• Statistics and Real World Evidence

# Courses

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## 4ST201 Statistics

 Aims of the course: The aim of the first part of this classwork is to get students acquainted with elements of probability, elementary statistical conceptions and possibilities of data analyzing and prezenting as well as with some elements of deductive and inductive ways of thinking. The aim of the second part is to get students acquainted with most used statistical procedures and methods, with conditions for their use, advantages and shortcomings and to teach students correctly interpret results. Learning outcomes and competences: Upon successful completion of this course, students will be able to select suitable statistical method for the description and analysis of given economic problem and to interpret the results obtained with the use of statistical method applied including the interpretation of computer outputs. Course contents: - analysis of one-dimensional statistical data - operations with random events and their probabilities - random variables and their probability distributions - the most important types of probability distributions - inferences based on random samples - methods of measurement of dependence among quantitative and qualitative variables, further some procedures used for categorical data analysis, review of classificatory approaches and some non-parametric methods, respectively - methods of time series analysis - methods of analysis of sample data - methods of statistical comparison.

7 Feb, 2018

## 4ST210 Statistics for Finance

 Aims of the course: It acquaints students with elementary statistical conceptions, data analysis and presentation. Attention is focused especially on the most commonly used statistical procedures and methods. Learning outcomes and competences: Upon successful completion of this course, students will be able to select a suitable statistical method for analysis of given economic problem and to interpret the results obtained with the use of statistical approach. Course contents: - analysis of one-dimensional statistical data - random variables and their probability distributions, the most important types of probability distributions - inferences based on random samples, theory of statistical estimates and statistical hypotheses testing, selected parametric and nonparametric tests, - methods of measurement of quantitative and qualitative variables relationships, simple and multiple regression and correlation analysis, one-dimensional ANOVA, - economic time series analysis, methods of time series description, time series decomposition, analytical smoothing, adaptive methods, seasonal adjustment, forecasting, - methods of statistical comparison, indexes.

7 Feb, 2018

## 4ST220 Introduction to Probability Theory and Mathematical Statistics

 Aims of the course: The subject is aimed at extending and perfecting student’s knowledge of probability and mathematical statistics. Learning outcomes and competences: Upon successful completion of this course, students will be able to apply probability and mathematical statistics and interpret results of these applications correctly. Course contents: 1. Introduction to probability calculus - random event and its probability - random variable, random vector - methods of describing of probability distributions - characteristics of random variables - the most important types of probability distributions, their characteristics and application - law of large numbers - central limit theorem. 2. Introduction to mathematical statistics - random sample, statistics, sample distributions - point and interval estimation - statistical hypotheses testing.

## 4ST318 Success in Statistics

 Aims of the course: The course is designed for all students interested in deepening their knowledge of statistical concepts in the English language; it broadens the mastery of procedures and methods of statistical data analysis. The understanding of the course content in Czech is an advantage, nevertheless, it is not a requirement. The aim of the first part of this course is to get students acquainted with the elements of probability, elementary statistical concepts, data analysis and presentation as well as some elements of deductive and inductive ways of thinking. The aim of the second part is to get students acquainted with the principles of the most widely used statistical methods and conditions for their use and to teach students to correctly interpret the results. Learning outcomes and competences: After a successful completion of this course, students will be able to apply the most important elementary statistical methods. Furthermore, students will be able to select a suitable statistical method for the description and analysis of a given problem and interpret the results obtained with the use of the method selected. Students will be able to present the achieved results in English and they will understand spoken and written texts on corresponding subjects. Course contents: Elementary descriptive statistics Elements of probability theory Discrete random variables and their probability distributions Continuous random variables and their probability distributions Elements of sampling and statistical inference Hypotheses testing Analysis of variance Regression analysis Time series analysis Index numbers

## 4ST601 Statistics

 Aims of the course: The aim of the first part of this course is to get students acquainted with elements of probability, elementary statistical concepts and possibilities of data analyzing and presenting as well as some elements of deductive and inductive ways of thinking. The aim of the second part is to get students acquainted with principles of the most used statistical methods and with conditions for their use and to teach students to correctly interpret the results. Learning outcomes and competences: Upon successful completion of this course, students will be able to apply the most important statistical methods. Further, students will be able to select suitable statistical method for the description and analysis of a given problem and interpret the results obtained with the use of the statistical method applied. Course contents: Elementary descriptive statistics (frequencies, measures of central tendency and dispersion, ...) Elements of probability theory Random variables and their probability distributions The most important probability distributions Elements of sampling and statistical inference (estimates, hypotheses testing, selected tests) Methods of measurement of dependence among quantitative and qualitative variables (contingency tables, analysis of variance, regression analysis, correlation analysis) Time series analysis (elementary descriptives, time series decomposition, smoothing, ...) Index numbers (types of indices and differences, simple indices, aggregate indices, ...)

## 4ST616 Regression

 Aims of the course: Regression analysis is a very important tool used for studying relationships between variables. The course provides an insight into the concept of linear regression models. Properties of ordinary least square estimates of the parameters of these models are provided. A brief discussion of other techniques which are relevant to regression analysis (such as non-parametric regression, robust regression, bootstrapping etc.) is also included. The course also provides a guide how to apply the methods to real data. Learning outcomes and competences: Upon successful completion of this course, students will be able to understand linear regression models, their properties and limitations. They will understand the concept of linear regression models in a wider context. Students will be able to apply the methods to real data. Course contents: * Regression model, linear regression model * Classical linear regression model, ordinary least squares estimation and its properties * Categorical explanatory variables * Transformation of variables * Relative importance of explanatory variables in linear regression model * Diagnostics of residuals, violation of assumptions and its correction * Variable selection, multicollinearity * Bootstrapping in regression * Robust and non-parametric regression