Subjects

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Statistical Data Analysis - AM143002

Title:	Statistical Data Analysis
Guaranteed by:	Department of Informatics and Chemistry (143)
Faculty:	Faculty of Chemical Technology
Actual:	from 2026
Semester:	winter
Points:	winter s.:5
E-Credits:	winter s.:5
Examination process:	winter s.:
Hours per week, examination:	winter 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
Level:
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	Svozil Daniel prof. Mgr. Ph.D.
Classification:	Mathematics > Probability and Statistics
Interchangeability :	N143042

Examination dates Schedule

Annotation

This course offers a comprehensive introduction to statistical data analysis, focusing on the fundamental concepts and techniques used in descriptive and inferential statistics. Designed for students with a basic understanding of mathematics, the course aims to develop practical skills in analyzing, interpreting, and presenting statistical data. Through a blend of theoretical knowledge and hands-on experience in R programming language, students will learn to apply statistical methods to various real-world scenarios.

Last update: Svozil Daniel (11.03.2026)

Course completion requirements

The credit is given on the completion of an individual project. The exam is written.

Last update: Svozil Daniel (11.03.2026)

Literature

Obligatory:

Rumsey, Deborah. Statistics for dummies. Hoboken: Wiley, 2003, xviii, 355 s. s. ISBN 0-7645-5423-9.

Rumsey, Deborah. Intermediate statistics for dummies. Indianapolis: Wiley, 2007, xviii, 362 s. s. ISBN 978-0-470-04520-6.

Motulsky, Harvey. Intuitive biostatistics, a nonmathematical guide to statistical thinking. New York: Oxford University Press, 2014, xxxv, 540 s. s. ISBN 978-0-19-994664-8.

Venables, W. N., Smith, D. M.. An introduction to R. Bristol: Network Theory, 2001, 139 s. s. ISBN 0-9541617-4-2.

Last update: Svozil Daniel (11.03.2026)

Requirements to the exam

written

Last update: Svozil Daniel (11.03.2026)

Syllabus

1. Descriptive statistics – graphical representation, measures of data centrality

2. Descriptive statistics – measures of variability in data

3. Normal distribution, Z distribution, lognormal distribution

4. Sampling distribution and the central limit theorem

5. Confidence interval

6. Hypothesis testing – critical region, one-tailed and two-tailed tests

7. Hypothesis testing – tests of the mean

8. Hypothesis testing – analysis of variance (ANOVA)

9. Covariance and correlation analysis

10. Linear regression – simple

11. Linear regression – multiple

12. Reserve

13. Summary

14. Early exam date

Last update: Svozil Daniel (11.03.2026)

Learning resources

Online course materials at UCT e-learning.

Online textbooks:

Last update: Svozil Daniel (11.03.2026)

Learning outcomes

Students will be able to:

apply descriptive statistical methods, including graphical data representation and the calculation of measures of central tendency and variability,

explain the properties of basic probability distributions and use the central limit theorem when working with sampling distributions,

construct and interpret confidence intervals for selected statistical characteristics,

perform and interpret statistical hypothesis testing, including tests of means and analysis of variance (ANOVA),

analyze relationships between variables using covariance, correlation, and simple as well as multiple linear regression.

use the R software (environment for statistical computing)

do the exploratory analysis and hypotheses testing in R

do the regression analysis in R

analyze multidimensional data in R

present results of statistics analysis using graphical capabilities of R

Last update: Svozil Daniel (11.03.2026)

Registration requirements

Mathematics

Last update: Svozil Daniel (11.03.2026)