Multivariate analysis involves examining multiple variables to understand relationships and patterns. It is crucial for understanding how several factors interact simultaneously.
Correlation Coefficient $r$: $r$ measures the strength and direction of the linear relationship between two variables.
Multicollinearity: A situation in which two or more independent variables in a regression model are highly linearly related.
Principal Component Analysis (PCA): A statistical procedure that uses orthogonal transformation to convert possibly correlated variables into uncorrelated principal components.