Statistical Analysis

Course guarantor:

Tomáš Löster

Lecturer:

Tomáš Löster

Tutor:

Tomáš Löster

Supervisor:

Institute of Economic Studies

Synopsis:

The course builds on the introductory courses of statistics and prefaces slightly advanced statistical analysis methods.

Requirements:

Students complete the course with a credit and an exam.

Credit requirements:

1. Attendance at teaching blocks (min. 80%).

2. Active participation in exercises, which means solving problems assigned directly in class.

Exam requirements:

After receiving the credit, the student will be able to take the exam. The exam will be written, on pre-determined dates. The exam will consist of two parts - a theoretical part and a practical (or computational) part. To successfully complete the exam, the student must obtain at least 50%.

Syllabus of lectures:

[1] Repetition of the basics of statistics I. Descriptive statistics. Statistical induction.

[2] Repetition of the basics of statistics II. Hypothesis testing.

[3] Analysis of variance (ANOVA).

[4] Correlation analysis. Correlation coefficients. Hypothesis testing and confidence intervals for the correlation coefficient.

[5] Regression analysis I. Correlation vs. Causality. Testing of parameter hypotheses and confidence intervals of model parameters.

[6] Regression analysis II. Evaluation of the quality of the regression model. Nonlinear models transformations into a linear shape.

[7] Regression analysis III. A multidimensional model of linear regression. Multiple correlation coefficient.

[8] Regression analysis IV. Discussion of assumptions of model application and consequences of its violations. Residual analysis. Tests of homoskedasticity, multicollinearity, and autocorrelation.

[9] Time series I.

[10] Time series II..

[11] Time series III.

[12] Time series IV.

[13] Description of multivariate statistical methods.

[14] Choice of statistical method.

Syllabus of tutorials:

[1] Repetition of the basics of statistics I. Descriptive statistics. Statistical induction.

[2] Repetition of the basics of statistics II. Hypothesis testing.

[3] Analysis of variance (ANOVA).

[4] Correlation analysis. Correlation coefficients. Hypothesis testing and confidence intervals for the correlation coefficient.

[5] Regression analysis I. Correlation vs. Causality. Testing of parameter hypotheses and confidence intervals of model parameters.

[6] Regression analysis II. Evaluation of the quality of the regression model. Nonlinear models transformations into a linear shape.

[7] Regression analysis III. A multidimensional model of linear regression. Multiple correlation coefficient.

[8] Regression analysis IV. Discussion of assumptions of model application and consequences of its violations. Residual analysis. Tests of homoskedasticity, multicollinearity, and autocorrelation.

[9] Time series I.

[10] Time series II..

[11] Time series III.

[12] Time series IV.

[13] Description of multivariate statistical methods.

[14] Choice of statistical method.

Study Objective:

The course will aim to acquaint students with basic methods in evaluating one- and multidimensional sample statistical files, with methods of dependency analysis, modeling and analysis of time series, and basic types of indices used to compare economic indicators. Students will gain experience using statistical methods in technical and economic practice, computer data processing, and interpretation of results.

Study materials:

Note:

Further information:

https://moodle-vyuka.cvut.cz/course/view.php?id=5809

Time-table for winter semester 2026/2027:

Time-table is not available yet

Time-table for summer semester 2026/2027:

Time-table is not available yet

The course is a part of the following study plans:

• kombi - Project and Process Innovation Management, N0413A050002 Projektové řízení inovací (compulsory course)
• kombi - Management inovací v regionech, N0413A050002 Projektové řízení inovací (compulsory course)
• kombi - Management moderních výrobních technologií, N0413A050002 Projektové řízení inovací (compulsory course)