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.  
+
*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).
+
*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.
+
*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 POSSON 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).
+
*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>.  
+
<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.x or m is nonnumeric.
+
  1.<math>x</math> or <math>m</math> is non-numeric.
2.x<0 or m<0.
+
2.<math>x<0</math> or <math>m<0</math>.
 
 
where,
 
 
 
'''X''' - are represents number of events.
 
 
 
'''Mean '''- is the expected numeric values.
 
 
 
'''Cumulative '''- returned the logical value that determines the form of the probability distribution.
 
 
 
If '''TRUE''' - returnd the cumulative Poisson probability that the number of random events occuring will be between 0 and X.
 
 
 
If '''FALSE''' -returns the Poisson probability mass function that the number of events occuring will be exactly X.
 
 
 
</div>
 
----
 
<div id="1SpaceContent" class="zcontent" align="left">
 
 
 
Returns the Poisson distribution.
 
 
 
'''Formula''' :-
 
 
 
If Cumulative =FALSE
 
 
 
POISSON = (e<sup>-λ <sub>λ</sub>× <sub>) / x!</sub></sup>
 
 
 
If Cumulative = TRUE
 
 
 
POISSON = Σ<font size="3">(e<sup>-λ <sub>λ</sub>×<sub> ) /k!</sub></sup></font>
 
 
 
</div>
 
----
 
<div id="7SpaceContent" class="zcontent" align="left">
 
 
 
If X orMean is nonnumeric, POISSON returns the #ERROR.
 
 
 
If X &lt; 0 or Mean &lt; 0 ,POISSON returns the #ERROR.
 
 
 
</div>
 
----
 
<div id="12SpaceContent" class="zcontent" align="left"><div class="ZEditBox" align="left">
 
 
 
POISSON
 
 
 
</div></div>
 
----
 
<div id="8SpaceContent" class="zcontent" align="left">
 
 
 
Lets see an example in (Column1, Row1)
 
 
 
UNIQ1bbe901cd2555324-nowiki-00000004-QINU
 
 
 
POISSON returns 0.44568.
 
 
 
Cosider an another example
 
  
UNIQ1bbe901cd2555324-nowiki-00000005-QINU
+
==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
  
POISSON returns 0.195367.
+
==Related Videos==
  
</div>
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{{#ev:youtube|JR-1ftUj__Y|280|center|POISSON}}
----
 
<div id="10SpaceContent" class="zcontent" align="left"><div class="ZEditBox" align="justify">Syntax </div><div class="ZEditBox"><center></center></div></div>
 
----
 
<div id="4SpaceContent" class="zcontent" align="left"><div class="ZEditBox" align="justify">Remarks </div></div>
 
----
 
<div id="3SpaceContent" class="zcontent" align="left"><div class="ZEditBox" align="justify">Examples </div></div>
 
----
 
<div id="11SpaceContent" class="zcontent" align="left"><div class="ZEditBox" align="justify">Description </div></div>
 
----
 
<div id="2SpaceContent" class="zcontent" align="left">
 
  
{| id="TABLE3" class="SpreadSheet blue"
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==See Also==
|- class="even"
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*[[Manuals/calci/EXPONDIST | EXPONDIST ]]
| class=" " |
 
| class="  " | Column1
 
| Column2
 
| Column3
 
| Column4
 
|- class="odd"
 
| class=" " | Row1
 
| class="sshl_f" | 0.44568
 
|
 
|
 
|
 
|- class="even"
 
| class=" " | Row2
 
| class="sshl_f" | 0.195367
 
|
 
|
 
|
 
|- class="odd"
 
| Row3
 
| class=" SelectTD ChangeBGColor SelectTD" |
 
<div id="2Space_Handle" class="zhandles" title="Click and Drag to resize CALCI Column/Row/Cell. It is EZ!"></div><div id="2Space_Copy" class="zhandles" title="Click and Drag over to AutoFill other cells."></div><div id="2Space_Drag" class="zhandles" title="Click and Drag to Move/Copy Area.">[[Image:copy-cube.gif]]  </div>
 
|
 
|
 
|
 
|- class="even"
 
| Row4
 
|
 
|
 
|
 
|
 
|- class="odd"
 
| class=" " | Row5
 
|
 
|
 
|
 
|
 
|- class="even"
 
| Row6
 
|
 
|
 
|
 
|
 
|}
 
  
<div align="left">[[Image:calci1.gif]]</div></div>
+
==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