Difference between revisions of "Manuals/calci/poisson"
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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 | + | *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 | + | *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 | + | *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> | + | *The <math>POISSON</math>probability mass function is: |
− | *The | + | <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>. | ||
*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. |
2.<math>x<0</math> or <math>m<0</math>. | 2.<math>x<0</math> or <math>m<0</math>. | ||
==Examples== | ==Examples== | ||
− | #POISSON(6,2,TRUE)=0.995466194 | + | #=POISSON(6,2,TRUE) = 0.995466194 |
− | #POISSON(6,2,FALSE)=0.012029803 | + | #=POISSON(6,2,FALSE) = 0.012029803 |
− | #POISSON(10.2,7,TRUE)=0.901479206 | + | #=POISSON(10.2,7,TRUE) = 0.901479206 |
− | #POISSON(10.2,7,FALSE)=0.070983269 | + | #=POISSON(10.2,7,FALSE) = 0.070983269 |
− | #POISSON(6,0,TRUE)=1 | + | #=POISSON(6,0,TRUE) = 1 |
+ | |||
+ | ==Related Videos== | ||
+ | |||
+ | {{#ev:youtube|JR-1ftUj__Y|280|center|POISSON}} | ||
==See Also== | ==See Also== |
Latest revision as of 19:46, 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
- =POISSON(6,2,TRUE) = 0.995466194
- =POISSON(6,2,FALSE) = 0.012029803
- =POISSON(10.2,7,TRUE) = 0.901479206
- =POISSON(10.2,7,FALSE) = 0.070983269
- =POISSON(6,0,TRUE) = 1