Course detail

Statistics 2

FP-Msta2KAcad. year: 2019/2020

Students will acquire basic knowledge of the mathematical statistics, the categorical and correlation analysis, the analysis of variance, the regression analysis and time series analysis.

Language of instruction

English

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

Ing. Karel Doubravský, Ph.D.

Department

Institute of Informatics (ÚI)

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 completing the course, students will be prepared to practically apply these methods in a Master's degree courses and in real environments.

Prerequisites

Basic knowledge and skills of a probability, random variables, important distributions of random variable, a descriptive statistics.

Co-requisites

Not applicable.

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.

Assesment methods and criteria linked to learning outcomes

he course-unit credit is awarded on the following conditions:
- passing control tests,
- submitting answers to calculating problems and theoretical questions.

The exam has a written form.
In the first part of the exam student solves 4 examples within 80 minutes. 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 to calculating questions and theoretical questions,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

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

Descriptive statistics (week 1)
Sample characteristics
Empirical distribution function
Analysis of big data samples
Mathematical statistics (week 2 - 4)
Point and interval estimates
Testing of statistical hypothesis
Analysis of bivariate data sample (week 5 - 7)
Correlation analysis
Categorial analysis
Analysis of variance
Regression analysis (week 8 - 10)
Linear models
Nonlinear models
Time series analysis (week 11 - 13)
Time series characteristics
Decomposition of time series

Work placements

Not applicable.

Aims

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

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

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

MATHEWS, P. Design of Experiments with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 978-08-738-9637-5.
KARPÍŠEK. Z. a M. DRDLA. Applied statistics. 1.vyd. Brno: PC-DIR Real, 1999. ISBN 8021414936.

Recommended reading

HINDLS, R. aj. Analýza dat v manažerském rozhodování. Praha : Grada Publishing, 1999. ISBN 80-7169-255-7.
SWOBODA, H. Moderní statistika. Praha : Svoboda, 1977.
BUDÍKOVÁ, M., T. LERCH a Š. MIKOLÁŠ. Základní statistické metody. 1. vyd. Brno: Masarykova univerzita v Brně, 2005. ISBN 80-210-3886-1.
KROPÁČ, J. STATISTIKA. 2. vyd. Brno: Akademické nakladatelství CERM, 2012. 138 s. ISBN 978-80-7204-788-8.
KARPÍŠEK, Z. Matematika IV. 2. vyd. Brno: Akademické nakladatelství CERM, 2003. ISBN 80-214-2522-9.
WONNACOTT, T.H. a R.J. WONNACOTT. Introductory Statistics. 5.ed. New York:Wiley, 1990. 736 s. ISBN 978-0471615187.
KROPÁČ, J. STATISTIKA B. 3. vyd. Brno: Akademické nakladatelství CERM, 2012. 152 s. ISBN 978-80-7204-822-9.

eLearning

eLearning: currently opened course

Classification of course in study plans