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1. |
Name of the discipline |
MATH STATISTICS |
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2. |
Study course, specialty |
4, Computer mathematics and systems analysis |
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3. |
Semester of study |
7 |
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4. |
Amount of credits |
4 |
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5. |
Lecturer’s S.N.P. |
Yablonsky Oleg Leonidovich |
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6. |
Aims of the study |
To create a knowledge base and skills for students in the field of mathematical statistics acquaint students with the basic principles and methods of mathematical statistics and examples of their applications further formation of students’ skills in abstract mathematical thinking and the ability to apply it in specific tasks, enhancing their mathematical culture |
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7. |
Prerequisites |
Algebra and number theory, Discrete Math, Analytic geometry, Mathematical analysis, Differential equations, The theory of functions of a complex variable, Functional analysis, Probability Theory |
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8. |
Contents of the discipline |
Section 1. Fundamentals of the theory of random processes (in the overview order). Topic 1.1. Definition of a random process. Processes with discrete and continuous time. The trajectories of a random process. Topic 1.2. Random processes with independent increments. Examples: a Poisson random process and a random process of Brownian motion. Section 2. Elements of mathematical statistics. Topic 2.1. The subject and tasks of mathematical statistics. Topic 2.2. Basic concepts of sampling theory: sampling, variational series, histogram, frequency range, empirical distribution function, Glivenko’s theorem. Distributions of ordinal statistics. Selective moments. Asymptotic normality of sample moments. Topic 2.3. Estimation of unknown parameters. Consistency (strong consistency) of evaluations. Displaced and unbiased estimates, optimal estimates. Inequality Rao-Kramer. Efficiency. Methods of maximum likelihood and moments. Sufficient statistics. Trust assessment. Topic 2.4. Testing of statistical hypotheses. Criteria chi-square Pearson for testing hypotheses about the form of distribution, homogeneity and independence. Evenly the most powerful criteria. Topic 2.5. Parametric hypotheses. The Neumann-Pearson lemma. Examples. Topic 2.6. Linear Regression and Least Squares. |
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9. |
Recommended literature |
Main literature: 1. Borovkov AA Probability Theory. M .: Nauka, 1986. 2. Wentzel A.D. Course of the theory of random processes. – Moscow: Nauka, 1978. 3. Gikhman II, Skorokhod AV, Yadrenko MI Theory of Probability and Mathematical Statistics. Kiev: Vishcha Shk., 1979. 4. Gnedenko B. V. Course of the theory of probability. Moscow: Nauka, 1988. 5. Zueў M. M., Syachko Str. Ul. Theory is the iMateNastse i i matematichnaya statyshka. Mazyr: Belaetser, 2000. 6. Ivchenko GI, Medvedev Yu. I. Mathematical statistics. М: Высш. shk., 1984. 7. Lazakovich NV, Stashulyonok SP, Yablonsky O.L. Theory of Probability: A Textbook. – 3rd ed., With modified. – Minsk: BSU, 2013. 8. Rozanov Yu. A. The theory of probability, random processes, mathematical statistics. Moscow: Nauka, 1985. 9. Sevastyanov BA Course of the theory of probability and mathematical statistics. Moscow: Nauka, 1982. 10. Chistyakov VP Course of the theory of probability. M .: Nauka, 1987. 11. Shiryaev AN Probability. M .: Nauka, 1989. Additional literature: 12. Bolshev LN, Smirnov NV Tables of mathematical statistics. Moscow: Nauka, 1983. 13. AN Kolmogorov, Basic concepts of the theory of probability. Moscow: Nauka, 1974. 14. Kramer G. Mathematical methods of statistics. Moscow: The World, 1976. 15. VM Kruglov. Additional chapters of the theory of probability. М: Высш. shk., 1984. 16. Lazakovich NV, Stashulyonok SP, Yablonsky O.L. Course of probability theory: an electronic textbook. – Minsk: E-book of the Belarusian State University, 2003. 17. Leman E. Testing of statistical hypotheses. Moscow: Nauka, 1964. 18. Partasarati K. Introduction to probability theory and measure theory. Moscow: Mir, 1983. 19. Feller V. Introduction to the theory of probability and its applications. М .: Мир, 1984.Т.1,2. 20. PA Henneken and A. Tortra, Theory of Probability and some of its applications. Moscow: Nauka, 1974. Collected problems on the discipline “Theory of Probability and Mathematical Statistics”: 21. Zhdanovich VF, Lazakovich NV Radyno N.Ya. Tasks for laboratory work on the course of probability theory and mathematical statistics in two parts. Part 1. Minsk, 1998. 22. Zhdanovich VF, Lazakovich N.V. Radyno N.Ya., Stushulenok S.P. Tasks for laboratory work on the course of probability theory and mathematical statistics in two parts. Part 2. Minsk, 1999. 23. Meshalkin L.D. A collection of problems in probability theory. M: MSU, 1963. 24. Prokhorov AV, Ushakov VG, Ushakov NG Problems on the theory of probability: Basic concepts. Limit theorems. Random processes. M: Science, 1986. 25. Sevastyanov BA, Chistyakov VP, Zubkov AM Collection of problems in probability theory. M: Science, 1989. 26. Theory of Probability: a Workshop: Textbook. allowance for university students. specialist. : at 2 pm Part 1 / [aut .: NV Lazakovich, EM Radyno, SP Stashulyonok, SL Shtin, O.L. Yablonsky]; Ed. NV Lazakovich. – Minsk: BSU, 2011. – 147 p. 27. Theory of Probability: a Workshop: Textbook. allowance for university students. specialist. : at 2 pm Part 2 / [aut .: N. V. Lazakovich, E. M. Radyno, S. P. Stasulenok, A. G. Yablonskaya, O.L. Yablonsky]; Ed. NV Lazakovich. – Minsk: BSU, 2014.- 175s. References: 28. Prokhorov Yu.V., Rozanov Yu.A. Probability theory. М .: Science, 1973 29. A handbook on probability theory and mathematical statistics. Korolyuk VS, Portenko NI, Skorokhod AV, Turbin AF-M: Science, 1985. |
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10. |
Methods of teaching |
interactive methods of teaching (working in small groups (team), problem training) are organized taking into account the inclusion in the learning process of all students of the group. Joint activity means that each student makes his own individual contribution, in the course of the work there is an exchange of knowledge, ideas, methods of activity. Organized individual, steam and group work. Interactive methods are based on the principles of interaction, activity of trainees, reliance on group experience, mandatory feedback |
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11. |
Language of teaching |
Russian |
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12. |
Knowledge control and requirements |
– test; – colloquium The score on the exam is set taking into account: The score on the exam is set taking into account: 30% – work in laboratory and practical classes, 70% – oral examination. |
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13. |
Certification form |
examination |