Implementation of Principal Component Analysis (PCA) and Exploratory Factor Analysis (FA) on Taiwan Air Quality Index data (2016–2024). This study reduces 17 correlated environmental variables into five principal components/factors explaining approximately 74% of total variance, revealing distinct pollution dimensions including particulate matter, combustion-related gases, ozone dynamics, sulfur emissions and spatial–geographical variation.

Contains a multivariate analysis of the Titanic dataset using R that focuses on the variables Age, SibSp, Parch and Fare. The analysis includes correlation, variance covariance and eigen value also eigen vector analysis as part of Multivariate Analysis course assignment.