Difference between revisions of "Variance"
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==Definition== | ==Definition== | ||
| − | Given | + | Given an [[Integral (measure theory)|integrable]] [[Random variable|random variable]] {{M|X}} we define the '''variance''' of {{M|X}} as follows: |
* <math>\text{Var}(X)=\mathbb{E}\left[(X-\mu)^2\right]</math> where {{M|\mu}} is the ''mean'' or [[Expected value|expected value]] of {{M|X}} | * <math>\text{Var}(X)=\mathbb{E}\left[(X-\mu)^2\right]</math> where {{M|\mu}} is the ''mean'' or [[Expected value|expected value]] of {{M|X}} | ||
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==Other forms== | ==Other forms== | ||
{{Begin Theorem}} | {{Begin Theorem}} | ||
| − | Theorem: <math>\text{Var}(X)=\mathbb{E}[X^2] | + | Theorem: <math>\text{Var}(X)=\mathbb{E}[X^2]-(\mathbb{E}[X])^2</math> |
{{Begin Proof}} | {{Begin Proof}} | ||
* <math>\text{Var}(X)=\mathbb{E}\left[(X-\mu)^2\right]</math> | * <math>\text{Var}(X)=\mathbb{E}\left[(X-\mu)^2\right]</math> | ||
Latest revision as of 19:45, 24 July 2016
Definition
Given an integrable random variable X we define the variance of X as follows:
- Var(X)=E[(X−μ)2]where μ is the mean or expected value of X
Other forms
[Expand]
Theorem: Var(X)=E[X2]−(E[X])2
