Temas Subtemas
Multivariate data description and inference. Exploratory analysis of multivariate data.
Statistical inference of multivariate data.
Introduction to R statistical software.
Elements of unsupervised learning.
 Principal component analysis (PCA).
Unsupervised clustering or classification methods.
Elements of supervised learning Multivariate linear regression models.
Selection, regularization of linear models and dimension reduction methods. Ridge regression.
Least absolute shrinkage and selection operator (LASSO).
Principal component regression.
Partial least squares (PLS) regression.
Classification methods.
 Introducción.
Métodos de clasificación supervisada.
Statistical Process Control (SPC).
 Control charts of variables and attributes.
Hotelling's T2 control chart.
Regression adjustment.
Interpretation of out-of-control signals.
Case studies to be solved using R statistical software.
 Case studies in the field of naval and maritime engineering.