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
| Type of study | BachelorFollow-up Master |
|---|---|
| Language of instruction | Czech |
| Code | 546-0182/01 |
| Abbreviation | MAD |
| Course title | Methods of Data Processing and Analysis |
| Credits | 6 |
| Coordinating department | Department of Environmental Engineering |
| Course coordinator | doc. Mgr. Oldřich Motyka, Ph.D. |
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.
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.
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