|
Lecturer(s)
|
-
Boďa Martin, doc. PhDr. Ing. PhD.
|
|
Course content
|
1. Descriptive statistics for univariate and multivariate data: measures of central tendency, dispersion, correlation and dependence. 2. Exploratory data analysis: visual display of quantitative data. 3. Theory of random variables and probability laws. 4. Discrete probability distributions, their properties and applications. 5. Continuous probability distributions, their properties and applications. 6. Laws of large numbers, central limit theorem. 7. Concepts of inferential statistics, theory of random sample. 8. Point and confidence estimation of univariate parameters. 9. Testing of hypotheses of univariate parameters: parametric and non-parametric approaches.
|
|
Learning activities and teaching methods
|
|
unspecified
|
|
Learning outcomes
|
The course aims at equipping students with basic notions of probability theory and mathematical statistics applicable in descriptions of natural and socioeconomic phenomena. The course begins with an elementary introduction into univariate and multivariate descriptive statistics, then makes through a selective overview of probability theory, and finalizes with basic introduction into inferential statistics. The ambition is to imprint on students an understanding of statistical thinking. Students are expected to gain practical skills by working with data in a suitable statistical software package (Microsoft Excel, R).
|
|
Prerequisites
|
unspecified
|
|
Assessment methods and criteria
|
unspecified
|
|
Recommended literature
|
|