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Terms for 2017

September, 5

Admission Interviews

September, 7

Open Day

September, 12

Admission Interviews

September, 14

Open Day

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Statistics

The aim of the subject is to get students acquainted with elements of probability, elementary statistical conceptions and possibilities of data analyzing and presenting, get students acquainted with the most commonly used statistical procedures and methods, with conditions for their use, advantages and shortcomings and to teach students to interpret results correctly.

What are you going to learn

  1. Descriptive Statistics - Meaning and concept of modern statistics, basic statistical terms, processing of numeric variables, frequency distribution, tables, graphs (histogram, polygon).
  2. Characteristics of unidimensional statistical files - Description of unidimensional statistical files, degree position (level): average, mode, median, quantile, rate variability (variation margin, variance, standard deviation, coefficient of variation), degree of obliqueness and sharpness.
  3. Probability theory - Random events and operations with random events, definition of probability, rules for counting probability, random variables and their distribution, characteristics of random variables.
  4. Probability theory (distribution of random variables) - The most important types of probabilistic distributions (discrete distributions - binomial, Poisson, hypergeometric), the most important types of probabilistic distribution (continuous distributions: uniform, exponential, normal, t, F, chi-sq).
  5. Limit theorems. Methods of statistical induction - Limit theorems, types of statistic surveys, random sampling, basic theory of statistical estimation (point and interval estimates)
  6. Testing of statistical hypotheses - Testing of statistical hypotheses, basic concepts and process of testing hypotheses, parametric tests with parameters of the basic file.
  7. Methods of examining dependencies - Distribution of basic file test, examining addictions of categorial features, construction of contingency tables, test of independence in contingency tables, leak rate of dependency (Cramér's and Pearson's contingency coefficient).
  8. Types of dependencies between numeric characters - Basic methods of monitoring and assessing the dependence between quantitative variables, simple regression analysis, basic concepts, choice of the type of regression function, linear regression model, estimation of parameters of regression model (method of the smallest squares).
  9. Assessment of the quality of a regression model. Correlation Analysis - Assessment of the quality of a regression model, measurement of leak rate of linear dependence - the correlation analysis.
  10. Time series - Chronological average, elemental characteristics of time series, classical decomposition of time series (additive, multiplicative model).
  11. Time series - trend component - Analytical compensation of time series - trend modeling, adaptive approaches to modeling time series (moving averages), the seasonal component of time series.
  12. Time series - Seasonal adjustment, quality of a model. Seasonal adjustment, assessment of quality of a model, possibilities of construction of forecasts.

How the course is organized

Full time study

The course consist of 12 lectures and 12 seminars, each lasting 1,5 hours.

Part time study

The course consist of 3 blocks, each lasting 3 hours.