151-0352/01 – Correlation and Regression Analysis (KRA)
| Gurantor department | Department of Mathematical Methods in Economics | Credits | 4 |
| Subject guarantor | prof. Ing. Jana Hančlová, CSc. | Subject version guarantor | prof. Ing. Jana Hančlová, CSc. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2010/2011 | Year of cancellation | 2012/2013 |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master, Bachelor |
Subject aims expressed by acquired skills and competences
The goal is to:
- Be able to examine the relationship between variables,
- Understand the process of modeling the behavior of economic systems based on regression analysis,
- Select and use appropriate methods to the formulation, estimation, prediction and verification of modeled systems,
- Explain the behavior of the theoretical context of economic systems modeled with empirical results and make appropriate modifications and corrections model
- Use the estimated regression models to predict and determine the success of prediction,
- Learn how to use SPSS software product for regression and correlation analysis.
Teaching methods
Lectures
Tutorials
Other activities
Summary
1. Pearson's correlation coefficient - the characteristics, correlation and covariance, correlation and causality.
2. The serial correlation coefficients, correlations for dichotomous variables, testing hypotheses about the significance of coefficients, tests of equality of two coefficients.
3. The correlation matrix, partial correlation and its significance.
4. Correlation and linear regression equation (straight line), features a simple linear regression, principle of the method of least squares.
5. Hypothesis testing and parameter estimation of the regression straight line coefficient determination, residues and their analysis.
6. Nonlinear regression and be transformed into a linear regression model.
7. Prediction of the regression equation.
8. Multiple linear regression model, significance of regression coefficients, test of significance of regression coefficients.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
1. Preparation and submission of homework.
2. Successful completion of ongoing testu1.
3. Successful completion of the final testu2.
Deadlines for submission of homework and deadlines and testu1 testu2 timetable are given subject. Each test is possible to repeat once again. Successful completion is achieved at least half the possible points in each test and assignment.
E-learning
http://moodle.vsb.cz/
Other requirements
not *******************************
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Pearson's correlation coefficient - the characteristics, correlation and covariance, correlation and causality.
2. The serial correlation coefficients, correlations for dichotomous variables, testing hypotheses about the significance of coefficients, tests of equality of two coefficients.
3. The correlation matrix, partial correlation and its significance.
4. Correlation and linear regression equation (straight line), features a simple linear regression, principle of the method of least squares.
5. Hypothesis testing and parameter estimation of the regression straight line coefficient determination, residues and their analysis.
6. Nonlinear regression and be transformed into a linear regression model.
7. Prediction of the regression equation.
8. Multiple linear regression model, significance of regression coefficients, test of significance of regression coefficients.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.