Probability Statistical Inference Programming in R

SKIILS ACQUIRED: CG1 Ability to apply analysis techniques with the aim to adapt the information to real problems. CG2 Ability to identify the best stochastic model for each real problem, and to apply it for its analysis, design and solution. CE5 Apply advanced statistical foundation for the development and analysis of real problems that include the prediction of a response variable. KNOWLEDGE ACQUISITION 1) linear models 2) generalized linear models 3) generalized additive models

Regression Models 1) Linear regression: Estimation. Inference. Diagnostics. 2) Introduction to Generalized Linear Models: Exponetial family. Estimation. Inference. Diagnostics. 4) Logistic Regression, Multinomial, Ordinal, Poisson. 3) Generalized Additive Models: Smoothing Methods. Penalized Splines. Estimation. Smoothing parameter selection

Learning activities: Master classes Exercises Computer labs Projects Teaching methodologies: Presentations of the professor in class with computing and visual media, where the professor develops the mail concepts of the subject and provides bibliography supplementing the knowledge of students. Critical reading of texts recommended by the professor: manuals and/or academic papers, either for their posterior discussion in class, or for widening and consolidating the subject matter.