MAOK4006 Biostatistics II
- Course codeMAOK4006
- Number of credits2,5
- Teaching semester2025 Autumn
- Language of instructionEnglish
- CampusEvenstad
- Required prerequisite knowledge
Biostatistics.
- Maximum Likelihood Estimation, information theory
- Extending the Generalized Linear Models (Overdispersion, Zero-Inflation, Generalized Additive Models)
- Generalized Linear (Mixed) Models, hierarchical models for longitudinal, clustered, or nested data
- Hierarchical (ie “mixed”) models
- General concepts of Bayesian inference
- Advantages and challenges of the Bayesian approach over the conventional Frequentist approach to data analysis
- Building complex hierarchical models by blocks
- Fitting, understanding, and interpreting regression models in the Bayesian framework
Learning Outcome
After successful completion of the course, the student will have the following knowledge, skills, and general competence.
The student
- Has a thorough understanding of (some of) the statistical modelling approaches most prominently used in Ecology research.
- Has a thorough knowledge of the advantages and drawbacks of the Bayesian and Frequentist approaches to data analysis.
The student
- Can plan and carry out advanced data analyses within the field of applied ecology.
- Can evaluate the advantages and drawbacks of different statistical methods for a given study.
- Can interpret advanced statistical methods stemming from hierarchical models.
The student
- Can carry out research with scholarly integrity.
- Can make an informed decision regarding which statistical approach is the most suitable to address a given scientific problem.
- Can participate in professional discussions relying on (bio)statistics literacy.
One-week intensive course together with a PhD-level course. Lectures and computer lab.
Vurderingsordning | Karakterskala | Gruppe/individuell | Varighet | Hjelpemidler | Andel | Kommentar |
---|---|---|---|---|---|---|
Written assignment | Passed - not passed | 100 |
One individual written report about an assigned or chosen problem in applied ecology, graded pass/fail.
Reading list
No reading list available for this course