Course detail
Statistics 2
FP-STA2Acad. year: 2024/2025
Students will acquire basic knowledge of mathematical statistics, categorical and correlation analysis, analysis of variances, regression analysis and time series analysis.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Basic knowledge of probability theory and random variables is required for successful completion.
Rules for evaluation and completion of the course
COURSE COMPLETION
The course-unit credit is awarded on the following conditions (max. 40 points):
- preparation of semester assignments (the topic of the assignments will be specified during the semester).
The exam (max. 60 points)
- has a written form with the possibility of using computer technology and consists of four computational examples and a theoretical question.
The grade, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semester assignments (max. 40 points),
- points achieved by solving examples (max. 51 points),
- points achieved by answering a theoretical question (max. 9 points).
The grade 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. An excused absence of a student from seminars can be compensated for by submitting solution of alternate exercises.
COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY PLAN
The course-unit credit is awarded on the following conditions (max. 40 points):
- preparation of semester assignments (the topic of the assignments will be specified during the semester).
The exam (max. 60 points)
- has a written form with the possibility of using computer technology and consists of four computational examples and a theoretical question.
The grade, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semester assignments (max. 40 points),
- points achieved by solving examples (max. 51 points),
- points achieved by answering a theoretical question (max. 9 points).
The grade and corresponding points:
A (100-90), B (89-80), C (79-70), D (69-60), E (59-50), F (49-0).
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.
To develop students' awareness and ability to use statistical tools as a basis for data analysis in the management of individual business processes.
Study aids
Prerequisites and corequisites
Basic literature
Studijní materiály vystavené na e-learningu.
Recommended reading
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
Classification of course in study plans
- Programme BAK-MIn Bachelor's 2 year of study, summer semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
The course explains the basic ideas and methods of mathematical statistics, correlation analysis, categorical analysis and time series analysis.
Basic contents:
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
Teacher / Lecturer
Syllabus
Elearning