Course Credit Requirements

To obtain course credit, students must fulfil the following three requirements:

1. Attendance at seminars: A maximum of three absences is permitted. In the case of illness or other serious reasons, absences must be excused in advance.

2. Midterm credit test: Students must successfully complete a midterm credit test covering the material discussed during the semester. A minimum score of 60% is required to pass the test.

2. Semester project: The project must be submitted by the specified deadline(s). Students may be allowed one revision of the project. The decision on whether a revision will be permitted is entirely at the discretion of the respective instructor. Students are expected to demonstrate independence and a creative approach when completing the semester project.

The date of the credit test and deadline(s) for project submission will be announced at the beginning of the semester.

Make-up option:

Students who fail to meet any of the above requirements (insufficient attendance, failure to submit the project, or failure to achieve at least 60% on the midterm credit test) may be allowed to take a cumulative make-up credit test. This test covers practical problems from the material covered throughout the semester. To obtain course credit, students must achieve at least 60% on this test as well.

Exam

Only students who earned the credit can take the exam. The exam will be written, in pre-announced dates. Students have to register for the chosen date in SIS. The duration of the exam is 90 minutes and the maximum number of points is 100. The exam will consist of two parts - a theoretical part (maximum of 50 points) and a practical part (maximum of 50 points). To successfully pass the exam, a student needs to earn at least 25 points from each of the two parts. The final grade of students who earned the credit and met the minimum required-point levels in the exam is then determined on the basis of the total sum of points from the above mentioned requirements on the following scale: A 90-100%, B 80-90%, C 70-80%, D 60-70%, E 50-60%, F less than 50%.

Everyone is responsible for obliging to standard rules of academic honesty and integrity and for submitting own solutions to problems. All written tests are conducted in accordance with the common examination rules of the School of Business. Any violations of the exam rules or general academic honesty and integrity or of ethic codex (that includes any sort of cheating, copying of someone else’s work and presenting it as own, use of AI inappropriately and without properly citing it) will result in grade F.

Obligatory:

• STUDENMUND, A.H. . Using econometrics: A practical guide.. New York: Pearson Global Edition, 2017, s. ISBN 978-01-3136773-9.
• LEVINE, SZABAT, STEPHAN . Business Statistics: A First Course. . New York: Pearson Global Edition, 2016, s. ISBN .

Recommended:

• LIND, D., MARCHAL, W., WATHEN, S.. Statistical Techniques in Business and Economics, (16th Edition). . : McGraw-Hill Education. , 2015, s. ISBN 978-0078020520..
• WARNER, R.M. . Applied Statistics. . : SAGE Publicatons Inc., 2012, s. ISBN .
• TRIOLA, M., F. . Essentials of Statistics (5th Edition). : Pearson Education, 2015, s. ISBN .

Ke zkoušce se mohou přihlásit pouze studenti, kteří získali zápočet. Zkouška je písemná a probíhá v předem vyhlášených termínech. Na zvolený termín zkoušky se studenti přihlašují prostřednictvím systému SIS. Délka zkoušky je 90 minut a maximální počet bodů je 100. Zkouška se skládá ze dvou částí – teoretické části (maximálně 50 bodů) a praktické části (maximálně 50 bodů). Pro úspěšné složení zkoušky je nutné získat alespoň 25 bodů z každé části. Konečné hodnocení studentů, kteří získali zápočet a splnili minimální bodové hranice u zkoušky, se stanoví na základě celkového součtu bodů podle následující stupnice: A: 90–100 %, B: 80–89 %, C: 70–79 %, D: 60–69 %, E: 50–59 %, F: méně než 50 %.

1. Repetition of the basics of statistics I. Descriptive statistics - characteristics. Basic probability distributions – discrete and continuous.

2. Repetition of the basics of statistics II. Statistical induction - point and interval estimates. Hypothesis testing, selected basic parametric tests (equality of mean, variance, etc.).

3. Repetition of the basics of statistics III. Normal and standardized normal distribution, the use and practical significance. Verification of normality.

4. Introduction to analyzing dependence I. Types of variables and types of data. Types of relationships between variables, difference between correlation and causality. Testing the independence of categorical variables (Pearson's Chi-square test).

5. Introduction to analyzing dependence II. Analysis of variance (Anova). Verification of test assumptions: normality and variance within groups. One-way and two-way ANOVA, nonparametric versions of the test.

6. Correlation analysis. Correlation coefficients for two- and multi-dimensional sets of normally distributed random variables (paired, partial, multiple). Testing hypotheses about the correlation coefficient. Correlation coefficients for violations of normality (Spearman's correlation coefficient, tetrachoric and biserial correlation coefficient).

7. Introduction to regression analysis I. Simple and multidimensional linear regression model and other types of regression models.

8. Introduction to regression analysis II. Basic evaluation of estimation results. Testing hypotheses and constructing confidence intervals for model parameters. Coefficient of determination.

9. Linear regression model (LRM). Least squares method and its assumptions. Gauss-Markov theorem and required properties of estimation. Violation of GMV assumptions and their consequences.

10. Specification of LRM. Choice of explanatory variables and choice of the functional form. Nonlinear models which can be transformed into a linear one. Multicollinearity in LRM.

11. Evaluation of the quality of the linear regression model. Residual analysis. Homoskedasticity, autocorrelation and endogeneity in LRM (with relevant tests). Normality of residuals.

12. Introduction to time series analysis I. Specifics of time series and their importance. Descriptive characteristics of time series, visualizations. Decomposition of time series.

13. Introduction to time series analysis I. Trend analysis and possibilities for using LRM in time series analysis.

14. Final recap.