Monomial degree is fundamental to the theory of univariate and multivariate polynomials. Another statistical distribution approach to visualize multivariate data is parallel coordinates. These distributions are of great importance in the estimation of covariance matrices in multivariate statistics. Comparative study of physical-chemical variables of streams using multivariate analyses. It occurs frequently in likelihood-ratio tests in multivariate statistical analysis. The issue is particularly relevant in multivariate and regression problems. The covariance matrix is related to the moment of inertia tensor for multivariate distributions. Cumulative distribution functions are also used to specify the distribution of multivariate random variables. PCA is the simplest of the true eigenvector-based multivariate analyses. Algebraic geometry is a branch of mathematics, classically studying zeros of multivariate polynomials. Inversion formulas for multivariate distributions are available. Gaussian processes are thus useful as a powerful non-linear multivariate interpolation tool. The application of multivariate statistics is multivariate analysis. This decorrelation is related to principal components analysis for multivariate data. Originally, algebraic geometry was the study of common zeros of sets of multivariate polynomials.