Course detail

Applied Analytical Statistics

FP-BAASEAcad. year: 2024/2025

Students will gain a basic understanding of discrete, continuous random variables and their important distribution types, processing of quantitative and qualitative trait data sets, point and interval estimation, the most used parametric and goodness-of-fit tests, simple and composite indices, linear and nonlinear regression models, and time series analysis.

Language of instruction

English

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

Ing. Karel Doubravský, Ph.D.

Department

Institute of Informatics (ÚI)

Offered to foreign students

Of all faculties

Entry knowledge

Basic knowledge of mathematical analysis is required for successful completion.

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 a theoretical question (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. Attendance at exercises is required and checked by the tutor. An excused absence of a student from seminars can be compensated for by submitting solution of alternate exercises.

COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY PLAN

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 a theoretical question (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).

Aims

Students will be introduced to the basic concepts of random variables of dikrete, continuous type and their important distributions, data set processing, point and interval estimation, statistical hypothesis testing, linear and nonlinear regression models, and time series analysis. Students will be able to apply relevant methods to solve practical problems. Upon completion of the course, students will be able to use statistical tools as a basis for data analysis in a real business environment so that they are able to obtain relevant information needed to support the management of business activities.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

FIELD, A., J. MILES and Z. FIELD. Discovering Statistics Using R. 1 edition. Los Angeles, Calif.: SAGE Publications Ltd., 2012. ISBN 978-1-4462-0046-9. (EN)
MATHEWS, P. Design of Experiments with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 978-08-738-9637-5. (EN)
Study materials available in the Moodle E-learning system. (EN)

Recommended reading

BOX, George E. P., William Gordon HUNTER a J. Stuart HUNTER, 1978. Statistics for experimenters: an introduction to design, data analysis, and model building. B.m.: Wiley. ISBN 978-0-471-09315-2. (EN)
KARPÍŠEK, Z. a M. DRDLA. Applied Statistics. Brno University of Technology, Faculty of Business and Management. Brno, 1999. ISBN 80-214-1493-6. (EN)
MONTGOMERY, Douglas C., 2008. Design and Analysis of Experiments. B.m.: John Wiley & Sons. ISBN 978-0-470-12866-4. (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme BAK-E Bachelor's

    branch BAK-ESBD , 1 year of study, summer semester, compulsory

  • Programme BAK-ESBD Bachelor's 2 year of study, summer semester, compulsory

  • Programme BAK-Z Bachelor's

    branch BAK-Z , 1 year of study, summer semester, elective

Type of course unit

 

Lecture

13 hod., optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

The course explains the basic ideas of probability theory, random variables, mathematical statistics, correlation analysis, categorical analysis, regression analysis and time series analysis.

Basic contents:

1. Discrete and continuous random variable (basic concepts, empirical and function characteristics)
2. Important type of distributions (Binomial distribution, Poission distribution, Gauss distribution, Exponential distribution...)
3. Bivariate random variables (correlation)
4. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function)
5. Data sample analysis
6. Parameters’ estimation (point and interval estimates)
7. Test of statistical hypothesis (basic concepts and procedure)
8. Basic parametric tests (t-test, F-test, ANOVA)
9. Index analysis
10. Individual and composite indexes
11. Linear regression model (basic concepts, the least square method)
12. Non-linear regression model (linearizable and non-linearizable regression models)
13. Time series analysis (basic characteristics, decomposition)

Exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

The topics of exercises correspond to the topics of lectures.

Elearning

eLearning: currently opened course