Statistical Analysis
Code  Completion  Credits  Range  Language 

32MCKSTAN01  Z,ZK  5  24B  Czech 
 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:

Credit requirements:
1. Attendance at exercises, when a proper apology excuses a maximum of three absences.
2. Active participation in exercises, which means solving tasks assigned directly in class. To obtain the credit, the student must get at least 5 points for the activity (one solved example = 1 point), the solution of the example on the board is included, separately in a notebook, or completed at home (submit no later than the beginning of the following exercise).
3. Processing and submission of homework using MS Excel. A student who submits at least three such assigned tasks in a reasonable quality (assessed by the teacher) and at the same time meets the conditions of the 1st and 2nd obtains the credit automatically, without the need to complete a credit paper.
4. Successful elaboration of the final credit written work, for min. 50% (except those that meet point 3). The final credit written work will occur in the last week of teaching, at the time of practice.
 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://moodlevyuka.cvut.cz/course/view.php?id=5809
 Timetable for winter semester 2024/2025:

06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri  Timetable for summer semester 2024/2025:
 Timetable is not available yet
 The course is a part of the following study plans: