Course detail

Statistics 2

FP-STA2Acad. year: 2023/2024

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

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Entry knowledge

Students will gain knowledge of mathematical statistics, categorical and correlation analysis, analysis of variance, regression analysis and time series analysis and their use in business process management. Emphasis is primarily placed on the practical part, which is aimed at familiarizing with the use of statistical programs in the implementation of the above-mentioned methods and procedures.

Rules for evaluation and completion of the course

The course-unit credit is awarded on the following conditions (max. 40 points):
- elaboration of semestral assignments.

The exam (max. 60 points)
- has a written form.
In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to a theoretical question within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in  semestral assignments,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

The grades and corresponding points:
A (100–90), B (89–80), C (79–70), D (69–60), E (59–50), F (49–0).

 

COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY

The course-unit credit is awarded on the following conditions (max. 40 points):
- elaboration of semestral assignments.

The exam (max. 60 points)
- has a written form.
In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to a theoretical question within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semestral assignments,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

The grades and corresponding points:
A (100–90), B (89–80), C (79–70), D (69–60), E (59–50), F (49–0).

 

Attendance at lectures is not mandatory  but is recommended. Attendance at exercises is required and checked by the tutor.

Aims

Learning outcomes of the course unit is to acquaint students with the principal of mathematical statistics, categorical and correlation analysis, analysis of variances, regression analysis and time series analysis so that they are able to apply this knowledge appropriately in management, informatics and economic problems.
Students will acquire basic knowledge of mathematical statistics, categorical and correlation analysis, analysis of variances, regression analysis and time series analysis.
At the end of the course students will be able to use these methods in master's courses and in the real managerial problems.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

KROPÁČ, J. STATISTIKA B. 3. vyd. Brno: Akademické nakladatelství CERM, 2012. 152 s. ISBN 978-80-7204-822-9. (CS)
Studijní materiály vystavené na e-learningu.

Recommended reading

BUDÍKOVÁ, M., T. LERCH a Š. MIKOLÁŠ. Základní statistické metody. 1. vyd. Brno: Masarykova univerzita v Brně, 2005. ISBN 80-210-3886-1.
JAMES, G., D. WITTEN, T. HASTIE a R. TIBSHIRANI. An Introduction to Statistical Learning: with Applications in R. New York: Springer New York, 2014. 426 s. ISBN 978-1-4614-7137-0.
FIELD, A., J. MILES and Z. FIELD. Discovering Statistics Using R. 1 edition. Los Angeles, Calif.: SAGE Publications Ltd., 2012. ISBN 978-1-4462-0046-9.

eLearning

Classification of course in study plans

  • Programme BAK-MIn Bachelor's, 2. year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Empirical characteristics
2. Empirical distribution function
3. Analysis of large data sets
4. Point and interval estimates
5. Testing statistical hypothesis
6. Correlation analysis
7. Categorical analysis
8. Analysis of variance
9. Linear regression models
10. Nonlinear regression models (linearizable functions)
11. Nonlinear regression models (non-linearizable functions)
12. Time serie analysis
13. Time serie decomposition and identify its trend

Exercise

26 hours, compulsory

Teacher / Lecturer

Syllabus

The topics of exercises correspond to the topics of lectures.

eLearning