Difference between revisions of "Manuals/calci/poisson"

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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 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 occurring will be exactly <math>x</math>.
 
*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>, <math>x=0,1,2,...</math>where <math> \lambda </math> is the shape parameter and <math>\lambda</math>>0. <math>e</math> is the base of the natural logarithm (e=2.718282).
+
*The <math>POISSON</math>probability mass function is: <math> f(x,\lambda)=\frac{\lambda^x.e^{-\lambda}}{x!}</math>, <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>.  
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*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.<math>x</math> or <math>m</math> is non-numeric.
 
  1.<math>x</math> or <math>m</math> is non-numeric.

Revision as of 04:12, 22 January 2014

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

See Also

References

Poisson distribution