1. Descriptive statistics: Basic concepts. Population and sample. Frequency tabulation. Graphical presentation of data. 2. Descriptive statistics: Measures of location, variability, shape. Identification and rejection of outliers, Box-and-Whisker plot. 3. Probability: Basic concepts of probability theory. Random events and random variables, description of discrete random variables, description of continuous random variables. Discrete probability distributions. Continuous probability distributions. Quantiles. 4. Statistical induction: Relationship between population and sample, sampling methods, sample properties. Estimations of population parameters, point estimations, interval estimations. Robust estimation. 5. Statistical induction: Hypothesis testing. Testing procedure, null and alternative hypothesis, errors of the first and second kind. One?sample and two?sample parametric tests. 6. Statistical induction: One?way ANOVA, ratio of variance. Chi?square test and other variants (Yates' correction, Fisher Exact Probability Test, McNemar's Test). Contingency coefficients. 7. Statistical induction: Non?parametric tests. Tests of outliers (Grubbs Test, Dean and Dixon Q-test). Goodness?of?Fit tests (Kolmogorov-Smirnov Test). Alternatives to one-, two- and multiple sample tests (sign test, Mann-Whitney Test, Wilcoxon Test, Friedman Test, Kruskal-Wallis Test). 8. Correlation analysis: Linear Correlation Coefficient, Spearman Rank-Order Correlation Coefficient. Testing significance of Correlation Coefficient and Spearman Rank-Order Correlation Coefficient. Scatter plot. 9. Simple regression: Linear regression, least square method, residual SD, regression coefficients. Alternative models (linear, logarithmic, hyperbolic, polynomial). Variable transformations. Model selection (R-squared, R-squared adjusted, model selection via test). 10. Simple regression: Model control (testing of regression model and regression coefficients). General linear model. Residual analysis (randomness, independency, normality, homoskedasticity of residuals). Prediction. 11. Multiple regression: Selection of appropriate variables, correlation matrix, model comparison. Testing of regression model and regression coefficients. Residual analysis. 12. Time series: Types of time series, basic concepts, measures. Trend, periodic and random component. 13. Time series: Trend analysis, moving averages smoothing, smoothing with regression function. Forecasting.
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