Introduction to the Use of R

• Introduction to R syntax and presentation of the main commands.
• Exploratory data analysis.
• Hypothesis test examples: Student’s t test, Two independent samples test, Paired data test, Chi-square test.

Data Preprocessing

• Management of missing data.
• Principal component analysis.
• Cluster Analysis: Hierarchical Clustering Algorithms, Non-Hierarchical Clustering Algorithms (K-Means).

The Selection of Model Variables

• Classical approaches: best subset selection, forward selection, backward elimination, stepwise regression.
• Modern approaches: shrinkage methods (ridge, lasso), boosting.

Evaluation of the Model

• Information criteria.
• Cross-validation.

Analysis of the Variance

All lessons will be accompanied by practical examples carried out with the R software.