Introduction to Programming with R (19151) Basic Statistics (19152)

- Ability to test hypotheses using data and the most appropriate tools. - Ability to estimate linear regression models with cross-sectional data, as well as to understand and explain the statistical principles underlying the estimations. - Ability to interpret the parameters of a linear regression, obtain predictions and evaluate the goodness of fit.

1. Parametric and non parametric estimation 2. Advanced Inference 3. Introduction to advanced modelization 4. Empirical examples

Training Activities: - Theoretical-practical classes - Tutorials - Group work - Individual student work - Partial and final examinations Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group.