| Course Unit Code | 470-4405/02 |
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| Number of ECTS Credits Allocated | 6 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Winter, Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| BRI10 | prof. Ing. Radim Briš, CSc. |
| Summary |
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| This is an introductory course in statistics. The course will emphasize methods of applied statistics and data analysis. Theoretical considerations will be included to the extent that knowledge of theory is necessary for a sound understanding of methods and contributes to the development of data analysis skills and the ability to interpret results of statistical analysis. The objective of the course is to develop sufficient knowledge of statistical tools and procedures, understanding of the underlying theory on which the procedures are based, and facility in the application of statistical tools to enable the student to incorporate sound statistical methodology into other areas of his or her own work. |
| Learning Outcomes of the Course Unit |
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| The course is designed for graduates to gain an initial idea of the basic concepts and tasks that fall within the field of probability and statistics and were able to apply their knowledge in practice. |
| Course Contents |
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1) Introduction to Probability Theory
2) Discrete random variable
3) Selected distributions of discrete random variables
4) Continuous random variable
5) Selected distributions of continuous random variables
6) Limit Theorems
7) Random Vector
8) Introduction to statistics, exploratory analysis
9) The survey, random sampling and basic sample characteristics
10) Introduction to estimation theory
11) Introduction to hypothesis testing (principle)
12) Hypotheses testing - mean, probability, variance (one-sample and two-sample tests)
13) Analysis of variance (verification normality, ANOVA and Kruskal-Wallis test)
14) Non-Parametric Hypothesis Tests |
| Recommended or Required Reading |
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| Required Reading: |
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BERTSEKAS, Dimitri P. a TSISIKLIS, John N. Introduction to probability. Second edition. Nashua, NH: Athena Scientific, [2008]. ISBN 978-1886529236.
JAMES, Gareth; WITTEN, Daniela; HASTIE, Trevor a TIBSHIRANI, Robert. An introduction to statistical learning: with applications in R. Second edition. Springer texts in statistics. New York: Springer, [2021]. ISBN 978-1071614174.
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LITSCHMANNOVÁ, Martina. Vybrané kapitoly z pravděpodobnosti. Online. VŠB-TUO, 2011. Dostupné z: http://mi21.vsb.cz/modul/vybrane-kapitoly-z-pravdepodobnosti.
LITSCHMANNOVÁ, Martina. Úvod do statistiky. Online. VŠB-TUO, 2011. Dostupné z: https://mi21.vsb.cz/modul/uvod-do-statistiky.
ANDĚL, Jiří. Základy matematické statistiky. Vyd. 3. Praha: Matfyzpress, 2011. ISBN 978-80-7378-162-0.
JAMES, Gareth; WITTEN, Daniela; HASTIE, Trevor a TIBSHIRANI, Robert. An introduction to statistical learning: with applications in R. Second edition. Springer texts in statistics. New York: Springer, [2021]. ISBN 978-1071614174. |
| Recommended Reading: |
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| WHEELAN, Charles. Naked Statistics: Stripping the Dread from the Data. W. W. Norton & Company, 2014. ISBN 978-0393347777. |
ANDĚL, Jiří. Statistické metody. Páté vydání. Praha: Matfyzpress, 2019. ISBN 978-80-7378-381-5.
FRIEDRICH, Václav. Statistika 1: vysokoškolská učebnice pro distanční studium. Plzeň: Západočeská univerzita, 2002. ISBN 80-7082-913-3.
BRUCE, Peter; BRUCE, Andrew a GEDECK, Peter. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. 2. O'Reilly Media, 2020. ISBN 978-1492072942. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Tasks are not Defined |