Course detail

Applied Analytical Statistics

FP-BAASEAcad. year: 2023/2024

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

6

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

Students will gain knowledge of random variables, mathematical statistics, categorical and correlation analysis, analysis of variance, regression analysis and time series analysis and their use in business process management. Emphasis is primarily placed on the practical part, which is aimed at familiarizing with the use of statistical programs in the implementation of the above-mentioned methods and procedures.

Rules for evaluation and completion of the course

The course-unit credit is awarded on the following conditions (max. 40 points):
- elaboration of semestral assignments.

The exam (max. 60 points)
- has a written form.
In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to a theoretical question within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semestral assignments,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

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

 


COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY

The course-unit credit is awarded on the following conditions (max. 40 points):
- elaboration of semestral assignments.

The exam (max. 60 points)
- has a written form.
In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to a theoretical question within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semestral assignments,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

The grades 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 seminars is controlled.

Aims

Students will be introduced to basic concepts of discrete and continuous random variables and their major distribution, processing data files, point and interval estimation, hypothesis testing, linear and nonlinear regression models and time series analysis. Students will be able to use appropriate methods in dealing with informatics and economic problems. After completing the course, students will be prepared to practically apply these methods in ICT and related economic subjects using statistical programs.
Students acquire basic knowledge of random variables and important types of their distribution, processing data sets of quantitative and qualitative character, point and interval estimation, the most widely used parametric tests and tests of goodness of fit, simple and complex indices, linear and nonlinear regression models and analysis of time series, and will be able to use this knowledge in real business environment so that they are able to receive 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)

Recommended reading

KARPÍŠEK, Z. a M. DRDLA. Applied Statistics. Brno University of Technology, Faculty of Business and Management. Brno, 1999. ISBN 80-214-1493-6. (EN)
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)
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-Z Bachelor's

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

  • 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

Type of course unit

 

Lecture

13 hours, optionally

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

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 hours, compulsory

Teacher / Lecturer

Ing. Karel Doubravský, Ph.D.

Syllabus

The topics of exercises correspond to the topics of lectures.

eLearning

eLearning: currently opened course