546-0182/02 – Methods of Data Processing and Analysis (MAD)

Gurantor departmentDepartment of Environmental EngineeringCredits6
Subject guarantorMgr. Oldřich Motyka, Ph.D.Subject version guarantorMgr. Oldřich Motyka, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageEnglish
Year of introduction2022/2023Year of cancellation
Intended for the facultiesHGFIntended for study typesBachelor, Follow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
MOT127 Mgr. Oldřich Motyka, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 3+3
Part-time Credit and Examination 12+12

Subject aims expressed by acquired skills and competences

Students will learn to analyse and assess quantitative, semi-quantitative and qualitative data. They will be capable of describing and visualising the data using the descriptive statistics, identifying and assessing the data outliers and the distribution of the data. They will also be able to formulate the statistical hypothesis, to use appropriate statistical test and to correctly interpret the results. Moreover, they will gain proficiency in correlation and regression data analysis, as well as in multivariate and spatial analysis.

Teaching methods



Students will be accustomed to preparation of the sampling plan, collection, modification and assessment of the data. They will know to appropriately use and interpret statistical methods based on the specifics of the particular dataset. The data assessment will be performed in the R environment and the respective libraries.

Compulsory literature:

RUMSEY, Deborah, J. 2016, Statistics For Dummies, 2nd edition. Hoboken, NJ, USA: Wiley. ISBN 978-1-119-29352-1 BRUNSDON, Chris a Lex COMBER, 2019. An introduction to R for spatial analysis and mapping. Second edition. Los Angeles: SAGE. Spatial analytics and GIS (Sage). ISBN 978-152-6428-509 HOTHORN, Torsten a Brian EVERITT, c2014. A handbook of statistical analyses using R. 3rd ed. Boca Raton: CRC Press. Spatial analytics and GIS (Sage). ISBN 978-148-2204-582 HUSSON, Francois, Sebastian LE a Jérôme PAGÈS, 2017. Exploratory Multivariate Analysis by Example Using R. 2nd ed. Boca Raton: Chapman and Hall/CRC. ISBN 9780-429-225-437

Recommended literature:

VENABLES, William M. & SMITH, David M., 2009. An Introduction to R. 2nd edition, Network Theory Ltd. ISBN 978-0954612085 MAINDONALD, J. H. a John BRAUN, 2010. Data analysis and graphics using R: an example-based approach. Third edition. Cambridge: Cambridge University Press. Cambridge series on statistical and probabilistic mathematics. ISBN 978-113-9194-648 WICKHAM, Hadley, 2016. Ggplot2: elegant graphics for data analysis. Second edition. [Cham]: Springer. Use R!. ISBN 978-3-319-24277-4 ZELTERMAN, Daniel, 2015. Applied multivariate statistics with R. Cham: Springer. Statistics for biology and health (Springer). ISBN 978-3-319-14093-3

Way of continuous check of knowledge in the course of semester

Students provide their own dataset, discussion of its analysis is a part of the exam. The exam consists of both written and oral part.


Other requirements

Active participation in seminars.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

1. Sampling plan preparation, management and saving of the data 2. Types of data - quantitative and qualitative, description, characteristcs of variability, visualisation, outlier identification. 3. Hypothesis testing - null and alternative hypothesis, type I. and II. errors, statistical test and its power, p-value. 4. Introduction to the R environment and R studio interface, projects creation, data import, graphic outputs. 5. One-sample and two-sample tests - parametric and non-parametric methods, categorical data analysis - chi-squared and Fisher test. 6. Analysis of variance (ANOVA) - assessment of variances and normality, Kruskal-Wallis test - non-parametric alternative to ANOVA. 7. Correlation analysis - Pearson and Spearman correlation coefficent, data similarity measures (coefficients of similarity, correlation, covariance). 8. Regression analysis - linear regression, linear model assumptions, regression model parametres, coefficient of determination, statistical tests. 9. Regression analysis - polynomial regression, statistical tests, residual analyses. 10. Multiple linear regression - types of variable interactions, multicolinearity, missing data problems, applications. 11. Spatial data, autocorrelation, sampling, analysis, local and global statistics. 12. Multivariate analysis of data - principles, assumptions and data modification prior to the analysis. 13. Exploratory analysis, Principle component analysis (PCA), Multiple correspondence analysis (MCA), Factorial analysis of mixed data (FAMD), cluster analysis.

Conditions for subject completion

Full-time form (validity from: 2022/2023 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit  
        Examination Examination 100  51 3
Mandatory attendence participation: - mandatory attendace in seminars - proving ability to analyze data on the example of data (with a commentary) produced by a student - in-person exam - written and oral part

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Conditions for subject completion and attendance at the exercises within ISP: - seminar work, topic is going to be assigned following consultation with the student - proving ability to analyze data on the example of data (with a commentary) produced by a student - in-person exam - written and oral part

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0532A330044) Geoinformatics P English Ostrava 1 Compulsory study plan
2023/2024 (N0532A330044) Geoinformatics P English Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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