Probability Theory: Poisson Distribution

In summary, to compute the probability that a random variable with a Poisson distribution with parameter λ = 2 is greater than or equal to 4, we can use the formula 1-P(X≤3). This simplifies to Ʃn = 0∞ 2n/(n+4)!. When plugged into Wolfram Alpha, this yields [1/48(3e2-19)].
  • #1
mliuzzolino
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Homework Statement



A random variable has a Poisson distribution with parameter λ = 2. Compute the following probabilities, giving an exact answer and a decimal approximation.

P(X ≥ 4)


Homework Equations



P(X = k) = λke/k!

The Attempt at a Solution



P(X ≥ 4) = Ʃk = 4 λke/k!

= λ4e/4! + λ5e/5! + λ6e/6! + [itex]\cdots[/itex]

= e4/4! + λ5/5! + λ6/6! + [itex]\cdots[/itex]]

= e Ʃk = 4 λk/k!

Let n = k - 4

=e Ʃn = 0 λn+4/(n+4)!

plug in λ = 2

= e-2 Ʃn = 0 2n+4/(n+4)!

= e-2 Ʃn = 0 2n24/(n+4)!

= 16e-2 Ʃn = 0 2n/(n+4)!

This is as far as I have gotten, but I'm not sure I'm on the correct track. I used wolfram alpha to reduce the summation term, Ʃn = 0 2n/(n+4)!, to [1/48(3e2-19)], but I'm at a loss as to how to get there myself.

Anyone have any suggestions? It would be greatly appreciated!
 
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  • #2
mliuzzolino said:

Homework Statement



A random variable has a Poisson distribution with parameter λ = 2. Compute the following probabilities, giving an exact answer and a decimal approximation.

P(X ≥ 4)


Homework Equations



P(X = k) = λke/k!

The Attempt at a Solution



P(X ≥ 4) = Ʃk = 4 λke/k!

= λ4e/4! + λ5e/5! + λ6e/6! + [itex]\cdots[/itex]

= e4/4! + λ5/5! + λ6/6! + [itex]\cdots[/itex]]

= e Ʃk = 4 λk/k!

Let n = k - 4

=e Ʃn = 0 λn+4/(n+4)!

plug in λ = 2

= e-2 Ʃn = 0 2n+4/(n+4)!

= e-2 Ʃn = 0 2n24/(n+4)!

= 16e-2 Ʃn = 0 2n/(n+4)!

This is as far as I have gotten, but I'm not sure I'm on the correct track. I used wolfram alpha to reduce the summation term, Ʃn = 0 2n/(n+4)!, to [1/48(3e2-19)], but I'm at a loss as to how to get there myself.

Anyone have any suggestions? It would be greatly appreciated!

##P(X \geq 4) = 1-P(X \leq 3).##
 
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  • #3
Ray's 'clue' is what you must use for this problem. generally, this would be a difficult problem. But luckily, 4 is not a large number.
 
  • #4
Doh! That little fact completely slipped my mind. Thanks guys!
 

What is a Poisson Distribution?

A Poisson Distribution is a mathematical concept used to model the probability of rare events occurring in a given time interval or space. It is often referred to as a discrete probability distribution, as it calculates the likelihood of a certain number of events happening in a fixed interval.

What are the key assumptions of the Poisson Distribution?

The Poisson Distribution relies on three key assumptions: (1) The events are independent, meaning the occurrence of one event does not affect the probability of another event happening. (2) The average rate of events occurring is constant. (3) The probability of an event happening in a very small time or space interval is proportional to the size of the interval.

How is the Poisson Distribution different from the Normal Distribution?

The Poisson Distribution is different from the Normal Distribution in several ways. Firstly, the Poisson Distribution deals with discrete events, while the Normal Distribution deals with continuous events. Additionally, the Normal Distribution assumes a symmetrical bell-shaped curve, while the Poisson Distribution does not. Lastly, the Poisson Distribution is used to model rare events, while the Normal Distribution is used for events that occur more frequently.

How can the Poisson Distribution be applied in real-life situations?

The Poisson Distribution has many real-life applications, such as in the fields of insurance, finance, and healthcare. For example, it can be used to calculate the likelihood of car accidents, the number of customer arrivals at a store, or the number of patients arriving at a hospital in a given time period.

What are the limitations of the Poisson Distribution?

While the Poisson Distribution can be a useful tool, it also has its limitations. It assumes that events are independent, and in real-life situations, this may not always be the case. Additionally, it assumes a constant rate of events, which may not be true in some scenarios. Finally, it is not suitable for events with a large number of occurrences, as it is designed to model rare events.

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