Difference between revisions of "Manuals/calci/EXPONDIST"

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==Description==
 
==Description==
  
*This function gives the  Exponential Distribution. This distribution used to model the time until something happens in the process.
+
*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.
 
*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.  
+
*In EXPONDIST(x, lambda,cu), <math>x</math> is the value of the function, <math>lambda</math> is called rate parameter and <math>cu</math>(cumulative) is the TRUE or FALSE.
*Suppose we are not giving the cu value, by default it will consider the cu value is FALSE.  
+
*This function will give the Cumulative Distribution Function when <math>cu</math> is TRUE, otherwise it will give the Probability Density Function , when <math>cu</math> is FALSE.  
 +
*Suppose we are not giving the <math>cu<math> value, by default it will consider the <math>cu</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.
 
  1. <math>x</math> or <math>\lambda</math> is non-numeric.
 
  2. <math>x<0</math> or <math>\lambda \le 0</math>
 
  2. <math>x<0</math> or <math>\lambda \le 0</math>
The Probability Density Function of an Exponential Distribution is
+
The Probability Density Function of an Exponential Distribution is
 
:<math>f(x,\lambda)=\lambda e^{-\lambda x} , x \ge 0 </math>
 
:<math>f(x,\lambda)=\lambda e^{-\lambda x} , x \ge 0 </math>
 
:<math> =0  ,  x<0</math>
 
:<math> =0  ,  x<0</math>
 
or   
 
or   
:<math>f(x;\lambda)= λe^{-\lambda x} .H(x)</math>
+
:<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
+
*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 :
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:<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>
+
*The mean or expected value of the Exponential Distribution is: <math>E[x]=\frac{1}{\lambda}</math>
 
*The variance of the Exponential Distribution is: <math>Var[x]=\frac{1}{\lambda^2}</math>.
 
*The variance of the Exponential Distribution is: <math>Var[x]=\frac{1}{\lambda^2}</math>.
  

Revision as of 03:32, 4 December 2013

EXPONDIST(x,lambda,cu)


  • is the value of the function
  • is the value of the rate parameter
  • is the logical value like TRUE or FALSE

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.
  • In EXPONDIST(x, lambda,cu), is the value of the function, is called rate parameter and (cumulative) is the 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 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

  • The mean or expected value of the Exponential Distribution is:
  • The variance of the Exponential Distribution is: .

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"

See Also

References