Linear models

Mixed models

Regression

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.

E-learning

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.