Course: Statistical Modelling in Biomedicine
Course type: elective
Lecturer: Lara Lusa, Ph.D., Associate Professor
| Study programme and level | Study field | Academic year | Semester |
|---|---|---|---|
| Applied statistics, second level | All modules | 1st or 2nd | 1st or 2nd |
For the timeline see Curriculum.
Prerequisites:
- Valid inscription to start the course, positively evaluated home work for taking the exam.
Content (Syllabus outline):
General concepts:
- Formulation of models, estimation of parameters, interpretation
- Interaction, relaxing the linearity assumption
- Explained variation
- Overfitting
- Resampling, validation of models
- Using R in statistical modelling
Logistic regression:
- Fitting the model: maximum likelihood, point and interval estimation of the odds ratio, test statistic, residuals, goodness of fit, influential points
- Interpretation of the model
- Evaluating predictive value of the model
- ROC curves
Objectives and competences:
The aim is for students to learn about strategies of statistical modelling, and to evaluate and validate a model, using logistic regression as an example.
Intended learning outcomes:
Students will be able to fit and interpret models, which will adequately fit the data.
