Normal Distribution: Difference between revisions
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f(''x'') {{=}} (<sup>1</sup>⁄<sub>σ {{math|{{radical|2π}}}}</sub>) ''e''<sup>-<sup>(x-µ)²</sup>⁄<sub>2σ²</sub></sup> | f(''x'') {{=}} (<sup>1</sup>⁄<sub>σ {{math|{{radical|2π}}}}</sub>) ''e''<sup>-<sup>(x-µ) ²</sup>⁄<sub>2σ ²</sub></sup> | ||
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*The parameter ''μ'' in this formula is the ''mean'' or ''expectation'' of the distribution (and also its median and mode). | |||
*The parameter ''σ'' is its standard deviation; its variance is therefore σ'<sup> 2</sup>. A random variable with a Gaussian distribution is said to be '''normally distributed''' and is called a '''normal deviate'''. | |||
The parameter ''μ'' in this formula is the ''mean'' or ''expectation'' of the distribution (and also its median and mode). The parameter ''σ'' is its standard deviation; its variance is therefore σ'<sup> 2</sup>. A random variable with a Gaussian distribution is said to be '''normally distributed''' and is called a '''normal deviate'''. | |||
[[File:Standard Normal.png]] | [[File:Standard Normal.png]] | ||
Revision as of 15:37, 3 August 2013
The normal distribution is
f(x) = (1⁄σ √2π) e-(x-µ) ²⁄2σ ²
- The parameter μ in this formula is the mean or expectation of the distribution (and also its median and mode).
- The parameter σ is its standard deviation; its variance is therefore σ' 2. A random variable with a Gaussian distribution is said to be normally distributed and is called a normal deviate.
