SubjectsSubjects(version: 955)
Course, academic year 2023/2024
Statistics 2 - AB501014
Title: Statistics 2
Guaranteed by: Department of Economics and Management (837)
Faculty: Central University Departments of UCT Prague
Actual: from 2020
Semester: summer
Points: summer s.:6
E-Credits: summer s.:6
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Koťátková Stránská Pavla Ing. Ph.D.
Is interchangeable with: B501014
This subject contains the following additional online materials
Annotation -
In the Statistics II course, students will learn and practice use and interpretation of elementary statistical methods for data processing and basic analysis of economic and social phenomena. The students will learn how to distinguish the situations and appropriate methods for given circumstances and types of data. A guidance to proper presentation and interpretation of the data and their results will be provided as well. Selected topics will be presented in the computing environments of MS Excel and Gretl.
Last update: Scholleová Hana (10.12.2021)
Literature -

R: LIND, D., MARCHAL, W., WATHEN, S. Statistical Techniques in Business and Economics, (16th Edition). McGraw-Hill Education 2015. ISBN-13: 978-0078020520.

R: TRIOLA, M., F. Essentials of Statistics (5th Edition). Pearson Education 2015. ISBN-13: 978-0321924599.

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

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

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

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

Last update: Krajčová Jana (08.02.2021)
Requirements to the exam -

After obtaining the credit, a student will be able to 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).

Last update: Koťátková Stránská Pavla (12.09.2023)
Syllabus -

1. Repetition of the basics of statistics I Random variable and probability. Probability distributions. All in practice.

2. Repetition of the basics of statistics II. Descriptive statistics. Statistical inference - point and interval estimates. Hypothesis testing, basic parametric tests (equality of mean, variance, etc.). All in practice.

3. Repetition of the basics of statistics III. Basic non-parametric tests: Mann-Whitney, Wilcoxon rank-sum, sign test, etc.

4. Normal distribution and its importance. Standard tests of normality: Pearson Chi-squared test, Shapiro-Wilk test, Kolmogorov-Smirnov test.

5. Categorical data. Importance, interpretation, representation, analysis.

6. Association between ordinal and nominal variables. Contingency tables, Pearson Chi-squared test as test of independence.

7. Introduction to analysis of variance. Assumptions for 1-way ANOVA. Non-parametric one-way ANOVA (Kruskal-Wallis test). Multiple comparison.

8. Correlation analysis I. Graphical methods, Pearson and Spearman correlation coefficient. Tests of significance.

9. Correlation analysis for non-normal data: biserial and tetrachoric correlation.

10. Correlation analysis III. Multivariate correlation: pair, multiple, partial correlation.

11. Introduction to Regression analysis I. Correlation vs. causality. Simple linear regression model, tests of significance of coefficients, F-test, coefficient of determination. LRM in Gretl.

12. Introduction to Regression analysis II. Nonlinearities in simple regression model (logarithmic transformations, polynomials). Evaluating and interpreting the results, possible problems.

13. Methods of multivariate analysis. Cluster analysis, factor analysis, principal component analysis. Applications in marketing.

14. Final recap, consultations.

Last update: Scholleová Hana (10.12.2021)
Course completion requirements -

Credit can be awarded to student based on his participation in practical exercises and submitted homework during the semester. Alternatively student can pass a test at the end of the semester, with minimum required score of 60%. The minimum required attendance rate to seminars is 75%. The details will be agreed upon with the seminar instructor at the beginning of the semester.

A credit is required to allow a student to take the final exam. The final exam will cover both, the theory and the practical exercises. The exam will be in written form but can be complemented by oral examination.

Last update: Botek Marek (17.01.2020)
Teaching methods
Activity Credits Hours
Účast na přednáškách 1.1 32
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi 1.9 52
Příprava na zkoušku a její absolvování 1.9 52
Účast na seminářích 1.1 32
6 / 6 168 / 168
Coursework assessment
Form Significance
Examination test 80
Continuous assessment of study performance and course -credit tests 20