Covariance

Creator
Creator
Seonglae Cho
Created
Created
2023 Mar 7 2:38
Editor
Edited
Edited
2025 Apr 27 13:5
Refs

Dispersion matrix

A measure of
Linearity
relationship

Variance
is for 1-dimensional data while
Covariance Matrix
is for vector data distribution

Definition

using definition
Pairwise Independence
Covariance
0, not reversal. But jointly normally distributed but uncorrelated, then they are indeed independent.
Since covariance is the expected value of the product of deviations from their respective means for X and Y, it can be interpreted as the dot product of two vectors. In particular,
Correlation
is like cosine, bounded between -1 and 1, as it's equivalent to dividing by the vector magnitudes.
 
 
 
 

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