How can the Poisson distribution be rewritten in terms of P(X <= 1)?

In summary: This is valid, I believe, because if x can only take on positive integers, then P(x \geq 1) is exactly the same as P(x= 1) which is \frac{e^{-\lambda}\lambda^1}{1!}= \lambda e^{-\lambda}
  • #1
Bob19
71
0
Hello I'm Presented with the following Poisson distribution question

[tex]P(X = x) = \frac{e^{-\lambda} \cdot \lambda^{x}}{x!}[/tex]

where [tex]x \in (1,2,3,\ldots)[/tex] and [tex]\lambda > 0[/tex]

Then I'm suppose to show that the above can be re-written if

[tex]P(X \leq 1) = 1 - e^{- \lambda}[/tex]

Any idears on how I do that?

I'm told [tex]\sum_{x=0} ^ {\infty} p(x) = \frac{e^{- \lambda} \cdot \lambda^{x}}{x!} = e^{- \lambda} \sum _{x=0} ^{\infty} \frac{\lambda ^{x}}{x!}[/tex]

if [tex] \lambda ^{x} = 1[/tex]

then [tex] p(x) = e^{- \lambda} [/tex]

This must give [tex]P(X \leq 1) = 1- e^{- \lambda}[/tex]

Can anybody please tell me if I'm on the right track here?

Sincerley Bob
 
Last edited:
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  • #2
Bob19 said:
Hello I'm Presented with the following Poisson distribution question
[tex]P(X = x) = \frac{e^{-\lambda} \cdot \lambda^{x}}{x!}[/tex]
where [tex]x \in (1,2,3,\ldots)[/tex] and [tex]\lambda > 0[/tex]
Then I'm suppose to show that the above can be re-written if
[tex]P(X \leq 1) = 1 - e^{- \lambda}[/tex]
Any idears on how I do that?
I'm told [tex]\frac{e^{- \lambda} \cdot \lambda^{x}}{x!} = e^{- \lambda} \sum _{x=0} ^{\infty} \frac{\lambda ^{x}}{x!} = e^{- \lambda} e^{\lambda} = 1[/tex]
Then if [tex] \lambda ^{x} = 1[/tex]
No, you are not told that! Obviously
[tex]\frac{e^{- \lambda} \cdot \lambda^{x}}{x!}[/tex]
,for specific x, is not the same as sum for all x:
[tex]e^{- \lambda} \sum _{x=0} ^{\infty} \frac{\lambda ^{x}}{x!}[/tex]
What you are told is that the sum for all x is equal to 1: so that this is a valid probability distribution.
Since x can only take on positive integer values, [itex]P(x \leq 1)[/itex] is exactly the same as [itex]P(x= 1)[/itex] which is
[tex]\frac{e^{-\lambda}\lambda^1}{1!}= \lambda e^{-\lambda}[/tex]
Assuming, as you say, that that is [itex]1- e^{-\lambda}[/itex], then you are not told that [itex]e^{\lambda}= 1[/itex]. You are told, rather, that [itex]e^{\lamba}= 1+ \lambda[/itex] so that
P(X= x) can be written as
[tex]P(X= x)= \frac{e^{-\lamba}\lamba^x}{x!}= \frac{\lamba^x}{(1+\lambda)x!}[/tex]
 
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  • #3
Hello Hall and Thank You,

x = 1

then [tex]\frac{e^{-\lamba}\lamba^1}{1!}= \frac{\lamba^1}{(1+ 1)1!} = e^{- \lambda} [/tex]

Which is then [tex]P(x \geq 1) = 1- e^{- \lambda}[/tex]

Am I on the right track now ?

Sincerely
Bob
 
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  • #4
I don't know why the Latex isn't showing properly. What I was trying to say was that IF P(X<= 1)= P(X= 1)= lambda e-lambda is equal to 1- e-lambda, then multiplying both sides of the equation by elamba we have lambda= elambda- 1 or elambda= lambda + 1.

Now to "rewrite" P(x)= (lambdaxe-lambda/x!, just replace that e-lambda bu 1/(lambda+ 1), getting P(x)= lambdax/(x!(lambda+ 1)).
 

What is the Poisson distribution?

The Poisson distribution is a probability distribution that is used to model the number of occurrences of a certain event within a fixed interval of time or space. It is often used in situations where events occur independently and at a constant rate.

What are the characteristics of a Poisson distribution?

Some characteristics of a Poisson distribution include: the mean and variance are equal, the distribution is discrete, and the events are independent and occur at a constant rate. Additionally, the shape of the distribution is determined by a single parameter, lambda (λ), which represents the average number of events that occur in the given interval.

How is the Poisson distribution different from other probability distributions?

The Poisson distribution is different from other distributions in that it is discrete, meaning that it deals with whole numbers, rather than continuous values. It is also unique in that it only has one parameter, lambda (λ), whereas other distributions may have multiple parameters.

What are some real-world applications of the Poisson distribution?

The Poisson distribution is commonly used in a variety of fields, including biology, finance, and engineering. Some specific applications include modeling the number of earthquakes in a certain region, the number of customers arriving at a store, and the number of accidents on a highway.

How is the Poisson distribution related to the binomial distribution?

The Poisson distribution is often used as an approximation of the binomial distribution in situations where the number of trials is large and the probability of success is small. Additionally, the Poisson distribution can be derived from the binomial distribution by taking the limit as the number of trials goes to infinity and the probability of success goes to zero.

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