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Statistics[Distributions]

 Normal
 normal (Gaussian) distribution

 Calling Sequence Normal(mu, sigma) NormalDistribution(mu, sigma)

Parameters

 mu - distribution mean sigma - scale parameter

Description

 • The normal distribution is a continuous probability distribution with probability density function given by:

$f\left(t\right)=\frac{\sqrt{2}{ⅇ}^{-\frac{{\left(t-\mathrm{\mu }\right)}^{2}}{2{\mathrm{\sigma }}^{2}}}}{2\sqrt{\mathrm{\pi }}\mathrm{\sigma }}$

 subject to the following conditions:

$\mathrm{\mu }::\mathrm{real},0<\mathrm{\sigma }$

 • The normal variate Normal(mu,sigma) is related to the standardized variate Normal(0,1) by Normal(0,1) ~ (Normal(mu,sigma)-mu)/sigma.
 • Note that the Normal command is inert and should be used in combination with the RandomVariable command.

Examples

 > $\mathrm{with}\left(\mathrm{Statistics}\right):$
 > $X≔\mathrm{RandomVariable}\left(\mathrm{Normal}\left(\mathrm{\mu },\mathrm{\sigma }\right)\right):$
 > $\mathrm{PDF}\left(X,u\right)$
 $\frac{\sqrt{{2}}{}{{ⅇ}}^{{-}\frac{{\left({u}{-}{\mathrm{\mu }}\right)}^{{2}}}{{2}{}{{\mathrm{\sigma }}}^{{2}}}}}{{2}{}\sqrt{{\mathrm{\pi }}}{}{\mathrm{\sigma }}}$ (1)
 > $\mathrm{PDF}\left(X,0.5\right)$
 $\frac{{0.3989422802}{}{{ⅇ}}^{{-}\frac{{0.5000000000}{}{\left({0.5}{-}{1.}{}{\mathrm{\mu }}\right)}^{{2}}}{{{\mathrm{\sigma }}}^{{2}}}}}{{\mathrm{\sigma }}}$ (2)
 > $\mathrm{Mean}\left(X\right)$
 ${\mathrm{\mu }}$ (3)
 > $\mathrm{Variance}\left(X\right)$
 ${{\mathrm{\sigma }}}^{{2}}$ (4)

References

 Evans, Merran; Hastings, Nicholas; and Peacock, Brian.  Statistical Distributions. 3rd ed. Hoboken: Wiley, 2000.
 Johnson, Norman L.; Kotz, Samuel; and Balakrishnan, N. Continuous Univariate Distributions. 2nd ed. 2 vols. Hoboken: Wiley, 1995.
 Stuart, Alan, and Ord, Keith. Kendall's Advanced Theory of Statistics. 6th ed. London: Edward Arnold, 1998. Vol. 1: Distribution Theory.