 Subjects(version: 901)

Statistics 1 - AB501009
Title: Statistics 1 Department of Economics and Management (837) from 2020 to 2021 winter winter s.:6 winter s.:6 winter s.: winter s.:2/2 C+Ex [hours/week] unknown / unknown (unknown) unlimited English full-time Bachelor's course can be enrolled in outside the study planenabled for web enrollment
Guarantor: Koťátková Stránská Pavla Ing. Ph.D. B501009
This subject contains the following additional online materials
 Literature - ---CzechEnglish
Last update: Krajčová Jana Mgr. Ph.D., M.A. (15.09.2020)

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

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

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

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

 Requirements to the exam - Czech
Last update: Fialová Jana (14.01.2020)

Zápočet: aktivní účast na cvičeních, zpracování korespondenčních úkolů, závěrečné zápočtové písemné práce

Zkouška: písemná - část teoretická a část praktická

 Syllabus
Last update: Scholleová Hana doc. RNDr. Ing. Ph.D. (10.12.2021)

1. Introduction to Statistics. Types of data, data representation and visualization.

2. The essentials of probability theory. Random Experiments, Sample space, Events, Probabilities.

3. Axioms of probability. Elementary probability theorems, conditional probability, multiplication rule. Subjective probability.

4. Random variable and probability theory. Random variable, frequency, probability distribution and its representation and main characteristics. Probability function, density function, cumulative distribution function and their properties.

5. Selected probability distributions I. Discrete random variable.

6. Selected probability distributions II. Continuous random variable.

7. Multidimensional random variable. Random vectors and multivariate probabilistic distributions.

8. Joint, marginal and conditional probability. Independence.

9. Storing data in random variables, introducing descriptive statistics, characteristics of location and of variability, central moments. Variance decomposition.

10. Introduction to statistical inference. From understanding a sample to assessing population. Point and interval estimates.

11. Statistical inference continued. Hypothesis testing: null and alternative hypothesis, level of significance, critical values and rejection interval, type I and type II errors, p-value, one-sided and two-sided alternative hypothesis.

12. Basic parametric tests: equality of mean, variance, one-sample or two-sample tests.

13. Introduction to non-parametric testing. Importance of normality. Assigning ranks. Selected non-parametric tests: Mann-Whitney, Wilcoxon rank-sum, sign test.

14. Final recap, consultations.

 Course completion requirements - ---CzechEnglish
Last update: Botek Marek Ing. Mgr. Ph.D. (17.01.2020)

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.

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