Study Programmes

Content (Syllabus outline)

1. Introduction 
1.1 Brief overview of the basics of statistics 
1.2 Basics of programming in R using Rcmdr and RStudio 
1.3 Testing hypotheses about the mean (one sample, two samples) 
2.  Linear Models
2.1 Simple regression model
2.2 Diagnostics of the linear model
2.3 Polynomial regression
2.4 Multiple Regression Model
2.4.1 More than one predictor variable and their interaction 
2.4.2 Categorical predictor variables (one-way ANOVA) 
2.4.3 Categorical and numerical predictor variables and their interaction
2.4.4 General model diagnostic
2.5 Inferential analysis of model parameters and interpretation of statistical modelling results 

3. Time series
3.1 Introduction 
3.2 Time series decomposition (trend, seasonal, random component) 
3.3 Basic time series modelling (AR, MA, ARMA models) 
3.4 General linear model considering autocorrelation of errors

4. Multivariate statistical methods
4.1 Clustering (hierarchical clustering) 
4.2 Principal Component Analysis (PCA) 

Prerequisites

Prerequisites for performing study obligations:
- 100% attendance at laboratory exercises
- Regular study work (exercise homeworks)