/*! 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} Q5E A college professor never finish... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

A college professor never finishes his lecture before the end of the hour and always finishes his lectures within \({\rm{2}}\) min after the hour. Let \({\rm{X = }}\)the time that elapses between the end of the hour and the end of the lecture and suppose the pdf of \({\rm{X}}\) is

\({\rm{f(x) = \{ }}\begin{array}{*{20}{c}}{{\rm{k}}{{\rm{x}}^2}}&{{\rm{0}} \le {\rm{x}} \le {\rm{2}}}\\{\rm{0}}&{{\rm{otherwise}}}\end{array}\)

a. Find the value of \({\rm{k}}\) and draw the corresponding density curve. (Hint: Total area under the graph of \({\rm{f(x)}}\) is \({\rm{1}}\).)

b. What is the probability that the lecture ends within \({\rm{1}}\) min of the end of the hour?

c. What is the probability that the lecture continues beyond the hour for between \({\rm{60}}\) and \({\rm{90}}\) sec?

d. What is the probability that the lecture continues for at least \({\rm{90}}\) sec beyond the end of the hour?

Short Answer

Expert verified

(a) The value of\({\rm{k}}\)is obtained as\({\rm{k = }}\frac{{\rm{3}}}{{\rm{8}}}\).

(b) The probability that the lecture ends within\({\rm{1}}\)min of the end of the hour is\({\rm{0}}{\rm{.125}}\).

(c) The probability that the lecture continues beyond the hour for between\({\rm{60}}\)and\({\rm{90}}\)sec is\({\rm{0}}{\rm{.2969}}\).

(d) The probability that the lecture continues for at least \({\rm{90}}\) sec beyond the end of the hour is \({\rm{0}}{\rm{.5781}}\).

Step by step solution

01

Concept Introduction

Probability refers to the likelihood of a random event's outcome. This word refers to determining the likelihood of a given occurrence occurring.

02

The value for \({\rm{k}}\)

(a)

Since\({\rm{f(x)}}\)is a legitimate pdf, hence it must satisfy following condition –

\(\int_{{\rm{ - }}\infty }^\infty {{\rm{f(x)}} \cdot {\rm{dx = 1}}} \)

For the given pdf –

\(\begin{aligned}{\rm{k}}\int_{{\rm{ - }}\infty }^\infty {\rm{f}} {\rm{(x)}} \cdot dx &= \int_{\rm{0}}^{\rm{2}} {\rm{k}} {{\rm{x}}^{\rm{2}}} \cdot {\rm{dx = 1}}\\\left( {\frac{{{\rm{k}}{{\rm{x}}^{\rm{3}}}}}{{\rm{3}}}} \right)_{\rm{0}}^{\rm{2}} &= 1\\\frac{{{\rm{k(2}}{{\rm{)}}^{\rm{3}}}}}{{\rm{3}}}- 0 &= 1\\ k &= \frac{{\rm{3}}}{{\rm{8}}}\end{aligned}\)

Hence the pdf can finally be written as –

\({\rm{f(x) = }}\left\{ {\begin{array}{*{20}{l}}{\frac{{{\rm{3}}{{\rm{x}}^{\rm{2}}}}}{{\rm{8}}}}&{{\rm{1}} \le {\rm{X}} \le {\rm{2}}}\\{{\rm{0}}\;\;\;}&{{\rm{otherwise}}}\end{array}} \right.\)

Conditions satisfied by a pdf: For a pdf to be a legitimate pdf, it must satisfy the following two conditions –

  1. \({\rm{f(x)}} \ge {\rm{0}}\)for all\({\rm{x}}\)
  2. \(\int_{{\rm{ - }}\infty }^\infty {{\rm{f(x)}} \cdot {\rm{dx = 1}}} \)

Therefore, the value is obtained as \({\rm{k = }}\frac{{\rm{3}}}{{\rm{8}}}\).

03

Finding the probability

(b)

The probabilitythat lecture ends within \({\rm{1}}\) minute of the end of the hour is denoted as \({\rm{P(X < 1)}}\).

\(\begin{array}{c}{\rm{P(X < 1) = }}\int_{\rm{0}}^{\rm{1}} {\frac{{{\rm{3}}{{\rm{x}}^{\rm{2}}}}}{{\rm{8}}}} \cdot {\rm{dx}}\\{\rm{ = }}\left( {\frac{{{{\rm{x}}^{\rm{3}}}}}{{\rm{8}}}} \right)_{\rm{0}}^{\rm{1}}\\{\rm{ = }}\left( {\frac{{\rm{1}}}{{\rm{8}}}{\rm{ - 0}}} \right)\\{\rm{ = 0}}{\rm{.125}}\end{array}\)

Therefore, the value is obtained as \({\rm{0}}{\rm{.125}}\).

04

Finding the probability

(c)

The probability that lecture continues beyond the hour for between \({\rm{60}}\) and \({\rm{90}}\) seconds is denoted as \({\rm{P(X < 1}}{\rm{.5)}}\).

