Exponential Distribution and Waiting Time

In summary, the conversation discusses the use of the memoryless property of the exponential distribution in finding the expected waiting time for a bus and the expected additional waiting time after a specified waiting time. The distribution of the additional waiting time is the same as the distribution of the original waiting time due to the memoryless property. This property has significant applications in probability and is not just a trivial concept.
  • #1
gajohnson
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Homework Statement



Suppose that the waiting time for the CTA Campus bus at the Reynolds Club stop is a continuous random variable Z (in hours) with an exponential distribution, with density f(z) = 6e–6z for z ≥ 0; f(z) = 0 for z < 0.

(a) What is the expected waiting time in minutes (the expected value of Z)?

(b) Suppose you have been waiting exactly ½ hour. What is the expected additional waiting time
E(W), where W = Z – ½ ? [Hint: For a > ½ , what is the conditional probability Z > a, given Z > ½ ? What is the conditional probability Z < a, given Z > ½ ? What is the conditional probability W < b=a–0.5, given Z > ½ ? What is the conditional density of W?]

Homework Equations


The Attempt at a Solution



Part (a) is easy, simply doing the expected value calculation and coming away with E(Z)=1/6.

Isn't the answer the part (b) 1/6 as well due to the memoryless property of the exponential distribution? Or am I misunderstanding the question and/or the memoryless property? If you've already been waiting 1/2 hour, the expected additional waiting time is the same as the expected waiting time at time 0, is it not?
 
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  • #2
gajohnson said:

Homework Statement



Suppose that the waiting time for the CTA Campus bus at the Reynolds Club stop is a continuous random variable Z (in hours) with an exponential distribution, with density f(z) = 6e–6z for z ≥ 0; f(z) = 0 for z < 0.

(a) What is the expected waiting time in minutes (the expected value of Z)?

(b) Suppose you have been waiting exactly ½ hour. What is the expected additional waiting time
E(W), where W = Z – ½ ? [Hint: For a > ½ , what is the conditional probability Z > a, given Z > ½ ? What is the conditional probability Z < a, given Z > ½ ? What is the conditional probability W < b=a–0.5, given Z > ½ ? What is the conditional density of W?]


Homework Equations





The Attempt at a Solution



Part (a) is easy, simply doing the expected value calculation and coming away with E(Z)=1/6.

Isn't the answer the part (b) 1/6 as well due to the memoryless property of the exponential distribution? Or am I misunderstanding the question and/or the memoryless property? If you've already been waiting 1/2 hour, the expected additional waiting time is the same as the expected waiting time at time 0, is it not?

Yes, but even more than that is true: you can even say what is the distribution of W, and the question asks you to do that.
 
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  • #3
Ray Vickson said:
Yes, but even more than that is true: you can even say what is the distribution of W, and the question asks you to do that.

Yes, of course. But it works out pretty simply that the distribution of W is the same as the distribution of Z. Thanks for confirming this for me. Sometimes something seems so simple that I question it--an instance of trying to disentangle actual mathematical results from the intentions of those writing the exercises.
 
  • #4
gajohnson said:
Yes, of course. But it works out pretty simply that the distribution of W is the same as the distribution of Z. Thanks for confirming this for me. Sometimes something seems so simple that I question it--an instance of trying to disentangle actual mathematical results from the intentions of those writing the exercises.

You may not be aware of it, but the memoryless property of the exponential---in all its glory and detail--- is perhaps one of the most useful facts in probability. It is used everywhere, especially in queueing theory, reliability modelling, etc. This problem is introducing you to one of the most important topics in the subject, so is not just a "busy work" homework problem.
 
  • #5
Ray Vickson said:
You may not be aware of it, but the memoryless property of the exponential---in all its glory and detail--- is perhaps one of the most useful facts in probability. It is used everywhere, especially in queueing theory, reliability modelling, etc. This problem is introducing you to one of the most important topics in the subject, so is not just a "busy work" homework problem.

I am indeed aware of the importance of the memoryless property of the exponential (although likely not to the extent that you are), and this is precisely why it seemed to me that there was a trick involved.

I don't know quite how I gave off the impression that I thought the memoryless property was itself trivial, I only meant to say that I was suspicious that the solution was a trivial application of that property. However, if I have accidentally implied differently, you defended its honor nobly.
 
  • #6
gajohnson said:
I am indeed aware of the importance of the memoryless property of the exponential (although likely not to the extent that you are), and this is precisely why it seemed to me that there was a trick involved.

I don't know quite how I gave off the impression that I thought the memoryless property was itself trivial, I only meant to say that I was suspicious that the solution was a trivial application of that property. However, if I have accidentally implied differently, you defended its honor nobly.

Well, the magic is that it is "easy", but in a way very deep. The math is trivial but the consequences are far from trivial.
 
  • #7
I look forward to discovering more about this wonderful thing. Thanks for your insight!
 

Related to Exponential Distribution and Waiting Time

What is the Exponential Distribution?

The Exponential Distribution is a probability distribution that describes the time between events in a Poisson process, where events occur continuously and independently at a constant average rate.

What is the relationship between the Exponential Distribution and waiting time?

The Exponential Distribution is commonly used to model waiting times. This is because the distribution describes the probability of waiting a certain amount of time before an event occurs, making it useful for predicting and analyzing waiting times.

How is the Exponential Distribution different from other probability distributions?

The Exponential Distribution is unique in that it is a continuous distribution, meaning it can take on any value within a certain range. It also has a constant hazard rate, which means the probability of an event occurring in a specific time interval is the same, regardless of how much time has already passed.

What are some real-life applications of the Exponential Distribution?

The Exponential Distribution is commonly used in fields such as engineering, finance, and medicine to model waiting times. For example, it can be used to predict the time between equipment failures, the time between customer arrivals at a store, and the time between medical emergencies.

How is the Exponential Distribution calculated and interpreted?

The Exponential Distribution is calculated using the formula f(x) = λe^(-λx), where λ is the average rate of events. This formula can be used to calculate the probability of waiting a certain amount of time before an event occurs. The distribution is interpreted as the probability of an event occurring at a specific time, given that no event has occurred before that time.

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