Probability of No Events in First Two Hours for Poisson Process with Rate 2

In summary, an exponential distribution is a probability distribution used to model the time between events in a Poisson process. It differs from other distributions in that it is continuous and has a constant hazard rate. The key features of an exponential distribution include the probability density function and cumulative distribution function. It is commonly used in scientific research to analyze continuous data and estimate the time to an event. Real-world examples include customer arrivals, solar flare intervals, and cell divisions.
  • #1
Quincy
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Homework Statement


Consider a Poisson process for which events occur at a rate of 2 per hour.
(a) Give the probability that the time until the first event occurs exceeds 2 hours. Use an exponential distribution to find the probability.

Homework Equations


The Attempt at a Solution


[tex]\lambda[/tex] = 1/2

pdf = (1/2)e-x/2, x >= 0

The probability that the time until the first event occurs exceeds two hours, is equal to the probability that 0 events occur in the first two hours.
I have the pdf, but I'm not sure how to use it to find the probability.
 
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  • #2
P(x > 2) = 1 - p(x=1) - p(x=2)
 

FAQ: Probability of No Events in First Two Hours for Poisson Process with Rate 2

1. What is an exponential distribution?

An exponential distribution is a probability distribution that describes the time between events in a Poisson process. It is used to model events that occur randomly and independently over time, such as radioactive decay or the time between customer arrivals at a store.

2. How is an exponential distribution different from other distributions?

An exponential distribution is different from other distributions in that it is a continuous distribution, meaning it can take on any value within a given range. It also has a constant hazard rate, meaning the probability of an event occurring in a given time interval remains the same regardless of how much time has passed.

3. What are the key features of an exponential distribution?

The key features of an exponential distribution include the probability density function (PDF), which describes the likelihood of a particular event occurring within a given time interval, and the cumulative distribution function (CDF), which describes the probability that an event will occur within a certain amount of time. The mean and standard deviation of an exponential distribution can also be calculated using the parameter, lambda (λ).

4. How is an exponential distribution used in scientific research?

An exponential distribution is used in scientific research to model a wide range of phenomena, from the time between earthquakes to the duration of a phone call. It is particularly useful for analyzing data that is continuous and has a constant hazard rate. It is also commonly used in survival analysis, where it is used to estimate the time to an event, such as the time until a patient recovers from a disease.

5. What are some real-world examples of an exponential distribution?

Some real-world examples of an exponential distribution include the time between customer arrivals at a store, the amount of time between solar flares, and the duration of phone calls. It is also commonly used in biology to model the time between cell divisions and in physics to model the decay of radioactive materials.

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