Credit: active participation ins seminar, submission of term project, or passing the credit test (practical)

Exam: written - theoretical and practical part

R: All the class materials at e-learning.vscht.cz

R: STUDENMUND, A.H. Using econometrics: A practical guide. New York: Pearson Global Edition, 2017. ISBN 978-01-3136773-9.

R: LEVINE, SZABAT, STEPHAN (2016), Business Statistics: A First Course. New York: Pearson Global Edition.

A: LIND, D., MARCHAL, W., WATHEN, S. (2015), Statistical Techniques in Business and Economics, (16th Edition). McGraw-Hill Education.

A: MENDENHALL, W.M, SINCICH, T.L. Statistics for Engineering and Sciences. 8th ed.. Taylor & Francis Inc., 2016.

A: WARNER, R.M. Applied Statistics. SAGE Publicatons Inc., 2012.

A: TRIOLA, M., F. (2015), Essentials of Statistics (5th Edition), Pearson Education.

A: ZÁŠKODNÝ, Přemysl (2012), The Principles of Probability and Statistics (Data Mining Approach). Praha: Curriculum.

A: SALKIND, N.J. Excel Statistics. Sage Publications, 2015.

Zápočet: aktivní účast na cvičeních, zpracování semstrálního projektu, popř. závěrečná zápočtová písemné práce

Zkouška: písemná - část teoretická a část praktická

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.