Difference between revisions of "Manuals/calci/EXPONDIST"
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*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. | ||
+ | *Exponential distribution is the only continuous memoryless random distribution. It is a continuous analog of the Geometric distribution. | ||
*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. | *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. | *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. |
Revision as of 23:20, 8 June 2014
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
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
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"