| Course Unit Code | 470-8542/01 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Winter 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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| LIT40 | Ing. Martina Litschmannová, Ph.D. |
| Summary |
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Computer-based data processing requires their users to be able to analyze complex problems. This subject is a combination of lectures and computer-based practical, whereby theory is firmly placed into practice. In contrast to classical mathematical statistics, emphasis is placed not on particular methods but on their appropriate combinations, enabling the assessment of data quality, the selection of a suitable statistical model, its verification and the interpretation of the results with respect to the goal of data analysis. The learning is centered around focusing more on conceptual understanding of key concepts, and statistical thinking, and less on formulas and calculations, which can now be left to PCs. Statistical skills enable students to intelligently collect, analyze and interpret data relevant to their decision-making.
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| Learning Outcomes of the Course Unit |
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This subject could be considered a multidisciplinary subject in between statistics and informatics. Its aim is to expand basic knowledge of statistical methods acquired by students within the scope of the subject 541-0181 / 01 - Statistics and / or 548-0093 / 01 - Quantitative methods in geography, especially about advanced statistical methods used in technical practice combined with special computer-based procedures.
After passing the subject students should be able to effectively evaluate their own data and choose a suitable method for the creating a data model. They should be able to verify usability of the data model and they should be know how to interpret results in connection with the practical focus of the task. |
| Course Contents |
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Exploratory data analysis, types of variables, Exploratory analysis of single discrete and continuous variables, summarization of distributions.
Statistical inference. 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.
ANOVA for two factors.
Linear regression models, least squares estimation of)parameters and properties of the estimates.
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
Dummer R.M.; Introduction to Statistical Science, script of VŠB-TUO FEI, 1998, ISBN 80-7078-497-0 |
Briš R., Litschmannová M.,STATISTIKA I. pro kombinované a distanční studium, Elektronické skriptum VŠB TU Ostrava,2004.
Briš, R., Litschmannová M.: STATISTIKA II., 2007 Ostrava:VŠB – Technická univerzita Ostrava, 2007, ISBN 978-80-248-1482-7, elektronické skriptum.
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| Recommended 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
James.L.Johnson; Probability and Statistics for Computer Science, Wiley 2003, ISBN 0-471-32672-0 |
Likeš J., Cyhelský L.Hindls R.; Úvod do statistiky a pravděpodobnosti, 1994, VŠE Praha, ISBN 80-7079-028-8.
Likeš J., Machek J., Počet pravděpodobnosti, SNTL Praha 1981.
Likeš J., Machek J., Matematická statistika, SNTL Praha 1983.
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| 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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| Exercises evaluation and Examination | Credit and Examination | 100 (100) | 51 |
| Exercises evaluation | Credit | 33 | 15 |
| Examination | Examination | 67 | 18 |