The course "Statistics and Data Analysis" consists of a general introduction (descriptive characteristics, probability distributions, principles of basic problems: estimation, construction of interval estimators, decision problems - hypothesis testing), followed by a special part aimed at deepening the knowledge of basic methods of mathematical statistics and data analysis and introducing more advanced methods, with a strong emphasis on multivariate methods. Specifically, the study is divided into the following study blocks: " Basic principles of statistical methods - random variables, construction of point (maximum likelihood method) and interval estimators, basics of hypothesis testing (first and second order errors, power of the test), normality testing (Shapiro-Wilk test). Alternative procedures to statistical procedures based on the assumption of normality: non-parametric and robust procedures, L and M-estimates, ordinal tests. " Correlation analysis and linear regression - Pearson's and Spearman's correlation coefficient, Z-transformation, tests on correlation coefficient values, least squares method, tests and estimates in regression, basics of regression diagnostics. " Multivariate statistical analysis: concept of confidence region, basic estimates and tests, Hotelling test, principal components method, factor analysis. " Selected papers on statistical quality control and reliability, or other necessary topics according to the focus of the doctoral students' professional work.
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