Advanced Multivariate Statistics project

Simulations for an article about extreme quantile region estimation

Classification of the states of Mexico according to their level of electoral complexity.

Spatial statistical prediction of solar-induced chlorophyll fluorescence (SIF) from multivariate OCO-2 data

This repository contains learning materials and assignments for the subject MAST90138 Multivariate Statistics for Data Science.

Explore multivariate statistics through hands-on university projects. Each project delves into real-world datasets, applying statistical techniques like ANOVA, two-factor analysis, and binary logistic regression. Understand data analysis, interpretation, and modeling with R.

Classification of the federal electoral districts of Mexico according to their level of electoral complexity.

Multivariate data analysis using geometric algorithms made easy!

Simple demonstration of basic multivariate statistics.

US University admissions data has been analysed using multivariate techniques

Predict the activity of smartphone users (walking, sitting, lying) based on high dimensional sensor data.

Computational Statistic class coding assignment

In our second semester in ISI, Delhi, we did this project titled "Multivariate Analysis on Milk Transportation Data". The aim of the project was to implement Multivariate Statistical tools to extract insights from Milk Transportation Data.

Addiscore – An indicator to simplify the choice of prepared meals

exercises in multivariate statistics

Programa Computarizado, elaborado en el lenguaje de programacion MATLAB, referente al Trabajo Especial de Grado para optar al Título de Ingeniero Industrial, en la Facultad de Ingeniería de la Universidad de Carabobo, año 2015; la cual fue reconocida con mención honorífica.

Multivariate statistical hypothesis testing in Julia

This project includes framework for creating multivariate process monitoring control charts, identifying out-of-control points and removing the out-of-control data points (All the iterations are identified automatically by a loop and removed). The project also gives reader an idea of the approach followed for dimension reduction using PCA.