HEV9008 Statistical Rethinking. A course in Bayesian data analysis using R
- Course codeHEV9008
- Number of credits7,5
- Teaching semester2026 Spring
- Language of instructionEnglish
- CampusLillehammer
- 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.
- 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:
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
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
Presentation of solutions to practice problems (self-selected “hard” practice problems) in at least eight out of ten seminars.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written assignment | Passed - not passed | Individual |
| 100 |
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