Difference between revisions of "Manuals/calci/GAMMADIST"

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*The gamma function is defined by :
 
*The gamma function is defined by :
 
<math>Gamma(t) = \int\limits_{0}^{\infty}x^{t-1} e^{-x} dx</math>.  
 
<math>Gamma(t) = \int\limits_{0}^{\infty}x^{t-1} e^{-x} dx</math>.  
*And it is for all complex numbers except the negative integers and zero.  
+
*It is for all complex numbers except the negative integers and zero.  
*The probability density function of Gamma function using Shape, rate parameters is: f(x; α,ß)=[x^{α-1} e^-{x}]/ß^α Gamma(α), for x,α &ß>0, where e is the natural number(e=2.71828...),  α  is the number of occurrences of an event, and Gamma(α) is the Gamma function.
+
*The Probability Density Function of Gamma function using Shape, rate parameters is:
*The standard gamma probability density function is: f(x, α)=[x^{α-1} e^-x]/Gamma(α).  
+
<math> f(x; \alpha,\beta)=\frac{x^{\alpha-1} e^-{\frac {x}{\beta}}{\beta^{\alpha} Gamma(\alpha)}, for <math>x,\alpha & \beta > 0 </math>, where <math>e</math> is the natural number(e = 2.71828...),  <math>\alpha</math> is the number of occurrences of an event, and <math>Gamma(\alpha)</math> is the Gamma function.
*The  cumulative distribution function of Gamma is  F(x;α,ß)=[Gamma(in symbol V)(α, x/ß)]/Gamma(α), or F(x;α,ß)= e^-{x} Summation i=k to infinity 1/i! (x/ß)^i for any positive integer k.  
+
*The standard Gamma Probability Density function is:  
 +
<math>f(x, \alpha)=\frac{x^{\alpha-1} e^{-x}}{Gamma(\alpha)}</math>.  
 +
*The  Cumulative Distribution Function of Gamma is  <math>F(x;\alpha,\beta)=[\gamma(\alpha,\frac{x}{\beta}}{Gamma(\alpha)}</math>, or <math>F(x;\alpha,\beta)= e^-{\frac {x}{\beta}} \sum_{i=k}^{\infty}\frac{1}{i!}{\frac{x}{ß}}^i</math> for any positive integer <math>k</math>.  
 
*When alpha is a positive integer, then the distribution is called Erlang distribution.  
 
*When alpha is a positive integer, then the distribution is called Erlang distribution.  
 
*If the shape parameter α is held fixed, the resulting one-parameter family of distributions is a natural exponential family.
 
*If the shape parameter α is held fixed, the resulting one-parameter family of distributions is a natural exponential family.
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*This function shows the result as error when 1.Any one of the argument is non numeric
 
*This function shows the result as error when 1.Any one of the argument is non numeric
 
2. x<0, alpha<=0 or beta<=0
 
2. x<0, alpha<=0 or beta<=0
 +
 
==Examples==
 
==Examples==
  

Revision as of 00:30, 4 December 2013

GAMMADIST(x,alpha,beta,cu)


  • is the value of the distribution,
  • and are the value of the parameters
  • is the logical value like true or false.

Description

  • This function gives the value of the Gamma Distribution.
  • The Gamma Distribution can be used in a queuing models like, the amount of rainfall accumulated in a reservoir. *This distribution is the Continuous Probability Distribution with two parameters Failed to parse (syntax error): {\displaystyle \alpha & \beta} .
  • In GAMMADIST(x,alpha,beta,cu), is the value of the distribution, is called shape parameter and is the rate parameter of the distribution and is the logical value like TRUE or FALSE.
  • If is TRUE, then this function gives the Cumulative Distribution value and if is FALSE then it gives the Probability Density Function.
  • The gamma function is defined by :

.

  • It is for all complex numbers except the negative integers and zero.
  • The Probability Density Function of Gamma function using Shape, rate parameters is:

Failed to parse (syntax error): {\displaystyle f(x; \alpha,\beta)=\frac{x^{\alpha-1} e^-{\frac {x}{\beta}}{\beta^{\alpha} Gamma(\alpha)}, for <math>x,\alpha & \beta > 0 } , where is the natural number(e = 2.71828...), is the number of occurrences of an event, and is the Gamma function.

  • The standard Gamma Probability Density function is:

.

  • The Cumulative Distribution Function of Gamma is Failed to parse (syntax error): {\displaystyle F(x;\alpha,\beta)=[\gamma(\alpha,\frac{x}{\beta}}{Gamma(\alpha)}} , or Failed to parse (syntax error): {\displaystyle F(x;\alpha,\beta)= e^-{\frac {x}{\beta}} \sum_{i=k}^{\infty}\frac{1}{i!}{\frac{x}{ß}}^i} for any positive integer .
  • When alpha is a positive integer, then the distribution is called Erlang distribution.
  • If the shape parameter α is held fixed, the resulting one-parameter family of distributions is a natural exponential family.
  • For a positive integer n, when alpha = n/2, beta = 2, and cu= TRUE, GAMMADIST returns (1 - CHIDIST(x)) with n degrees of freedom.
  • This function shows the result as error when 1.Any one of the argument is non numeric

2. x<0, alpha<=0 or beta<=0

Examples

See Also

References

Bessel Function