Statistics and Data Science I (19140)

- Ability to estimate generalized linear regression models with cross-sectional data, as well as to understand and explain the statistical principles underlying the estimations. - Ability to apply robustness tests to generalized linear regression model estimates. - Ability to interpret the parameters of a generalized linear regression, obtain predictions and evaluate the goodness of fit.

1. Generalized Linear Models (GLM) 1.1. Regression models for categorical dependent and independent variables 1.2. Other models for other types of qualitative dependent variables: binary, ordered, multinomial, counting, etc. 2. Generalized Linear Mixed Models (GLMM) 3. Real Life Examples

Training Activities: - Theoretical-practical classes 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.