Postgraduate course

Course description

Objectives and Content

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 Outcomes

After the course, students:

  • will have practical skills in statistical modeling, model selection and hypothesis testing.
  • know when and how to apply linear models (lm), linear mixed effect models (lme), generalized linear models (glm) and generalized linear mixed effect models (glmm), survival analysis, and have a simple overview of prescription models.
  • must be able to make a graphical representation of results where, ideally, one is able to see raw data and how a chosen statistical model fits these.
  • must know why p-values ¿¿must be considered in the context of co-power variables.
  • must be able to master the programming language R.


Semester of Instruction



This course has a limited capacity, enrolment is based on application. The application deadline is Wednesday in week 33 for the autumn semester. Please see this page for more information. You will receive confirmation of whether you received a seat in Studentweb no later than Monday the week after the deadline.

It is compulsory to attend the first lecture/orientation meeting, or you risk losing your seat. If you are unable to attend the first lecture, you must contact the Study Section ( The time of the first lecture/orientation meeting can be found in the schedule on the course website or on Mitt UiB.

Required Previous Knowledge

Bachelor's degree in biology or equivalent. The course is limited to master programme students at the Department of Biological Sciences.


Recommended Previous Knowledge
BIO300A Academic Writing (can be taken parallel to BIO300B) or a basic course in Statistics.
Teaching and learning methods
  • 20 hours of lectures (theory).
  • 12 hours in computer lab (practical exercises).
  • Compulsory Assignments and Attendance
  • Attendance at lectures.
  • Computer lab assignments.

    Approved compulsory activities are valid for 3 semesters, including the semester of completion. Compulsory activities have to be passed in order to take the exam.

    Forms of Assessment
    Portfolio assessment
    Grading Scale
    The grading scale used is A to F. Grade A is the highest passing grade, grade F is a fail.
    Assessment Semester
    The exam is held in both semesters. In semesters with no teaching, the exam is held early in the semester.
    Course Evaluation
    Students will evaluate the course in accordance with the quality assurance system at UiB and the Department. You can find courseevaluations in the Quality Assurance Reports.