HEV9008 Statistical Rethinking. A course in Bayesian data analysis using R

    • Course code
      HEV9008
    • Number of credits
      7,5
    • Teaching semester
      2026 Spring
    • Language of instruction
      English
    • Campus
      Lillehammer
    • Required prerequisite knowledge

      Recommended prerequisite knowledge:
      Basic knowledge of the R programming language, including installation and the use of quarto or Rmarkdown documents. Knowledge of inferential statistics and regression modeling is recommended.

Course content
  • Bayesian data analysis in the context of being a tool to interrogate scientific models.
  • Advanced statistical models, including generalized linear models and hierarchical (or multilevel) models.
  • Generalizable workflows for precise scientific questions and inference.

Course evaluation — quality assurance system:
Normally, an evaluation of all courses must be carried out. The time/date and method are decided in consultation with student representatives. The course coordinator is responsible for ensuring that the evaluation is carried out.

Learning Outcome

Upon passing the course, students have achieved the following learning outcomes:

Knowledge

Students

  • have knowledge about workflows for Bayesian data analysis and model fitting
  • have knowledge about causal inference and the use of Directed Acyclic Graphs (DAG)
  • have knowledge about evaluation, comparison, and selection of statistical models
  • have knowledge about generalized linear models and hierarchical models
Skills

Students

  • can plan, evaluate, and draw inferences from statistical models built on specific research questions
  • can create and perform data analysis workflows in the R programming language
Required coursework

Presentation of solutions to practice problems (self-selected “hard” practice problems) in at least eight out of ten seminars.

Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Written assignment
Passed - not passed
Individual
  • All
100
Form of assessment

An individual written assignment will be graded Pass/Fail. The language in the report may be English or Norwegian.

Permitted examination support material:
All printed and written resources

Faculty
Faculty of Social and Health Sciences
Area of study
Helsefaglige utdanninger
Programme of study
Helse og velferd
Course level
Doctoral degree level (900-FU)