\(\begin{aligned}{\rm{P(1 < X < 1}}.5) &= \int_{\rm{1}}^{{\rm{1}}{\rm{.5}}} {\frac{{{\rm{3}}{{\rm{x}}^{\rm{2}}}}}{{\rm{8}}}} \cdot {\rm{dx}}\\ &= \left( {\frac{{{{\rm{x}}^{\rm{3}}}}}{{\rm{8}}}} \right)_{\rm{1}}^{{\rm{1}}{\rm{.5}}}\\ &= \left( {\frac{{{{{\rm{(1}}{\rm{.5)}}}^{\rm{3}}}}}{{\rm{8}}}{\rm{ - }}\frac{{\rm{1}}}{{\rm{8}}}} \right)\\ &= 0 {\rm{.2969}}\end{aligned}\)

Therefore, the value is obtained as \({\rm{0}}{\rm{.2969}}\).

05

Finding the probability

(d)

The probability that lecture continues at least \({\rm{90}}\) seconds beyond the end of the hour is denoted as \({\rm{P(X}} \ge {\rm{1}}{\rm{.5)}}\).

\(\begin{aligned}{\rm{P(X}} \ge {\rm{1}} .5) &= \int_{1.5}^2 {\frac{{{\rm{3}}{{\rm{x}}^{\rm{2}}}}}{{\rm{8}}}} \cdot {\rm{dx}}\\ &= \left( {\frac{{{{\rm{x}}^{\rm{3}}}}}{{\rm{8}}}} \right)_{1.5}^2\\ &= \left( {\frac{{{{(2)}^3}}}{{\rm{8}}}{\rm{ - }}\frac{{{{(1.5)}^3}}}{{\rm{8}}}} \right)\\ & = 0{\rm{.5781}}\end{aligned}\)

Therefore, the value is obtained as \({\rm{0}}{\rm{.5781}}\).

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

The temperature reading from a thermocouple placed in a constant-temperature medium is normally distributed with mean m, the actual temperature of the medium, and standard deviation. What would the value of s have to be to ensure that \(95\% \)of all readings are within \(.18\)of \(\mu \)?

Let X= the time between two successive arrivals at the drive-up window of a local bank. If X has an exponential distribution with \({\rm{\lambda = I}}\) (which is identical to a standard gamma distribution with \({\rm{\alpha = 1}}\) ), compute the following:

a. The expected time between two successive arrivals

b. The standard deviation of the time between successive arrivals

c. \({\rm{P(X}} \le {\rm{4)}}\)

d. \({\rm{P(2}} \le {\rm{X}} \le {\rm{5)}}\)

When a dart is thrown at a circular target, consider the location of the landing point relative to the bull’s eye. Let \({\rm{X}}\) be the angle in degrees measured from the horizontal, and assume that \({\rm{X}}\) is uniformly distributed on \(\left( {{\rm{0, 360}}} \right){\rm{.}}\)Define\({\rm{Y}}\)to be the transformed variable \({\rm{Y = h(X) = (2\pi /360)X - \pi ,}}\) so \({\rm{Y}}\) is the angle measured in radians and\({\rm{Y}}\)is between \({\rm{ - \pi and \pi }}\). Obtain \({\rm{E(Y)}}\)and\({{\rm{\sigma }}_{\rm{y}}}\)by first obtaining E(X) and \({{\rm{\sigma }}_{\rm{X}}}\), and then using the fact that \({\rm{h(X)}}\) is a linear function of \({\rm{X}}\).

The reaction time (in seconds) to a certain stimulus is a continuous random variable with pdf

\(f(x)= \left\{ {\begin{array}{*{20}{c}}{\frac{3}{2} \times \frac{1}{{{x^2}}}}&{1£x£3} \\0&{{\text{ }}otherwise{\text{ }}}\end{array}} \right.\)

a. Obtain the cdf.

b. What is the probability that reaction time is at most\({\rm{2}}{\rm{.5sec}}\)? Between \({\rm{1}}{\rm{.5}}\) and\({\rm{2}}{\rm{.5sec}}\)?

c. Compute the expected reaction time.

d. Compute the standard deviation of reaction time.

e. If an individual takes more than \({\rm{1}}{\rm{.5sec}}\) to react, a light comes on and stays on either until one further second has elapsed or until the person reacts (whichever happens first). Determine the expected amount of time that the light remains lit. (Hint: Let \({\rm{h(X) = }}\) the time that the light is on as a function of reaction time\({\rm{X}}\).)

The current in a certain circuit as measured by an ammeter is a continuous random variable \({\rm{X}}\) with the following density function:

\({\rm{f(x) = \{ }}\begin{array}{*{20}{c}}{{\rm{.075x + }}{\rm{.2}}}&{{\rm{3}} \le {\rm{x}} \le {\rm{5}}}\\{\rm{0}}&{{\rm{otherwise}}}\end{array}\)

a. Graph the pdf and verify that the total area under the density curve is indeed \({\rm{1}}\).

b. Calculate \({\rm{P(X}} \le {\rm{4)}}\). How does this probability compare to \({\rm{P(X < 4)}}\)?

c. Calculate \({\rm{P(3}}{\rm{.5}} \le {\rm{X}} \le {\rm{4}}{\rm{.5)}}\) and also \({\rm{P(4}}{\rm{.5 < X)}}\).

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.