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Registration information

Entry requirements: Bachelor's degree and English B or equivalent.

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

Information about application for exchange students

  • 15/03/2018 AS18 week 41-45, fulltime100%, day. Application code: SH-44065

Statistical Data Analysis in Infectious Disease Control

Course 7.5 credits

Epidemiology is the study of diseases in populations, and requires good knowledge of scientific data handling and basic statistical methodology. This course provides students with basic understanding and proficiency in descriptive statistics, including visualisation of data with graphical display. Students also learn experimental design and analysis in epidemiology. They work with statistical models for continuous and categorical response variables, with a focus on specific epidemiological methods. Teaching includes lectures, seminars and statistical computer assignments. The aim of this course is for students to be able to design, perform and analyse their own epidemiological experiments. Furthermore, they are able to read and critically evaluate the results and interpretations of the epidemiological literature.

The information below comes from the syllabus and is valid from: spring semester 2013

Course design

Lectures and seminars, but the main activity will be computer-aided statistical training.

Learning outcomes

Upon completion of the course, the student will have acqiured:

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


The examination will be planning, performing and reporting computer-based statistical analyses from previously unknown data sets.

Grading criteria will be distributed at course/module start.

Syllabus valid from spring semester 2013

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

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