Difference between revisions of "Manuals/calci/poisson"

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*<math>m </math> is the mean  
 
*<math>m </math> is the mean  
 
*<math>cu</math> is the logical value like TRUE or FALSE.
 
*<math>cu</math> is the logical value like TRUE or FALSE.
 
  
 
==Description==
 
==Description==
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*The Poisson distribution is a discrete probability distribution for the counts of events that occur randomly in a given interval of time.
 
*The Poisson distribution is a discrete probability distribution for the counts of events that occur randomly in a given interval of time.
 
*It is  is used to model the number of events occurring within a given time interval.  
 
*It is  is used to model the number of events occurring within a given time interval.  
*In <math>POISSON(x,m,cu), x </math> is the number of events in a given interval of time, <math> m </math> is the Average numeric value and <math> cu </math> is the logical value.  
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*In <math>POISSON(x,m,cu)</math>, <math>x</math> is the number of events in a given interval of time, <math>m </math> is the Average Numeric value and <math>cu</math> is the logical value.  
*If it is TRUE, this function will give the cumulative Poisson probability with the number of random events between 0 and x(included).
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*If it is TRUE, this function will give the Cumulative Poisson Probability with the number of random events between <math>0</math> and <math>x</math>(included).
*If it is FALSE,this function will give the Poisson probability mass function with the number of events occuring will be exactly x.
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*If it is FALSE, this function will give the Poisson Probability Mass function with the number of events occurring will be exactly <math>x</math>.
*The <math>POISSON </math>probability mass function is: <math> f(x,\lambda)=\frac{\lambda^x.e^{-\lambda}}{x!}</math>,      x=0,1,2,...where <math> \lambda </math> is the shape parameter and <math>\lambda</math>>0. e is the base of the natural logarithm (e=2.718282).
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*The <math>POISSON</math>probability mass function is:
*The cumulative Poisson probability function is:<math>F(k,\lambda)=\sum_{k=0}^x \frac{e^{-\lambda} .\lambda^k}{k!}</math>.  
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<math> f(x,\lambda)=\frac{\lambda^x.e^{-\lambda}}{x!}</math>  
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<math>x=0,1,2...</math> where <math> \lambda </math> is the shape parameter and <math>\lambda > 0</math>. <math>e</math> is the base of the natural logarithm (e=2.718282).
 +
*The Cumulative Poisson Probability function is:
 +
<math>F(k,\lambda)=\sum_{k=0}^x \frac{e^{-\lambda} .\lambda^k}{k!}</math>.  
 
*This function will return the result as error when  
 
*This function will return the result as error when  
  1.x or m is nonnumeric.
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  1.<math>x</math> or <math>m</math> is non-numeric.
  2.x<0 or m<0.
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  2.<math>x<0</math> or <math>m<0</math>.
  
 
==Examples==
 
==Examples==
#POISSON(6,2,TRUE)=0.995466194
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#=POISSON(6,2,TRUE) = 0.995466194
#POISSON(6,2,FALSE)=0.012029803
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#=POISSON(6,2,FALSE) = 0.012029803
#POISSON(10.2,7,TRUE)=0.901479206
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#=POISSON(10.2,7,TRUE) = 0.901479206
#POISSON(10.2,7,FALSE)=0.070983269
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#=POISSON(10.2,7,FALSE) = 0.070983269
#POISSON(6,0,TRUE)=1
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#=POISSON(6,0,TRUE) = 1
 +
 
 +
==Related Videos==
 +
 
 +
{{#ev:youtube|JR-1ftUj__Y|280|center|POISSON}}
  
 
==See Also==
 
==See Also==
 
*[[Manuals/calci/EXPONDIST  | EXPONDIST ]]
 
*[[Manuals/calci/EXPONDIST  | EXPONDIST ]]
 
  
 
==References==
 
==References==
 +
[http://en.wikipedia.org/wiki/Poisson_distribution Poisson distribution ]

Latest revision as of 20:46, 19 June 2015

POISSON(x,m,cu)


  • is the number of events.
  • is the mean
  • is the logical value like TRUE or FALSE.

Description

  • This function gives the value of the Poisson distribution.
  • The Poisson distribution is a discrete probability distribution for the counts of events that occur randomly in a given interval of time.
  • It is is used to model the number of events occurring within a given time interval.
  • In , is the number of events in a given interval of time, is the Average Numeric value and is the logical value.
  • If it is TRUE, this function will give the Cumulative Poisson Probability with the number of random events between and (included).
  • If it is FALSE, this function will give the Poisson Probability Mass function with the number of events occurring will be exactly .
  • The probability mass function is:

where is the shape parameter and . is the base of the natural logarithm (e=2.718282).

  • The Cumulative Poisson Probability function is:

.

  • This function will return the result as error when
1. or  is non-numeric.
2. or .

Examples

  1. =POISSON(6,2,TRUE) = 0.995466194
  2. =POISSON(6,2,FALSE) = 0.012029803
  3. =POISSON(10.2,7,TRUE) = 0.901479206
  4. =POISSON(10.2,7,FALSE) = 0.070983269
  5. =POISSON(6,0,TRUE) = 1

Related Videos

POISSON

See Also

References

Poisson distribution