hotelling-t2

pca: A Python Package for Principal Component Analysis.

Python implementation of the control charts used for process monitoring and anomaly detection

Computes Hotelling's T² confidence ellipses (2D) and ellipsoids (3D) for PCA or PLS score plots at a user-specified confidence level. Returns the boundary coordinates for plotting, along with the semi-axis lengths of each ellipse or ellipsoid.

A collection of simple parameter estimation and significance tests for the comparison of multivariate means and variation, covered in Chapters 4 and 5 of the book Multivariate Statistical Methods. A Primer. 5th edition.

Investigates how age, gender, BMI, and hypertension relate to pulse, blood pressure, and glucose using MANOVA, MANCOVA, and multivariate regression. Includes data cleaning, Box‑Cox transforms, model comparison, and post‑hoc analyses to identify key health predictors.

Análisis multivariado de 13.331 registros clínicos para identificar qué variables separan pacientes de alto y bajo riesgo cardiovascular. R, Hotelling, ACP, Kruskal-Wallis.

Hotellings T^2 Statistics Two sample Problem

Paquete tfmfdd y bloque de detección del TFM (Máster en IA, UNIR): detección de fallos en el proceso Tennessee Eastman con PCA y DPCA (T², SPE) bajo un protocolo de evaluación común.

A Python library for creating and analyzing confidence ellipses, including Hotelling's T-squared ellipses for multivariate statistical analysis and data visualization.

Analyze a dataset on muscular dystrophy and make statistical inferences