Poisson Distribution - why are these different?

WQgc3RhdGVtCg==In summary, the conversation discusses the probability of zero events occurring in 45 seconds for a Poisson process with a rate of 0.2 events per second. Two different approaches are used to calculate this probability, but they result in significantly different values due to a misunderstanding of the rate units. It is important to always check units in calculations.
  • #1
spitz
60
0

Homework Statement



[itex]X(t)[/itex] is a Poisson process with [itex]\lambda=0.2[/itex] events per second. What is the probability of zero events in [itex]45[/itex] seconds?

2. The attempt at a solution

[itex]\frac{45}{0.2}=225[/itex] ([itex]0.2[/itex] second intervals)

so [itex]P[X=0][/itex] in [itex]225[/itex] consecutive intervals is:

[itex]\left(e^{-0.2}\right)^{225} = 2.86 \times 10^{-20}[/itex]

or

[itex]\lambda = (45)(0.2)=9[/itex] events per [itex]45[/itex] seconds gives:

[itex]e^{-9} = 1.23 \times 10^{-4}[/itex]

Both are approx. zero, but they are way different. How come?
 
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  • #2
spitz said:

Homework Statement



[itex]X(t)[/itex] is a Poisson process with [itex]\lambda=0.2[/itex] events per second. What is the probability of zero events in [itex]45[/itex] seconds?

2. The attempt at a solution

[itex]\frac{45}{0.2}=225[/itex] ([itex]0.2[/itex] second intervals)

so [itex]P[X=0][/itex] in [itex]225[/itex] consecutive intervals is:

[itex]\left(e^{-0.2}\right)^{225} = 2.86 \times 10^{-20}[/itex]

or

[itex]\lambda = (45)(0.2)=9[/itex] events per [itex]45[/itex] seconds gives:

[itex]e^{-9} = 1.23 \times 10^{-4}[/itex]

Both are approx. zero, but they are way different. How come?

The 0.2 is---as you stated originally--- the number of events per second; there is no 0.2 sec anywhere in the problem! In other words, 0.2/sec = (1/5)/sec = 1 per 5 sec. This is a good illustration of the old advice: always check your units (sec ≠ per sec).

RGV
 

1. What is the Poisson Distribution?

The Poisson Distribution is a discrete probability distribution that is used to model the number of times an event occurs within a specific time interval or in a certain area. It is named after the French mathematician Siméon Denis Poisson.

2. What are the assumptions of the Poisson Distribution?

The assumptions of the Poisson Distribution include a fixed time interval or area, independence of events, and a constant probability of an event occurring during the interval or in the area.

3. How is the Poisson Distribution different from other probability distributions?

The Poisson Distribution differs from other probability distributions in that it models the number of occurrences of an event within a specific time interval or area, whereas other distributions may model continuous variables or the probability of events occurring at specific points in time.

4. When should the Poisson Distribution be used?

The Poisson Distribution should be used when modeling the number of occurrences of an event within a specific time interval or area, and when the assumptions of the distribution are met.

5. What are some real-world applications of the Poisson Distribution?

The Poisson Distribution is commonly used in fields such as finance, insurance, and epidemiology to model the number of events such as accidents, claims, or disease outbreaks. It is also used in quality control to monitor defect rates and in queuing theory to analyze waiting times.

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