In probability theory and statistics, the multivariate normal distribution, multivariate distribution or joint normal distribution is a generalization of the one dimensional normal distribution to higher dimension. The multivariate normal is often used to derives, at least approximately any set of correlated real-valued random variable each of which clusters around a mean value, straightforwardly assessing the joint likelihood dispersion between all optional and essential factors as opposed to approximating the joint likelihood through connecting the individual probabilities. The proposed thought is propelled by some central issues: (1) there are appropriate strategies for displaying the joint relations among factors including nonparametric methodologies and (2) one trait of optional information is comprehensiveness thus the joint dissemination of auxiliary information can be demonstrated dependably. By straightforwardly demonstrating the joint dispersion, information excess among factors is represented straightforwardly. Confounded weight alignment is not, at this point required. Additionally, non-Gaussian highlights between factors can be caught by applying non-parametric procedure.