Poisson distribution. E(X) not a whole number.

It does not have to be an integer. In summary, the problem is asking for the probability of no cars passing in an hour and exactly 4 cars passing in 30 minutes, given that on average there is one car passing every 20 minutes on a remote road. The probability is calculated using the Poisson distribution with λ = 3, and can be any non-negative real number as it does not necessarily have to be in the support of X.
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Darth Frodo
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Homework Statement


During the day, cars pass along a point on a remote road at an average rate of one per 20 minutes.

Calculate the probability that;

(i) in the course of an hour no car passes;
(ii) in the course of 30 minutes exactly 4 cars pass;

Homework Equations



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

The Attempt at a Solution



[itex]\lambda = 3[/itex]

[itex]P(X = 0) = e^{-3} = 0.0497[/itex] My question: In part 2 [itex]\lambda = 1.5[/itex]. Can [itex]\lambda[/itex] be a Real number or must it be an integer? I assume it can be any number as [itex] E(X) [/itex] does not necessarily have to be in the support of X.

Thanks.
 
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λ can be any non-negative real number.
 
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1. What is the Poisson distribution and how is it used in science?

The Poisson distribution is a probability distribution that is used to model the number of occurrences of a rare event in a given time or space. It is often used in science to analyze the frequency of events such as radioactive decay, natural disasters, and disease outbreaks.

2. How is the expected value (E(X)) calculated for a Poisson distribution?

The expected value of a Poisson distribution is equal to the mean and is calculated by multiplying the rate parameter (λ) by the sample size (n). It is represented by the formula E(X) = λn.

3. Why is the expected value (E(X)) not always a whole number in a Poisson distribution?

The expected value (E(X)) in a Poisson distribution is calculated using a continuous probability distribution, which means that it can take on any real value. In some cases, the expected value may not be a whole number due to the nature of the underlying data or the sample size used for the calculation.

4. How is the Poisson distribution related to the binomial distribution?

The Poisson distribution is a limiting case of the binomial distribution, where the number of trials (n) approaches infinity and the probability of success (p) approaches zero. In other words, the Poisson distribution is used when the probability of success is very small and the number of trials is very large.

5. Can the Poisson distribution be used for both discrete and continuous data?

Yes, the Poisson distribution can be used for both discrete and continuous data. It is commonly used for discrete data, but it can also be used to approximate continuous data if the sample size is large enough. However, it should be noted that the Poisson distribution is only suitable for data that follows a specific pattern, such as rare events occurring independently of each other.

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