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
Methods of Data Processing and Analysis
| Language of instruction | angličtina, čeština |
|---|---|
| Code | 546-0182 |
| Abbreviation | MAD |
| Course title | Methods of Data Processing and Analysis |
| Coordinating department | Department of Environmental Engineering |
| Course coordinator | doc. Mgr. Oldřich Motyka, Ph.D. |
Summary
Professional knowledge
Upon successful completion of the course, the student:
explains the principles of sampling plan design, data collection, preparation, quality control and assessment;
characterises the principal assumptions, applications and limitations of statistical methods used for different types of data;
explains the principles of descriptive statistics, statistical hypothesis testing, correlation and regression analysis, multivariate analysis and spatial data analysis;
describes the possibilities of using the R environment and relevant packages for data processing, analysis and visualisation.
Professional skills
Upon successful completion of the course, the student:
designs a sampling plan appropriate to the objective of the study and the nature of the data to be collected;
prepares, modifies and validates quantitative, semi-quantitative and qualitative data for statistical analysis;
identifies and verifies erroneous and outlying values and assesses the distribution of the analysed data;
formulates statistical hypotheses and selects and applies appropriate statistical methods with respect to the nature of the data;
performs descriptive, correlation, regression, multivariate and spatial data analyses in the R environment;
appropriately visualises data and correctly interprets the results of the analyses.
General competences
Upon successful completion of the course, the student:
makes independent and responsible decisions concerning the collection, preparation and statistical analysis of data;
provides professional justification for the selected analytical approach with respect to the objective of the analysis, the properties of the data and the assumptions of the methods applied;
critically evaluates the quality, uncertainty and limitations of the results and assumes responsibility for the methodological validity of the conclusions drawn;
clearly presents and defends the results of statistical analyses to both specialist and non-specialist audiences.
Upon successful completion of the course, the student:
explains the principles of sampling plan design, data collection, preparation, quality control and assessment;
characterises the principal assumptions, applications and limitations of statistical methods used for different types of data;
explains the principles of descriptive statistics, statistical hypothesis testing, correlation and regression analysis, multivariate analysis and spatial data analysis;
describes the possibilities of using the R environment and relevant packages for data processing, analysis and visualisation.
Professional skills
Upon successful completion of the course, the student:
designs a sampling plan appropriate to the objective of the study and the nature of the data to be collected;
prepares, modifies and validates quantitative, semi-quantitative and qualitative data for statistical analysis;
identifies and verifies erroneous and outlying values and assesses the distribution of the analysed data;
formulates statistical hypotheses and selects and applies appropriate statistical methods with respect to the nature of the data;
performs descriptive, correlation, regression, multivariate and spatial data analyses in the R environment;
appropriately visualises data and correctly interprets the results of the analyses.
General competences
Upon successful completion of the course, the student:
makes independent and responsible decisions concerning the collection, preparation and statistical analysis of data;
provides professional justification for the selected analytical approach with respect to the objective of the analysis, the properties of the data and the assumptions of the methods applied;
critically evaluates the quality, uncertainty and limitations of the results and assumes responsibility for the methodological validity of the conclusions drawn;
clearly presents and defends the results of statistical analyses to both specialist and non-specialist audiences.
Literature
Advised 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
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