Ph.D. -course

Course description

Course content

Access to the course requires admission to PhD programme at The Faculty of Mathematics and Natural Sciences. The course is the equivalent of BIO300B and have following compulsory activity in addition:

Objective and Contents:

The course provides a practical introduction to statistical modeling and graphical presentation of data through the use of the statistical programming software R. The main focus is on univariate models, but a simple overview of ordination is also given. The course provides knowledge about when and how to apply different statistical models depending on the type of design and type of data.

Learning Outcome

After the course, students:

  • will have practical skills in statistical modeling, model selection and hypothesis testing.
  • be able to select, use and interpret regression models and their diagnostics (linear models, linear mixed effect models, generalized linear models and generalized linear mixed effect models), and have a simple overview of survival analysis.
  • must be able to select and make a publication quality graphical representation of results where, ideally, one can see raw data and
  • how a chosen statistical model fits these.
  • must know why p-values must be considered in the context of effect size.
  • must be able to master the programming language R.

Compulsory Assignments and Attendance

  • 20 hours of lectures (theory).
  • 12 hours in computer lab (practical exercises).
  • Produce report based on analysis of own data.

Form of Assessment

Portfolio assessment

PhD students will analyse some of their own data (or similar archived data) using appropriate numerical methods covered in the course and write a fully reproducible fragment of a manuscript (numerical methods and results) with tables and figures as required