This course aims at preparing students for thesis research and introduces data processing and statistical data analysis techniques. The objectives of the course are:
To learn and being able to apply the elementary statistical techniques for the analysis of continuous datasets
To understand and apply regression analysis techniques
To learn and apply multivariate techniques for the analysis of complex data sets consisting of more than two variables
To understand the main geostatistical techniques for the analysis and mapping of spatial data
To learn how to perform effective and reproducible data processing and (statistical) analysis using R
Content
Statistics play an integral role in today's research in physical and social sciences, as they are used to quantify results of studies. Statistical analyses lend credibility to theories and are essential for the general acceptance of scientific statements. This course focuses on widely used statistical and geostatistical techniques in earth science research.
The first part of the course will cover the theory and application of elementary statistics, including topics such as distributions, covariance, correlation, statistical tests, regression, statistical significance, and an introduction to geostatistics. Students' understanding of these subjects will be assessed through a midterm exam. In the final weeks of the course, students will apply their acquired knowledge in a data analysis project conducted in small groups. The project will be assessed based on a presentation and a written report.
The course will be taught using the statistical software R.
Specific skills that will be learned by the student are:
Problem-solving skills
Analytical/quantitative skills
Technical skills
