Academic year 2023/2024 |
Supervisor: | doc. Mgr. Zuzana Hübnerová, Ph.D. | |||
Supervising institute: | ÚM | |||
Teaching language: | Czech | |||
Aims of the course unit: | ||||
The course objective is to make students majoring in Mathematical Engineering acquainted with methods of estimation theory, an asymptotic approach to statistical hypotheses testing, and prepare students for independent applications of these methods for statistical analysis of real data. |
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Learning outcomes and competences: | ||||
Students acquire needed knowledge from important parts of mathematical statistics, which will enable them to evaluate and develop stochastic models of technical phenomena and processes based on these methods and use them on PC. |
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Prerequisites: | ||||
Rudiments of probability theory and mathematical statistics, linear models. | ||||
Course contents: | ||||
This course is concerned with the following topics: theory of estimation, maximum likelihood, method of moments, Bayesian methods of estimation, testing statistical hypotheses, nonparametric methods, exponential family of distribution, asymptotic tests, generalized linear models. |
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Teaching methods and criteria: | ||||
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures. | ||||
Assesment methods and criteria linked to learning outcomes: | ||||
Course-unit credit requirements: active participation in seminars, mastering the subject matter, passing both written exams, and semester assignment acceptance. Design and defense of the project. Writing of the classification papers (4-5 examples from the discussed topics). Evaluation by points obtained from the project (max: 20 points) and from the classification letter (maximum 80 points): excellent (90 - 100 points), very good (80 - 89 points), good (70 - 79 points), satisfactory (60 - 69 points), sufficient (50 - 59 points), failed (0 - 49 points). |
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Controlled participation in lessons: | ||||
Participation in the exercise is mandatory and the teacher decides on the compensation for absences. | ||||
Type of course unit: | ||||
Lecture | 13 × 2 hrs. | optionally | ||
Computer-assisted exercise | 13 × 1 hrs. | compulsory | ||
Course curriculum: | ||||
Lecture | Unbiased and consistent estimates |
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Computer-assisted exercise | Unbiased and consistent estimates - examples of estimates and verification of their properties |
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Literature - fundamental: | ||||
1. Anděl, J. Základy matematické statistiky. Matfyzpress. Praha 2005 | ||||
2. Hogg, V.R., McKean J.W. and Craig A.T. Introduction to Mathematical Statistics. Seventh Edition, 2013. New York : Pearson. ISBN: 978-0-321-79543-4 | ||||
4. Lehmann, E.L., Casella G.: Theory of Point Estimation. New York: Springer. 1998 | ||||
5. Dobson, A. J. An introduction to generalized linear models. Chapman & Hall/CRC Boca Raton. 2002. | ||||
Literature - recommended: | ||||
2. Militký, J.: Statistické techniky v řízení jakosti. Pardubice : TriloByte, 1996. | ||||
5. Montgomery, D.D, Runger, G.: Applied Statistics and Probability for Engineers, New York : John Wiley & Sons. 2002 |
The study programmes with the given course: | |||||||||
Programme | Study form | Branch | Spec. | Final classification | Course-unit credits | Obligation | Level | Year | Semester |
N-MAI-P | full-time study | --- no specialisation | -- | GCr | 4 | Compulsory | 2 | 1 | W |
Faculty of Mechanical Engineering
Brno University of Technology
Technická 2896/2
616 69 Brno
Czech Republic
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