Difference between revisions of "Manuals/calci/EXPONDIST"

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<div style="font-size:30px">'''EXPONDIST(x,Lambda,cum)'''</div><br/>
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<div style="font-size:25px">'''EXPONDIST(X,Lambda,Cumulative)'''</div><br/>
*<math>x</math> is the value of the function
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*<math>X</math> is the value of the function.
*<math>lambda</math> is the value of the rate parameter
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*<math>Lambda(\lambda)</math> is the value of the rate parameter.
*<math>cu</math> is the logical value like TRUE or FALSE
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*<math>Cumulative</math> is the logical value like TRUE or FALSE.
 +
**EXPONDIST(), returns the exponential distribution.
  
 
==Description==
 
==Description==
 
+
*This function gives the  Exponential Distribution. This distribution is used to model the time until something happens in the process.
*This function gives the  Exponential Distribution. This distribution used to model the time until something happens in the process.
 
 
*This describes the time between events in a Poisson process i.e, a process in which events occur continuously and independently at a constant average rate.
 
*This describes the time between events in a Poisson process i.e, a process in which events occur continuously and independently at a constant average rate.
 
*For e.g Time between successive vehicles arrivals at a workshop.  
 
*For e.g Time between successive vehicles arrivals at a workshop.  
*In EXPONDIST(x, lambda,cu), xis the value of the function, lambda is called rate parameter and cu(cumulative) is the TRUE or FALSE. *This function will give the cumulative distribution function , when cu is TRUE,otherwise it will give the probability density function , when cu is FALSE.  
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*Exponential distribution is the only continuous memoryless random distribution. It is a continuous analog of the Geometric distribution.
*Suppose we are not giving the cu value, by default it will consider the cu value is FALSE.  
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*In <math>EXPONDIST(X,Lambda,Cumulative)</math>, <math>X</math> is the value of the function, <math> Lambda</math> is called rate parameter and <math>Cumulative</math> is either TRUE or FALSE.
 +
*This function will give the Cumulative Distribution Function when <math>Cumulative</math> is TRUE, otherwise it will give the Probability Density Function , when <math>Cumulative</math> is FALSE.  
 +
*Suppose we are not giving the <math>Cumulative</math> value, by default it will consider the <math>Cumulative</math> value is FALSE.  
 
*This function will give the error result when
 
*This function will give the error result when
  1. <math>x</math> or <math>\lambda</math> is non-numeric.
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  1. <math>X</math> or <math>Lambda</math> is non-numeric.
  2. <math>x<0</math> or <math>\lambda \le 0</math>
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  2. <math>X<0</math> or <math>Lambda \le 0</math>
The Probability Density Function of an Exponential Distribution is
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The Probability Density Function of an Exponential Distribution is:
:<math>f(x,\lambda)=\lambda e^{-\lambda x} , x \ge 0 </math>
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<math>f(x,\lambda)=\begin{cases}
:<math> =0 ,   x<0</math>
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\lambda e^{-\lambda x} &, x \ge 0 \\
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0 &, x<0
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\end{cases}</math>
 
or   
 
or   
:<math>f(x;\lambda)= λe^{-\lambda x} .H(x)</math>
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:<math>f(x;\lambda)= \lambda e^{-\lambda x} .H(x)</math>
  
*where <math>\lambda</math> is the rate parameter and H(x) is the  Heaviside step function
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*where <math>\lambda</math> is the rate parameter and <math>H(x)</math> is the  Heaviside step function
 
*This function is valid only on the interval [0,infinity].
 
*This function is valid only on the interval [0,infinity].
 
