1.Covariance matrix
The covariance matrix is a matrix that represents the covariance value between a given pair of elements in a random vector. The covariance matrix may also be calleddispersion matrix or variance-covariance matrix. This is because the variance of each element is expressed along the main diagonal of the matrix.
The covariance matrix is always a square matrix. Furthermore, it is positive semidefinite and symmetric. This matrix is useful in stochastic modeling and principal component analysis. In this article, we will learn about the variance-covariance matrix, its formula, examples, and various important properties related to it.
. Variance is a measure of dispersion and can be defined as the distribution of data relative to the mean of a given data set. Covariance is calculated between two variables and is a measure of how both variables vary together.