Course: Probability and Statistics

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Course title Probability and Statistics
Course code NTI/PAS
Organizational form of instruction Lecture + Lesson
Level of course Bachelor
Year of study not specified
Semester Winter
Number of ECTS credits 4
Language of instruction Czech, English
Status of course Compulsory
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Lecturer(s)
  • Chudoba Josef, Ing. Ph.D.
Course content
Lectures: Explorative and graphical methods of data analysis. Use of statistical software, Excel, Matlab Basic types of discrete probability distributions, their characteristics. Basic types of continuous probability distributions. Typical applications to selected cases (variation of measurement data, statistical quality control, survival analysis). Estimation of model parameters, maximum likelihood estimates, computation, properties. Basic tests of statistical hypotheses. Regression analysis, the least squares method, general trend functions construction. Analysis of variance, applications. Classification, basic methods, decision tree methods, applications, pattern recognition. Bayes methods for modelling, data analysis and decision. Random processes. Basic types and notions. Brown motion, Poisson process. Markov chains, random walk, models of queues. Monte Carlo methods, use of random generators. Practice: Explorative and graphical methods of data analysis. Use of statistical software, Excel, Matlab Basic types of discrete probability distributions, their characteristics. Basic types of continuous probability distributions. Typical applications to selected cases (variation of measurement data, statistical quality control, survival analysis). Estimation of model parameters, maximum likelihood estimates, computation, properties. Basic tests of statistical hypotheses. Regression analysis, the least squares method, general trend functions construction. Analysis of variance, applications. Classification, basic methods, decision tree methods, applications, pattern recognition. Bayes methods for modelling, data analysis and decision. Random processes. Basic types and notions. Brown motion, Poisson process. Markov chains, random walk, models of queues. Monte Carlo methods, use of random generators.

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing)
  • Class attendance - 42 hours per semester
Learning outcomes
The subject's goal is mastering of the basic methods of mathematical statistics placing emphasis on practical usage. This includes design of experiments and data analysis with statistical software R.
Student gain theoretical knowledge and practical skills in statistical analysis and theory of random processes.
Prerequisites
Unspecified

Assessment methods and criteria
Combined examination

Requirements for getting a credit are activity at the practicals /seminars and successful passing the tests. Examination is of the written and oral forms.
Recommended literature
  • Anděl, J. Statistické metody. Matfyzpress Praha, 1998.
  • Antoch, J.- Vorlíčková D. Vybrané metody statistické analýzy dat. Academia Praha, 1992.
  • D. Huff. How to Lie with Statistics, Penguin Books, 1973.
  • N. N. Taleb. The Black Swan -- The Impact of the Highly Improbable (First ed.), Penguin Ltd. London, April 2007, pp. 400..
  • R. Briš, M. Litschmannová. Statistika I., Technická univerzita Ostrava.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Applied Sciences in Engineering (2019) Category: Special and interdisciplinary fields 3 Recommended year of study:3, Recommended semester: Winter
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Nanomaterials (2013) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Applied Sciences in Engineering (2016) Category: Special and interdisciplinary fields 3 Recommended year of study:3, Recommended semester: Winter
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Automatic Control and Applied Computer Science (2016) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter
Faculty: Faculty of Mechatronics, Informatics and Interdisciplinary Studies Study plan (Version): Mechatronics (2016) Category: Special and interdisciplinary fields 1 Recommended year of study:1, Recommended semester: Winter