The Cumulative Distribution Function is :
 
The Cumulative Distribution Function is :
<math>F(x;\lambda)=1-e^{-\lambda x},   x \ge 0</math>
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<math>F(x,\lambda)=\begin{cases}
:<math>0     ,   x<0 </math>
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1-e^{-\lambda x} &, x \ge 0 \\
 +
0 &, x<0
 +
\end{cases}</math>
 
or
 
or
 
:<math>F(x,\lambda)=1-e^{-\lambda x}.H(x)</math>
 
:<math>F(x,\lambda)=1-e^{-\lambda x}.H(x)</math>
  
*The mean or expected value of the Exponential Distribution is: <math>E[x]=\frac{1}{ λ}</math>
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==ZOS==
*The variance of the Exponential Distribution is: <math>Var[x]=\frac{1}{\lambda^2}</math>.
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*The syntax is to calculate EXPONDIST in ZOS is <math>(X,Lambda,Cumulative)</math>.
 +
**where <math>X</math> is the value of the function
 +
**<math>Lambda</math> is the value of the rate parameter
 +
**<math>Cumulative</math> is the logical value like TRUE or FALSE.
 +
*For e.g.,EXPONDIST(11..12,2.3..3.3..0.4,FALSE)
 +
{{#ev:youtube|R1BjrD7T6Fs|280|center|Exponential Distribution}}
  
 
==Examples==
 
==Examples==
Question : If jobs arrive at an average of 15 seconds, λ = 5 per minute, what is the probability of waiting 30 seconds, i.e 0.5 min?
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Question : If jobs arrive at an average of 15 seconds, <math>\lambda =5</math> per minute, what is the probability of waiting 30 seconds, i.e 0.5 min?
Here λ=5 and x=0.5
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Here <math>\lambda =5</math> and <math>x=0.5</math>
=EXPONDIST(0.5,5,TRUE)=0.917915001
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*=EXPONDIST(0.5,5,TRUE) = 0.917915001
=EXPONDIST(5,3,TRUE)=0.999999694
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*=EXPONDIST(5,3,TRUE) = 0.999999694
=EXPONDIST(0.4,2,FALSE)=0.898657928"
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*=EXPONDIST(0.4,2,FALSE) = 0.898657928
 +
 
 +
==Related Videos==
 +
 
 +
{{#ev:youtube|RQ9VOaWxDFo|280|center|Exponential Probability}}
  
 
==See Also==
 
==See Also==
Line 43: Line 58:
  
 
==References==
 
==References==
*[http://en.wikipedia.org/wiki/Exponential_distribution Exponential Distribution]
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*[http://en.wikipedia.org/wiki/Exponential_distribution Exponential Distribution]
 +
 
 +
 
 +
 
 +
*[[Z_API_Functions | List of Main Z Functions]]
 +
 
 +
*[[ Z3 |  Z3 home ]]

Latest revision as of 16:04, 10 August 2018

EXPONDIST(X,Lambda,Cumulative)


  • is the value of the function.
  • is the value of the rate parameter.
  • is the logical value like TRUE or FALSE.
    • EXPONDIST(), returns the exponential distribution.

Description

  • This function gives the Exponential Distribution. This distribution is used to model the time until something happens in the process.
  • This describes the time between events in a Poisson process i.e, a process in which events occur continuously and independently at a constant average rate.
  • For e.g Time between successive vehicles arrivals at a workshop.
  • Exponential distribution is the only continuous memoryless random distribution. It is a continuous analog of the Geometric distribution.
  • In , is the value of the function, is called rate parameter and is either TRUE or FALSE.
  • This function will give the Cumulative Distribution Function when is TRUE, otherwise it will give the Probability Density Function , when is FALSE.
  • Suppose we are not giving the value, by default it will consider the value is FALSE.
  • This function will give the error result when
1.  or  is non-numeric.
2.  or 

The Probability Density Function of an Exponential Distribution is: or

  • where is the rate parameter and is the Heaviside step function
  • This function is valid only on the interval [0,infinity].

The Cumulative Distribution Function is : or

ZOS

  • The syntax is to calculate EXPONDIST in ZOS is .
    • where is the value of the function
    • is the value of the rate parameter
    • is the logical value like TRUE or FALSE.
  • For e.g.,EXPONDIST(11..12,2.3..3.3..0.4,FALSE)
Exponential Distribution

Examples

Question : If jobs arrive at an average of 15 seconds, per minute, what is the probability of waiting 30 seconds, i.e 0.5 min? Here and

  • =EXPONDIST(0.5,5,TRUE) = 0.917915001
  • =EXPONDIST(5,3,TRUE) = 0.999999694
  • =EXPONDIST(0.4,2,FALSE) = 0.898657928

Related Videos

Exponential Probability

See Also

References