| Course Unit Code | 470-6403/01 |
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| Number of ECTS Credits Allocated | 10 ECTS credits |
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| Type of Course Unit * | Choice-compulsory |
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| Level of Course Unit * | Third 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 | Czech |
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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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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.
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| Learning Outcomes of the Course Unit |
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| The objective of the course is to develop sufficient knowledge of statistical tools and procedures in applied engineering fields. |
| Course Contents |
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Lectures:
- Exploratory data analysis, types of variables, summarization of distributions. Probability theory.
- Random variable and probability distribution, expected value operator and moments of probability distribution, joint and conditional distributions.
- Probability models for discrete and continuous random variables.
- Sampling distributions of the mean, distribution of sample proportion.
- Point and interval estimation, hypothesis testing, pure significance tests, p-values Two sample tests, paired difference tests.
- One factor analysis of variance, ANOVA table, multiple comparisons, post hoc analysis.
- Simple linear regression model.
- Multiple regression models. |
| Recommended or Required Reading |
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| Required Reading: |
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Briš R., Probability and Statistics for Engineers, 2011, electronics script, Project CZ.1.07/2.2.00/15.0132. Dostupné z http://homel.vsb.cz/~bri10/Teaching/Prob%20&%20Stat.pdf
Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Second Edition, Springer 2008.
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Briš R., Litschmannová M.,STATISTIKA I. pro kombinované a distanční studium, Elektronické skriptum VŠB TU Ostrava,2004
Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Second Edition, Springer 2008.
Anděl J.: Statistické metody, MATFYZPRESS, vydavatelství MFF UK, Praha 2003, ISBN 80-86732-08-8.
Hebák P.,Hustopecký J., Jarošová E, Pečáková I.: Vícerozměrné statistické metody [1], INFORMATORIUM, Praha 2004, ISBN 80-7333-025-3. |
| Recommended Reading: |
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| James L.Johnson; Probability and Statistics for Computer Science, Wiley 2003, ISBN 0-471-32672-0 |
James L.Johnson; Probability and Statistics for Computer Science, Wiley 2003, ISBN 0-471-32672-0 |
| Planned learning activities and teaching methods |
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| Lectures, Project work |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Examination | Examination | | |