🏪Covariance Matrix

A covariance matrix is a symmetric square matrix that summarizes the variances and covariances between variables in a dataset.

What is its value?

It provides a measure of how two variables change together.

Example

If you have a dataset with n variables, the covariance matrix will be an n x n matrix. The element in the i-th row and j-th column represents the covariance between variables i and j.

The diagonal elements of the covariance matrix represent the variances of the individual variables, while the off-diagonal elements represent the covariances between pairs of variables.

Formula

Mathematically, for a dataset with variables X₁, X₂, ..., Xₙ, the covariance between variables i and j is computed as:

$$cov(Xᵢ, Xⱼ) = E[(Xᵢ - μᵢ)(Xⱼ - μⱼ)]$$

Where:

• E[ ] denotes the expected value (or average)

• μᵢ and μⱼ represent the means of variables Xᵢ and Xⱼ, respectively.

The important properties of the covariance matrix

1. Symmetry: The covariance matrix is always symmetric because cov(Xᵢ, Xⱼ) = cov(Xⱼ, Xᵢ).

2. Diagonal elements: The diagonal elements of the covariance matrix represent the variances of the individual variables: cov(Xᵢ, Xᵢ) = var(Xᵢ).

3. Positive semi-definiteness: The covariance matrix is positive semi-definite, which means that its eigenvalues are non-negative.

Summary

The covariance matrix is used to capture the relationships and dependencies between variables in multivariate data.