/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 31 A doctor has scheduled two appoi... [FREE SOLUTION] | 91Ó°ÊÓ

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A doctor has scheduled two appointments, one at 1 P.M. and the other at \(1: 30 \mathrm{P}, \mathrm{M}\). The amounts of time that appointments last are independent exponential random variables with mean 30 minutes. Assuming that both patients are on time, find the expected amount of time that the \(1: 30\) appointment spends at the doctor's office.

Short Answer

Expert verified
The expected amount of time that the 1:30 appointment spends at the doctor's office is 45 minutes.

Step by step solution

01

Determine the probability density function for appointment duration

Since the appointment duration follows an exponential distribution, the pdf can be written as: $$ f(x) = \frac{1}{\mu}e^{-\frac{x}{\mu}}, $$ where \(\mu\) is the mean of the distribution (30 minutes in this case) and \(x\) represents the appointment duration in minutes.
02

Calculate the expected duration for the second appointment, conditional on the first appointment duration

We are interested in finding the expected duration 1:30 appointment at the doctor's office. We can begin by finding the expected value of the second appointment duration, conditional on the duration of the first appointment: We integrate x multiplied by f2(x) times f1(y), and integrate from \(y=x\) to \(\infty\) dy then from 0 to x dx. $$ E[X_2|X_1] = \int_0^\infty x \left(\int_x^\infty \frac{1}{30}e^{-\frac{y}{30}} dy\right) \frac{1}{30}e^{-\frac{x}{30}} dx $$ Here, we have used two exponential random variables of mean 30 which are independent of each other. Now we need to compute the integral of \(E[X_2|X_1]\)
03

Compute the integral

First, we compute the inner integral: $$ \int_x^\infty \frac{1}{30}e^{-\frac{y}{30}} dy = e^{-\frac{x}{30}} \\ $$ Now, substituting this back in our main expression: $$ E[X_2|X_1] = \int_0^\infty x \cdot e^{-\frac{x}{30}} \frac{1}{30}e^{-\frac{x}{30}} dx = \int_0^\infty x \cdot e^{-\frac{2x}{30}} \frac{1}{30} dx $$ We use integration by parts with u = x and \(dv = e^{-\frac{2x}{30}} \frac{1}{30}dx\). We now find du and v: $$ du = dx \\ v = \int e^{-\frac{2x}{30}} \frac{1}{30} dx = -\frac{15}{30} e^{-\frac{2x}{30}} = -\frac{1}{2} e^{-\frac{2x}{30}} \\ $$ Now, we compute the integral using integration by parts formula: $$ \int u dv = uv - \int v du $$ $$ E[X_2|X_1] = -\frac{1}{2} xe^{-\frac{2x}{30}}\Big|_0^\infty - \int_0^\infty (-\frac{1}{2}) e^{-\frac{2x}{30}} dx $$ The first term is zero. The second term is: $$ \int_0^\infty (-\frac{1}{2}) e^{-\frac{2x}{30}} dx = \frac{1}{2} \int_0^\infty e^{-\frac{2x}{30}} dx $$ Now we compute the last integral above: $$ \frac{1}{2} \int_0^\infty e^{-\frac{2x}{30}} dx = \left[-\frac{1}{2}\cdot \frac{30}{2} e^{-\frac{2x}{30}} \right]_0^\infty = 15 $$ This gives the expected duration for the second appointment, given the first appointment duration.
04

Calculate the expected time the 1:30 appointment spends at the doctor's office

We have found that the expected duration for the second appointment, given the first appointment duration, is 15 minutes. Since both patients are on time, the 1:30 appointment has to wait for the first appointment to finish. Thus, the expected time the 1:30 appointment spends at the doctor's office is: (Expected time for the 1:30 appointment) = (30 min average duration of the first appointment) + (15 min expected duration for the second appointment given the first appointment duration) (Expected time for the 1:30 appointment) = 30 + 15 = 45 minutes Thus, the expected amount of time that the 1:30 appointment spends at the doctor's office is 45 minutes.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Probability Density Function
The probability density function (PDF) is a key concept in understanding continuous random variables. It describes the likelihood of a random variable taking on a particular value. Mathematically, for a continuous random variable X, the PDF is a function f(x) such that, for two numbers a and b with a ≤ b, the probability that X is between a and b is the integral of f(x) from a to b.

In the case of the exponential distribution, the PDF is given by: \[ f(x) = \frac{1}{\mu}e^{-\frac{x}{\mu}} \] where \( \mu \) is the mean of the distribution and represents the average interval between events. For an exponential random variable, the PDF is characterized by its 'memoryless' property, meaning the probability of an event occurring in the next instant is independent of how much time has already elapsed.

This concept allows us to calculate probabilities and expected values for exponential random variables by integrating over this function. The PDF plays a crucial role in helping us compute the expected duration of the appointments in the given exercise.
Exponential Random Variables
Exponential random variables come into play when modeling time until an event occurs, such as the length of time a patient spends at a doctor's appointment. The key parameter of an exponential distribution is its mean \( \mu \), which in the context of our exercise is 30 minutes. Exponential random variables have a unique 'memoryless' property implying that the probability of the event occurring in the future does not depend on how long you have already been waiting.

The exponential distribution is often used to model waiting times because events are equally likely to occur at any moment. In the exercise, we encounter two independent exponential random variables representing the durations of two appointments. Understanding the behavior of these variables and their independence is fundamental to solving the problem of finding the expected time the second patient will spend at the doctor's office.
Integration by Parts
Integration by parts is a powerful technique used to solve integrals, especially when the integrand is a product of two functions. It's based on the product rule for differentiation and serves as a counterpoint for multiplication in integration.

The formula for integration by parts is: \[ \int u dv = uv - \int v du \] where u and v are functions of x. In our exercise, we apply integration by parts to find the expected duration for the second appointment. We choose u to be the time x, and dv to be the exponential component of the PDF. This choice simplifies the integral into parts that are easier to handle analytically.

Integration by parts allows us to break down complex integrals into simpler parts, which is precisely what we do to solve the integral for the expected duration of the second appointment. This method is instrumental in handling the second step of the solution, transforming a potentially complicated integral into a form that is easier to evaluate.

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Most popular questions from this chapter

Suppose that \(\left[N_{0}(t), t \geqslant 0\right\\}\) is a Poisson process with rate \(\lambda=1\). Let \(\lambda(t)\) denote a nonnegative function of \(t\), and let $$ m(t)=\int_{0}^{t} \lambda(s) d s $$ Define \(N(t)\) by $$ N(t)=N_{0}(m(t)) $$ Argue that \(\\{N(t), t \geqslant 0\\}\) is a nonhomogeneous Poisson process with intensity function \(\lambda(t), t \geqslant 0\)

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Let \(X, Y_{1, \ldots .}, Y_{n}\) be independent exponential rardom variables; \(X\) having rate \(\lambda_{1}\) and \(Y_{i}\) having rate \(\mu\). Let \(A_{j}\) be the event that the \(j\) th smallest of these \(n+1\) random variables is one of the \(Y_{i}\). Find \(p=P\left\\{X>\max _{i} Y_{i}\right\\}\), by using the identity $$ p=P\left(A_{1} \cdots A_{n}\right)=P\left(A_{1}\right) P\left(A_{2} \mid A_{1}\right) \cdots P\left(A_{n} \mid A_{l} \cdots A_{n-1}\right) $$ Verify your answer when \(n=2\) by conditioning on \(X\) to obtain \(p\).

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