Course detail
Statistics 2
FP-STA2DAcad. year: 2020/2021
Students will acquire basic knowledge of mathematical statistics, categorical and correlation analysis, analysis of variance, regression analysis and time series analysis.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Exercise promote the practical knowledge of the subject presented in the lectures.
Assesment methods and criteria linked to learning outcomes
- 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
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
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
KROPÁČ, J. STATISTIKA. 2. vyd. Brno: Akademické nakladatelství CERM, 2012. 138 s. ISBN 978-80-7204-788-8.
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
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Exercise
Teacher / Lecturer
Syllabus
Elearning