Category:Covariance
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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.
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