Subject description
- GENERAL CONCEPTS:
- Formulation of models, estimation of parameters, interpretation
- Interaction, relaxing the linearity assumption
- Goodness-of-fit
- Explained variation
- Overfitting
- Resampling, validation of models
- Using R in statistical modelling
2. 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
The subject is taught in programs
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.
Teaching and learning methods
Lectures and lab exercises, where students will learn to apply theoretical knowledge to real data. R software will be used.
Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.
Expected study results
Knowledge and understanding:
Students will be able to fit and interpret models, which will adequately fit the data.
Basic sources and literature
Steyerberg E. W. (2009): Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer.
Harrell F. E. (2001): Regression Modeling Strategies. New York: Springer.
Everitt B., Rabe-Hesketh S. (2001): Analyzing Medical Data Using S-PLUS. New York: Springer.
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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana