This course aims at preparing students for thesis research and introduces several statistical techniques for data analysis. The course starts with a refresher of elementary statistics, with subjects like characteristics of population distributions, covariance, correlation, t-test, F-test, analysis of variance (ANOVA), and linear regression. The course continues with two multivariate analysis techniques: multiple regression and Principle Component Analysis (PCA). The last part of the course is devoted to the statistics of spatial data (geostatistics). The spatial correlation between data points will be modeled with the variogram and kriging techniques will be used for spatial prediction and modeling. Using geostatistical principles, the setting up and evaluation of sampling and monitoring strategies will be presented. The software used during computer practicals is Microsoft EXCEL for the elementary statistics and ‘R’ for multivariate analysis and geostatistics.

For students from a different background then physical geography (e.g. social geography) there is a possibility to replace the geostatistical module with a module on non-parametric statistics or a different statistical topic that is more relevant.

By the end of the course, the student will acquired :
- Advanced knowledge of elementary statistics, multivariate statistics and geostatistics; 
- Ability to apply relevant (geo-)statistical modules of EXCEL and ‘R’ software packages;
- Insight into statistical data problems and the possible analytical tools to solve those problems