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.