/*! 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} Q. 6.3 The joint density of X and Y is ... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

The joint density of X and Y is given by

f(x,y)=C(y-x)e-y-y<x<y,0<y<∞

(a) Find C.

(b) Find the density function of X.

(c) Find the density function of Y.

(d) Find E[X].

(e) Find E[Y].

Short Answer

Expert verified

(a) The value of C is 14.

(b) The density function of X is e-x4.

(c) The density function of Y is 12y2e-y.

(d) The value of EXis-1.

(e) The value ofEYis3.

Step by step solution

01

Given information (part a)

The function is f(x,y)=C(y-x)e-y-y<x<y,0<y<∞

02

Explanation (part a)

The joint density of X and Y is,

f(x,y)=c(y-x)e-y-y<x<y,0<y<∞0Otherwise

The value of C is,

C∫y=0∞e-y∫x=-yy(y-x)dxdy=1

C∫y=0∞e-yxy-x22x=-yydy=1

C∫y=0∞e-yy(y-(-y))-12y2-(-y)2dy=1

C∫y=0∞e-yy(y+y)-12y2-y2dy=1

C∫y=0∞e-y2y2dy=1

2C∫y=0∞y2e-ydy=1

2C2=14C=1C=14

03

Given information (part b)

The function isf(x,y)=C(y-x)e-y-y<x<y,0<y<∞.

04

Explanation (part b)

The density function of X is,

fx(x)=14∫x∞(y-x)e-ydyfx(x)=14∫x∞ye-y-xe-ydy=14∫x∞ye-ydy-∫x∞xe-ydy=14-ye-y-e-yx∞+xe-yx∞=14-ye-y-e-y+xe-yxα=14-0+xe-x-0-e-x+x0-e-x=e-x4

05

Given information (part c)

The function isf(x,y)=C(y-x)e-y-y<x<y,0<y<∞

06

Explanation (part c)

The density function of Y is,

fy(y)=14e-y∫x=-yy(y-x)dx=14e-yyx-x22x=-yy=14e-yy(y-(-y))-12y2-(-y)2=14e-yy(y+y)-12y2-y2=12y2e-y

07

Given information (part d)

The function isf(x,y)=C(y-x)e-y-y<x<y,0<y<∞

08

Explanation (part d)

The value of EXis,

E[X]=∫x=-∞∞xf(x)dx=14∫x=-∞∞xe-xdx+∫-∞0-2x2ex+xexdx=14∫x=-∞∞x2-1e-xdx-∫0∞-2y2e-y+ye-ydy=14Γ(2)1-∫0∞2y2e-ydy+∫0∞ye-ydy=141-2∫0∞y3-1e-ydy-∫0∞y2-1e-ydy=141-2Γ(3)1-Γ(2)1=14[1-2(2!)-1!]=-1

09

Given information (part e)

The function isf(x,y)=C(y-x)e-y-y<x<y,0<y<∞

10

Explanation (part e)

The value of EYis,

E[Y]=∫y=-∞∞yf(y)dy=12∫0∞yy2e-ydy=12∫0∞y4-1e-ydy=12Γ(4)14=123!=3

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

If X and Y are independent binomial random variables with identical parameters n and p, show analytically that the conditional distribution of X given that X + Y = m is the hypergeometric distribution. Also, give a second argument that yields the same result without any computations. Hint: Suppose that 2n coins are flipped. Let X denote the number of heads in the first n flips and Y the number in the second n flips. Argue that given a total of m heads, the number of heads in the first n flips has the same distribution as the number of white balls selected when a sample of size m is chosen from n white and n black balls

Let N be a geometric random variable with parameter p. Suppose that the conditional distribution of X given that N = n is the gamma distribution with parameters n and λ. Find the conditional probability mass function of N given that X = x.

A bin of 5 transistors is known to contain 2 that are defective. The transistors are to be tested, one at a time, until the defective ones are identified. Denote by N1 the number of tests made until the first defective is identified and by N2 the number of additional tests until the second defective is identified. Find the joint probability mass function of N1 and N2.

Let X1,X2,...be a sequence of independent uniform (0,1)random variables. For a fixed constant c, define the random variable N by N=min{n:Xn>c}Is N independent ofXN? That is, does knowing the value of the first random variable that is greater than c affect the probability distribution of when this random variable occurs? Give an intuitive explanation for your answer.

Consider a sequence of independent Bernoulli trials, each of which is a success with probability p. Let X1 be the number of failures preceding the first success, and let X2 be the number of failures between the first two successes. Find the joint mass function of X1 and X2.

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.