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Registration information

Entry requirements: The equivalent of English B and a Bachelor's degree including 90 credits in Environmental Science, Bioscience, Sociology, International Health, Media Communcation Studies or the equivalent

Selection: On the basis of previous university credits. Between 30 and 285 credits may be taken into account.

  • Closed... AS18 week 41-45, fulltime100%, day. Application code: SH-44155
  • 15/03/2019 AS19 week 41-45, fulltime100%, day. Application code: SH-44024


Course 7,5 credits

This course provides students with basic understanding and proficiency in descriptive statistics, including the visualisation of data using different graphical display techniques. Students also learn experimental design and analysis in environmental science and epidemiology. We work with statistical models for continuous and categorical response variables, both in general terms and with a special focus on specific environmental and epidemiological methods. Teaching includes lectures, seminars and statistical computer assignments. The aim of this course is to provide students with tools so that they are able to design, perform and analyse their own experiments. Furthermore, they are able to read and critically evaluate the results and interpretations of the scientific literature.

The information below comes from the syllabus and is valid from: autumn semester 2018

Course design

Lectures, seminars and computer-aided statistical training

Learning outcomes

Upon completion of the course, the student is able to:

  • discern and discuss basic statistical concepts: independent observations, ordinary least squares, maximum likelihood, generalised linear models, analysis of variance/deviance and error distributions
  • explain the importance of independent observations in sampling data for statistical analysis
  • describe the ideas of significance testing by comparing variance explained to random variance
  • independently report standard descriptive measurements and construct and interpret generalised linear models with normal and binomial error distributions
  • explain the limitations of statistical results; are they globally valid or only valid for the specific experimental set-ups, to which population do they refer, how could confounding (not measured) variables explain the results.


The examination comprises planning, performing and reporting computer-based statistical analyses from previously unknown data sets. Absence from seminars must be complemented by alternative assignments. Absence from more than 50% of the seminars requires that the student takes the course again. Complementary tasks must be handed in within one year of course completion.

Grading criteria will be distributed at course/module start.

Syllabus valid from autumn semester 2018

The above information and syllabus are based on the most recently validated decision. Any previous versions are available here.


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