This analysis uses ANCOVA to test for differences in mathematics, reading, and writing scores by gender, controlling for parental education level as a covariate. The analysis also examines the assumptions of ANCOVA (linearity, homogeneity of slopes, normality of residuals and homogeneity of variances) to ensure the validity of the results.

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.