Jones figures that the total number of thousands of miles that an auto

untchick04tm 2021-12-17 Answered
Jones figures that the total number of thousands of miles that an auto can be driven before it would need to be junked is an exponential random variable with parameter 120. Smith has a used car that he claims has been driven only 10,000 miles. If Jones purchases the car, what is the probability that she would get at least 20,000 additional miles out of it? Repeat under the assumption that the lifetime mileage of the car is not exponentially distributed, but rather is (in thousands of miles) uniformly distributed over (0,40).
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Dawn Neal
Answered 2021-12-18 Author has 35 answers
Step 1
Let X be the number of miles the car can be driven before being junked.
a) First, let XExp(12).
We know that the car has been driven 10,000 miles and we want to find the probability that it will last an additional 20,000 miles. We use the memoryless property of exponential distribution, where t=10000 and s=20000.
Then, P(X10000+20000X>10000)=P(X20000).
P(X30000X>10000)=P(X20000)
=20e120xdx
=e1
Step 2
b) Let XU(0,40). We want to find P(X>30X>10). By Bayes
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Bertha Jordan
Answered 2021-12-19 Author has 37 answers
Let X denote the exponential random variable with parameter 1.20, U denote the uniform random variable with parameters 0 and 40. The probability function of an exponential random variable with parameter 1/20 is
F(x)=1ex20,x0
The desired probability is P(X>30X>10). Since exponential random variable have memoryless property, we have
P(X>30X>10)=P(X>20)=1P(X20)=e10.3679
The probability function of an uniform random variable with parameter 0 and 40 is
F(x)=1400x=x40,0x40
Hence P(U>30U>10)=P(U>30)P(U>10)=1P(U30)1P(U10)=1304011040=13 is the desired probability.
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nick1337
Answered 2021-12-27 Author has 575 answers

Let X denote the total number of thousands of miles that the auto is driven before it needs to be junked. We want to compute P({X30}|{X10}). Assuming X is an exponential random variable with parameter 1/20, we have
P{X30}=30120ex/20dx=3/2etdt=e3/2,
P{X10}=10120ex/20dx=1/2etdt=e1/2,
P({X30}|{X10})=P{X30}/P{X10}=e3/2/e1/2=e1.3678.
Note that we could have saved some work here by using the memoryless property of the exponential random variable.
Assuming X is uniformly distributed over (0,40), we have
P{X30}=3040140dx=1/4,
P{X10}=1040140dx=3/4,
P({X30}|{X10})=P{X30}/P{X10}=(1/4)/(3/4)=1/3.

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