Mathematical Statistics
General data
Course ID: | 0600-FS2-2SM |
Erasmus code / ISCED: |
11.105
|
Course title: | Mathematical Statistics |
Name in Polish: | Statystyka matematyczna |
Organizational unit: | (in Polish) Instytut Matematyki. |
Course groups: | |
ECTS credit allocation (and other scores): |
(not available)
|
Language: | Polish |
Type of course: | (in Polish) obowiązkowe specjalnościowe |
Prerequisites: | Probability 0600-FS2-1PRB |
Short description: |
Course objectives: By the end of the course the student should have developed the skills to: verify when statistics are sufficient (minimum syfficient); determining the loss function; apply statistical decision theory in testing hypotheses. Also know: the advantages and disadvantages of different classes of estimators; how to termine the estimators in the class; properties of exponential statistical spaces and statistics in these spaces. |
Full description: |
Course profile: academic Form of study: stationary Course type: obligatory Academic discipline: Mathematics, field of study in the arts and science: mathematics Year: 2, semester: 3 Prerequisities: Stochastic Processes lecture 30 h. exercise class 30 h. Verification methods: lectures, exercises, consultations, studying literature, home works, discussions in groups. ECTS credits: 5 Balance of student workload: attending lectures15x2h = 30h attending exercise classes 15x2h = 30h preparation for classes 7x3h = 21h completing notes after exercises and lectures 7x2h = 14h consultations 12x1h = 12h the final examination: preparation.and take 12h + 3h = 15h control works: repeating the material and preparation 3x4h = 12h Quantitative description Direct interaction with the teacher: 75 h., 3 ECTS Practical exercises: 77 h., 3 ECTS |
Bibliography: |
(in Polish) 1. J.R. Barra Matematyczne podstawy statystyki, PWN, Warszawa 1982. 2. J. Bartoszewicz Wykłady ze statystyki matematycznej, PWN, Warszawa 1989. 3. R. Zieliński Siedem wykładów wprowadzających do statystyki matematycznej, PWN, Warszawa 1990. |
Learning outcomes: |
Learning outcomes: Students know the major theorems of mathematical statistics related to statistical spaces, statistics, exponential statistical spaces, hypothesis testing, theory of estimations, decision theory.K_W03 Students know how to use R/SPSS software for statistical processing of data.K_W12 Students understand fundamentals of mathematical statistics and statistical processing of data bases.K_U12 Students are able to determine the statistics and estimators with selected properties, as well as to test hypotheses.K_U12 Be able to express in writing and oral statistical content.K_U02 Students know foundations of the theory of Fisher information.K_W11 |
Assessment methods and assessment criteria: |
The overall form of credit for the course: final exam |
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