Course detail

Statistics 2

FP-stat2PAcad. year: 2024/2025

The course deals with main ideas and methods of point and interval estimates, the most used parametric and nonparametric tests, good fit tests, an analysis of variance, a categorial analysis, linear and nonlinear multiple regression models and time series analysis.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. RNDr. Bedřich Půža, CSc.

Department

Institute of Informatics (ÚI)

Entry knowledge

Fundamentals of a probability theory and a random variable.

Rules for evaluation and completion of the course

COURSE COMPLETION

The course-unit credit is awarded on the following conditions (max. 40 points):
- preparation of semester assignments (the topic of the assignments will be specified during the semester).

The exam (max. 60 points)
- has a written form with the possibility of using computer technology and consists of four computational examples and a theoretical question.

The grade, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semester assignments (max. 40 points),
- points achieved by solving examples (max. 51 points),
- points achieved by answering theoretical questions (max. 9 points).

The grade and corresponding points:
A (100-90), B (89-80), C (79-70), D (69-60), E (59-50), F (49-0).

Attendance at lectures is not mandatory but is recommended. 

Aims

The objective of this course is to familiar students with ideas and methods of point and interval estimates, the most used parametric and nonparametric tests, good fit tests, an analysis of variance, a categorial analysis, linear and nonlinear multiple regression models and time series analysis.
Students will be made familiar with the methods of mathematical statistics, regression analysis, and time series analysis and will learn how to use the respective methods when solving economics problems. After completing this course, students will be able to use statistical tools as a basis for data analysis in the management of individual company activities.

Study aids

see Course literature.
Study materials available on e-learning.

Prerequisites and corequisites

Not applicable.

Basic literature

BUDÍKOVÁ, M.; LERCH, T. a MIKOLÁŠ, Š. Základní statistické metody. Brno: Masarykova univerzita v Brně, 2005. ISBN 80-210-3886-1. hospodářství. Praha : Victoria Publishing, 1995. ISBN 80-7187-058-7.
Studijní materiály dostupné na e-learningu. (CS)

Recommended reading

ARLT, J. a ARLTOVÁ, M. Ekonomické časové řady. Praha: Professional Publishing, 2009. ISBN 978-808-6946-85
GUJARATI, D. N. a  PORTER, D.C. Basic econometrics. 5th ed. Boston: McGraw-Hill Irwin, 2009. ISBN 978-007-3375-779.

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme MGR-MEO Master's 1 year of study, winter semester, compulsory
  • Programme MGR-UFRP Master's 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

Topics of lectures are the following:

  1. Analysis of data sets.
  2. Parametric tests: t-test, paired t-test, two-sample t-test, F-test.
  3. Kolmogorov-Smirnov test, Shapiro-Wilk test, Pearson test.
  4. Analysis of variance: one-factor and two-factor ANOVA.
  5. Non-parametric tests: one-sample tests.
  6. Non-parametric tests: two-sample tests.
  7. Non-parametric analogues of analysis of variance.
  8. Categorical analysis.
  9. Multivariate regression models.
  10. Logistic regression.
  11. Time series analysis.
  12. Panel data.
  13. Exploratory analysis of multivariate data.

Exercise

13 hod., compulsory

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

Exercise promote the practical knowledge of the subject presented in the lectures.

Elearning

eLearning: currently opened course