1. komponenta

Lecture type	Total
Lectures	15
Exercises	45
Seminar	15

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES: To develop skills in applications of numerical and statistical methods in mathematical modeling; to understand and to interpret correctly statistical data and analyses and to develop skills in using statistical and numerical software.


COURSE DESCRIPTION AND SYLLABUS:
1. Simulations of discrete and continuous random variables and vectors values
2. Parametric model selection and fitting. Goodness of fit tests. Point and interval estimation of parameters.
3. Testing statistical hypotheses. Checking assumptions for applicability of various tests.
4. Kolmogorov - Smirnov test.
5. 2 - test.
6. Power of 2 - test.
7. Estimation of distribution and parameters using Monte Carlo methods.
8. Comparison of two populations.
9. Comparison of more then two populations. (one way and two ways analysis of variance, testing homogeneity of discrete distributions).
10. Bivariate distributions. Testing independencies (of discrete variables). Correlation. Testing correlations (parametric and nonparametric tests).
11. Regression analysis (estimation and selection of the linear regression model, testing hypotheses about model parameters).
12. Regression analysis: transformation of parameters.
13. Simulations of Markov processes


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Mandatory course - Regular study - Mathematical Statistics