Course detail

Statistics 2

FP-STA2Acad. year: 2022/2023

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.

Guarantor

Ing. Karel Doubravský, Ph.D.

Department

Institute of Informatics (ÚI)

Learning outcomes of the course unit

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.

Prerequisites

Not applicable.

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.
Exercise promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

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 theoretical questions 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 theoretical questions 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).

Course curriculum

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 series analysis
13. Time series decomposition and identify its trend

Work placements

Not applicable.

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.

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

Attendance at lectures is not mandatory  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

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.
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.
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.

Elearning

eLearning: currently opened course

Classification of course in study plans

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

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

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 hod., optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

The topics of exercises correspond to the topics of lectures.

Elearning

eLearning: currently opened course