Statistics

Code	Completion	Credits	Range	Language
32BC-P-STAT-01	Z,ZK	6	2P+2C	Czech

Course guarantor:

Tomáš Macák

Lecturer:

Tomáš Löster, Tomáš Macák, Jiří Zmatlík

Tutor:

Tomáš Löster, Tomáš Macák, Jiří Zmatlík

Supervisor:

Institute of Economic Studies

Synopsis:

Upon successful completion, students will acquire basic knowledge of applied statistics in the field of descriptive techniques of data sets, regression and correlation analysis, variance analysis, regression analysis, design and evaluation of experiments, hypothesis testing, and time series analysis. After completing the course, students will be ready to practically use these methods in follow-up courses and practical tasks in a business environment.

Requirements:

The following forms of attestation verify the study results:

a) credit (C)

b) examination (Exam)

Credits are awarded for meeting the teacher's requirements at the beginning of the semester. In Statistics I, the minimum active participation in the exercise is 75%, the development of a semester project of the required quality and scope is at a minimum level of 60%, and the writing of a credit test is at a minimum level of 60%.

The follow-up exam is a form of attestation that tests the student's knowledge of the principles and procedures within the subject areas listed below for the subject Statistics. The exam is always written and is usually supplemented by an oral part.

Syllabus of lectures:

1. Introduction to the problem, random variable, probability function, probability density, distribution function.

2. Basic types of distribution of discrete and continuous random variables, derivation of their essential numerical characteristics.

3. Numerical characteristics of a random variable, data types, mean value, variance, skewness coefficient, kurtosis coefficient, p-quantile, median, and mode.

4. Random vectors, distribution and frequency functions of a random vector, independent of components of a random vector, marginal probability distribution.

5. A statistical set with one argument, regression and correlation analysis, derivation of coefficients of the equation of a straight line, and assumptions of a linear regression model.

6. Non-linear regression, correlation analysis, correlation field with a general course, addition function, coefficient of determination.

7. Application of regression analysis for planning and designing experiments, the strength of factor influence on response, the interaction between factors, problems with the „change of only one factor“ approach, factorial designs, and analysis of variance.

8. Binary synthesis of the response variable and eliminating redundant factors in the experiment.

9. Statistics, statistical hypothesis, statistical tests, interval estimation, level of significance, critical values, test criteria, type 1 and type 2 errors.

10. Parametric and non-parametric statistical hypothesis testing. Single-choice and double-choice tests.

11. Dependency analysis of nominal and ordinal data type, contingency table, and independence hypothesis testing.

12. Statistical regulation. Shewhart control diagrams, Markov chains.

13. Methods of multidimensional analysis, external and internal analysis (principal component analysis, factor analysis, cluster analysis).

14. Choice of statistical methods, classification of statistical methods, problems of significance tests, Bayesian approach, computationally intensive methods.

Syllabus of tutorials: