This learning unit (LU) aims to provide a strong background in statistical modelling methods using linear models and generalized linear models.
Upon the conclusion of this LU the student should:
- Understand the basic concepts of the theory of linear models, with the perspective to its application in practical contexts;
- Identify advantages and disadvantages in application of a model in a given context;
- Mastering specific software which will come in useful in the understanding and application of statistical modelling techniques;
- Dealing with problems involving statistical modelling in different contexts.
The syllabus of this LU consists of the following points:
1. Classic Linear Models: Linear Regression (simple and multiple)
2. Generalized Linear Models: Logistic Regression, Log linear Models, and Nonlinear Regression.
3. Mixed Models.
4. Nonparametric Regression.
5. Regression Trees.
6. Introduction to Joint Regression Analysis
7. Regularization methodologies
• Faraway: Extending the Linear Model with R. Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall, 2006;
• Kutner, Nachtsheim, Neter: Applied Linear Statistical Models, 5th Ed., McGraw-Hill, 2004;
• Draper, Smith: Applied Regression Analysis, 3th. Ed., Wiley, 1998.
Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.