# Category:Covariance

Jump to navigation
Jump to search

This category contains results about Covariance.

Let $X$ and $Y$ be random variables.

Let $\mu_X = \expect X$ and $\mu_Y = \expect Y$, the expectations of $X$ and $Y$ respectively, exist and be finite.

Then the **covariance** of $X$ and $Y$ is defined by:

- $\cov {X, Y} = \expect {\paren {X - \mu_X} \paren {Y - \mu_Y} }$

where this expectation exists.

## Subcategories

This category has only the following subcategory.

### C

## Pages in category "Covariance"

The following 8 pages are in this category, out of 8 total.

### C

- Covariance as Expectation of Product minus Product of Expectations
- Covariance is Symmetric
- Covariance of Independent Random Variables is Zero
- Covariance of Linear Combination of Random Variables with Another
- Covariance of Multiples of Random Variables
- Covariance of Random Variable with Itself
- Covariance of Sums of Random Variables
- Covariance of Sums of Random Variables/Lemma