What are the differences between the courses?

Getting started with R (Sophie Karrenberg, IEG)

This course enables students to start using the code-based statistical software R independently. This course is particularly suitable for PhD students without prior experience in programming and also for students who wish to transition to R from another statistical program. Basic education in general statistics is desirable but not required. The main focus of the course is understanding how to work with R and RStudio efficiently. The course will cover: data loading and manipulation, R command structure, common basic statistics (for example ANOVA and regression), some graphs, and a brief introduction to programming structures. A large part of the course will be spent on practice sessions with help available.

Modern statistics in natural science (Göran Arnqvist, IEG)

This course gives a wide overview of the statistical "tool- box" used to analyze experimental data in natural sciences and aims at providing an understanding of the philosophy and reasoning behind hypothesis testing and statistical inferences. We cover experimental design and discuss and implement a wide variety of statistical modelling strategies and techniques, with some emphasis on linear modelling such as analyses of variance and deviance. The course is primarily aimed at students in biology and life sciences, but the course content is relevant also for students from a wide range of other domains in natural sciences.

Statistical methods in physics and engineering (Karin Schönning, Physics)

The course target PhD students in the physical sciences, e.g. physics, astronomy, engineering and earth sciences where mathematical modelling of problems and phenomena is central. The parameters of these mathematical models are central for describing concepts and making predictions, hence the course has a strong focus on estimating these parameters. Many problems in modern physical sciences are non-linear and therefore require numerical solutions. Non-linear problems constitute an important part of this course, and we offer a toolkit to approach such problems.