Lectures:
1. Getting to know the contents of the course, Introduction to probability theory
2. Random variable
3. Random vector
4. Probability models for discrete random variables
5. Probability models for continous random variables
6. Statistical survey and exploratory analysis
7. Sample characteristics, Introduction to estimation theory
8. Hypothesis testing - principle
9. One-sample parametric tests of hypotheses
10. Goodness of Fit tests
11. Tests for comparing more than two variances, ANOVA, Kruskal-Wallis test, methods of post hoc analysis for ANOVA and Kruskal-Wallis test
12. Analysis of the independence (introduction to correlation analysis, analysis of the independence of two categorical variables, measures of contingency, measures of association)
13. Introduction to regression analysis
Exercises:
1. Combinatorics, Classical probability
2. Probability Theory
3. Random variable
4. Random vector
5. Discrete random variable
6. Continuous random variable
7. Limit theorems, special continuous distributions
8. Exploratory statistics
9. Estimates of population parameters
10. Hypothesis testing I
11. Hypothesis testing II
12. ANOVA, Kruskal-Wallis test
13. Introduction to Regression Analysis
14. Presentation of the Project
For computer labs are provided two software products in the English language: Statgraphics v. 5.0 and MS Excel.
Term project:
The project is a record of data-generation process in which the student-applicant process applies the theoretical knowledge, practiced with the help of available software. Students may choose the topic of the project environment, which is close to him. In the project, the student must demonstrate the ability to correctly demonstrate and interpret data record related to the topic and ability to perform in accordance with the objective of the project one of the methods of statistical induction (eg, decision-making through the test, study the dependencies between variables, the design point and interval estimates of unknown parameters of probabilistic distribution, etc.)
1. Getting to know the contents of the course, Introduction to probability theory
2. Random variable
3. Random vector
4. Probability models for discrete random variables
5. Probability models for continous random variables
6. Statistical survey and exploratory analysis
7. Sample characteristics, Introduction to estimation theory
8. Hypothesis testing - principle
9. One-sample parametric tests of hypotheses
10. Goodness of Fit tests
11. Tests for comparing more than two variances, ANOVA, Kruskal-Wallis test, methods of post hoc analysis for ANOVA and Kruskal-Wallis test
12. Analysis of the independence (introduction to correlation analysis, analysis of the independence of two categorical variables, measures of contingency, measures of association)
13. Introduction to regression analysis
Exercises:
1. Combinatorics, Classical probability
2. Probability Theory
3. Random variable
4. Random vector
5. Discrete random variable
6. Continuous random variable
7. Limit theorems, special continuous distributions
8. Exploratory statistics
9. Estimates of population parameters
10. Hypothesis testing I
11. Hypothesis testing II
12. ANOVA, Kruskal-Wallis test
13. Introduction to Regression Analysis
14. Presentation of the Project
For computer labs are provided two software products in the English language: Statgraphics v. 5.0 and MS Excel.
Term project:
The project is a record of data-generation process in which the student-applicant process applies the theoretical knowledge, practiced with the help of available software. Students may choose the topic of the project environment, which is close to him. In the project, the student must demonstrate the ability to correctly demonstrate and interpret data record related to the topic and ability to perform in accordance with the objective of the project one of the methods of statistical induction (eg, decision-making through the test, study the dependencies between variables, the design point and interval estimates of unknown parameters of probabilistic distribution, etc.)