be familiar with graphical data exploration techniques
be able to perform analysis of variance and understand underlying assumptions
Be familiar with linear models and model selection
be familiar with parametric and non-parametric hypothesis testing
have acquired basic skills in statistical programming in the R language
This course will introduce the students to working with real-life data common and statistical methods used in evolution and ecology. The course focuses on developing robust and repeatable workflows for data wrangling, data visualisation and statistical analysis. The students will get an introduction to basic linear models, mode selection and more advanced methods such as Generalised Linear Models and Mixed Models. Throughout the course students will use the programming language R, the RStudio package, and Git.
Classes will be a mixture of lectures and practicals designed to build required skills for future modules and to perform analyses frequently encountered in the biological literature. Instruction will be given in English. More information will be given in Canvas at the start of the course. The estimated student workload in this course is 135 hours.
Approval of reports from all computer laboratory exercises. More information about the reports and exercises is given in Canvas by the start of the course.
Assessment methods and criteria
Portfolio. Graded assessment A-F. More information will be given in Canvas at the start of the course.
The study programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.