Probability Statistical Inference Programming in R

The main course objectives are: 1. Understand and apply linear regression for estimation, inference, and diagnostics. 2. Introduce the concept of Generalized Linear Models (GLMs) with a focus on the Exponential family, and develop skills in estimation, inference, and diagnostics for GLMs. 3. Explore logistic regression, along with Multinomial, Ordinal, and Poisson models. 4. Gain knowledge of Generalized Additive Models (GAMs) and become proficient in smoothing methods, penalized splines, estimation, and the selection of smoothing parameters.

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.