti získají základní znalosti z náhodných veličin, matematické statistiky, regresní analýzy a časových řad a budou schopni je aplikovat v ekonomických problémech. Po absolvování předmětu budou připraveni pro studium ekonomických předmětů, uvažujících náhodu. Důraz je kladen na pochopení možností těchto metod a na interpretaci výsledků.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Mgr. Veronika Novotná, Ph.D.

Department

Institute of Informatics (ÚI)

Learning outcomes of the course unit

Students will be made familiar with the fundamentals of random variables, mathematical statistics, analysis of index numbers, regression analysis and time series and will learn how to use its methods to solve economic problemes. After completion of this course students will be prepared to study economic topics working with uncertainty.

Prerequisites

Fundamentals of linear algebra and mathematical analysis.

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 on courses

EXAM: The exam has a written form.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in control tests
- points achieved in exam
- oral part.

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

Random events.
Discrete random variables.
Continuous random variables.
Processing data samples.
Tests of statistical hypotheses.
Composite and aggregate index numbers.
Regression analysis.
Time series.

Work placements

Not applicable.

Aims

The objective of the course is to make students familiar with the fundamentals of random variables, mathematical statistics, analysis of index numbers, regression analysis and time series. They will be able to study economic topics working with uncertainty, and to solve problems related to these topics applying the methods of this theory.

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 . 2. vyd. CERM, Brno, 2012. ISBN 978-80-7204-835-9.
SKALSKÁ, H. Aplikovaná statistika. Hradec Králové: Gaudeamus, 2013, 233 s. ISBN 978-80-7435-320-8. (CS)

Recommended reading

HATCHER, L.. Advanced statistics in research: reading, understanding, and writing up data analysis results. Saginaw, MI: ShadowFinch Media, 2013, 632 s. ISBN 978-0-9858670-0-3. (EN)
HEBÁK, P. Statistické myšlení a nástroje analýzy dat. 2. vydání. Praha: Informatorium, 2015, 877 s. ISBN 978-80-7333-118-4. (CS)
LITSCHMANNOVÁ, M. Úvod do statistiky, elektronická skripta a doplňkové interaktivní materiály, 2012. (CS)

Type of course unit

 

Lecture

13 hod., optionally

Teacher / Lecturer

doc. Mgr. Veronika Novotná, Ph.D.

Syllabus

Topics of lectures are the following:
Classical definition of probability.
Conditioned probability.
Formula of total probability.
Random variables.
Random events.
Discrete random variables.
Continuous random variables.
Processing data samples.
Tests of statistical hypotheses.
Composite and aggregate index numbers.
Regression analysis.
Time series.

Guided consultation in combined form of studies

16 hod., optionally

Teacher / Lecturer

doc. Mgr. Veronika Novotná, Ph.D.

Exercise

26 hod., compulsory

Teacher / Lecturer

doc. Mgr. Veronika Novotná, Ph.D.

Syllabus

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