Difference between revisions of "Manuals/calci/LOGNORMDIST"

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*Let the Normal Distribution function <math>x</math> and its Mean= <math>μ</math>, Standard Deviation = <math>σ</math>
 
*Let the Normal Distribution function <math>x</math> and its Mean= <math>μ</math>, Standard Deviation = <math>σ</math>
 
*Then the  lognormal cumulative distribution is calculated by:<math>F(x,μ,σ)=\frac{1}{2} \left[1+ erf \left (\frac{ln(x)-μ)}{σ\sqrt{2}}\right)\right ]= φ\left[\frac{ln(x)-μ}{σ}\right ]</math>
 
*Then the  lognormal cumulative distribution is calculated by:<math>F(x,μ,σ)=\frac{1}{2} \left[1+ erf \left (\frac{ln(x)-μ)}{σ\sqrt{2}}\right)\right ]= φ\left[\frac{ln(x)-μ}{σ}\right ]</math>
where <math>erf</math> is the  error function( the error function (also called the Gauss error function) is a special function  of sigmoid shape which occurs in probability, statistics and partial differential equations)
+
where <math>erf</math> is the  error function,. The error function (also called the Gauss error function) is a special function  of sigmoid shape which occurs in probability, statistics and partial differential equations.
*And <math>φ</math> is the Cumulative Distribution function of the Standard Normal distribution.  
+
*And <math>\phi</math> is the Cumulative Distribution function of the Standard Normal distribution.  
 
*This function will give the result as error when
 
*This function will give the result as error when
 
*1. Any one of the argument is non-numeric.
 
*1. Any one of the argument is non-numeric.
*2.suppose <math> x \le 0 </math> or <math> sd \le 0</math>
+
*2. Suppose <math> x \le 0 </math> or <math> sd \le 0</math>
  
 
==Examples==
 
==Examples==

Revision as of 23:59, 30 December 2013

LOGNORMDIST(x,m,sd)


  • is the value , is the mean of ,
  • And is the standard deviation of .

Description

  • This function gives the value of the cumulative log normal distribution.
  • This distribution is the continuous probability distribution.
  • Lognomal distribution is also called Galton's distribution.
  • A random variable which is log-normally distributed takes only positive real values.
  • Suppose is Normally Distributed function, then also Normally Distributed
  • also Normally Distributed.
  • Let the Normal Distribution function and its Mean= Failed to parse (syntax error): {\displaystyle μ} , Standard Deviation = Failed to parse (syntax error): {\displaystyle σ}
  • Then the lognormal cumulative distribution is calculated by:Failed to parse (syntax error): {\displaystyle F(x,μ,σ)=\frac{1}{2} \left[1+ erf \left (\frac{ln(x)-μ)}{σ\sqrt{2}}\right)\right ]= φ\left[\frac{ln(x)-μ}{σ}\right ]}

where is the error function,. The error function (also called the Gauss error function) is a special function of sigmoid shape which occurs in probability, statistics and partial differential equations.

  • And is the Cumulative Distribution function of the Standard Normal distribution.
  • This function will give the result as error when
  • 1. Any one of the argument is non-numeric.
  • 2. Suppose or

Examples

  1. LOGNORMDIST(2,5.4,2.76)=0.044061652
  2. LOGNORMDIST(10,24.05,12.95)=0.046543186
  3. LOGNORMDIST(50,87.0036,42.9784)=0.026597569
  4. LOGNORMDIST(-10,5,2)=NAN

See Also

References

Log-normal distribution