|
Lecturer(s)
|
-
Fišerová Eva, doc. RNDr. Ph.D.
|
|
Course content
|
1. Basic relations between probability theory and measure theory 2. Theorems about matrices 3. Random variables 4. Random vectors 5. Densities 6. Normal distribution 7. Regression 8. Correlation 9. Limit theorems 10. Estimation theory - consistent estimates and regular systems of densities 11. Estimation theory - sufficient and anciliary statistics 12. Estimation theory - maximal likelihood method
|
|
Learning activities and teaching methods
|
|
Dialogic Lecture (Discussion, Dialog, Brainstorming), Work with Text (with Book, Textbook)
|
|
Learning outcomes
|
Master procedures of linear statistical methods together with necessary basic facts of probability theory.
Knowledge To know basic procedures of linear statistical methods together with necessary basic facts of probability theory.
|
|
Prerequisites
|
Master's degree in mathematics.
|
|
Assessment methods and criteria
|
Oral exam
Exam: to know and to understand the subject
|
|
Recommended literature
|
-
C. R. Rao. (1978). Lineární metody statistické indukce a jejich aplikace. Praha, Academia.
-
J. Anděl. (1978). Matematická statistika. SNTL/ALPHA, Praha.
-
J. Anděl. (2005). Základy matematické statistiky. Praha.
|