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The joint density function ofXandYis given by

f(x,y)=1ye-(y+x/y),x>0,y>0

Find E[X],E[Y]and show that Cov(X,Y)=1

Short Answer

Expert verified

The value of E[X]=1

The value of E[Y]=1

The value ofCov(X,Y)=1

Step by step solution

01

Given Information

The density of Xand Yis f(x,y)=1ye-(y+x/y),x>0,y>0

E[X]=?

E[Y]=?

Cov(X,Y)=?

02

Explanation

Calculate the value ofE[X],

E[X]=00x1ye-y+xydydx

=0e-y0xye-xydxdy

=0ye-ydy

=ye-y-10-0e-y-1dy

=0+1

=1

03

Explanation

Calculate the value of E[Y],

E[Y]=00y1ye-y+xydxdy

=00e-ye-xydxdy

=0e-y0e-xydxdy

=0e-yydy

=1

04

Explanation

Calculate the value of Cov(X,Y),

E(XY)=00xy1ye-y+xydxdy

=0e-y0xe-xydxdy

=0e-yxe-xy-1y0-0e-xy-1ydxdy

=0e-y0+y2dy

=0y2e-ydy

=-y2e-y0+02ye-ydy

=0+2=2

05

Final Answer

HenceCov(X,Y)=E(XY)-E(X)E(Y)=2-1.1

=2-1

=1

So, the value ofCov(X,Y)=1

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Most popular questions from this chapter

There are n items in a box labeled H and m in a box labeled T. A coin that comes up heads with probability p and tails with probability 1 鈭 p is flipped. Each time it comes up heads, an item is removed from the H box, and each time it comes up tails, an item is removed from the T box. (If a box is empty and its outcome occurs, then no items are removed.) Find the expected number of coin flips needed for both boxes to become empty. Hint: Condition on the number of heads in the first n + m flips.

The number of accidents that a person has in a given year is a Poisson random variable with mean 蹋 However, suppose that the value of changes from person to person, being equal to 2for 60percent of the population and 3for the other 40percent. If a person is chosen at random, what is the probability that he will have

(a) 0accidents and,

(b) Exactly 3accidents in a certain year? What is the conditional probability that he will have3 accidents in a given year, given that he had no accidents the preceding year?

A deck of n cards numbered 1 through n is thoroughly shuf铿俥d so that all possible n! orderings can be assumed to be equally likely. Suppose you are to make n guesses sequentially, where the ith one is a guess of the card in position i. Let N denote the number of correct guesses.

(a) If you are not given any information about your earlier guesses, show that for any strategy, E[N]=1.

(b) Suppose that after each guess you are shown the card that was in the position in question. What do you think is the best strategy? Show that under this strategy

E[N]=1n+1n1++11n1xdx=logn

(c) Supposethatyouaretoldaftereachguesswhetheryou are right or wrong. In this case, it can be shown that the strategy that maximizes E[N] is one that keeps on guessing the same card until you are told you are correct and then changes to a new card. For this strategy, show that

E[N]=1+12!+13!++1n!e1

Hint: For all parts, express N as the sum of indicator (that is, Bernoulli) random variables.

A bottle initially contains m large pills and n small pills. Each day, a patient randomly chooses one of the pills. If a small pill is chosen, then that pill is eaten. If a large pill is chosen, then the pill is broken in two; one part is returned to the bottle (and is now considered a small pill) and the other part is then eaten.

(a) Let X denote the number of small pills in the bottle after the last large pill has been chosen and its smaller half returned. Find E[X].

Hint: De铿乶e n + m indicator variables, one for each of the small pills initially present and one for each of the small pills created when a large one is split in two. Now use the argument of Example 2m.

(b) Let Y denote the day on which the last large pills chosen. Find E[Y].

Hint: What is the relationship between X and Y?

Let be the standard normal distribution function, and let X be a normal random variable with mean 渭 and variance 1. We want to find E[ (X)]. To do so, let Z be a standard normal random variable that is independent of X, and let

I=1,鈥呪赌呪赌呪赌ifZ<X0,鈥呪赌呪赌呪赌ifZX

(a) Show that E[IX=x]=(x).

(b) Show that E[(X)]=P{Z<X}.

(c) Show that E[(X)]=2.

Hint: What is the distribution of X-Z?

The preceding comes up in statistics. Suppose you are about to observe the value of a random variable X that is normally distributed with an unknown mean 渭 and variance 1, and suppose that you want to test the hypothesis that the mean 渭 is greater than or equal to 0. Clearly you would want to reject this hypothesis if X is sufficiently small. If it results that X = x, then the p-value of the hypothesis that the mean is greater than or equal to 0 is defined to be the probability that X would be as small as x if 渭 were equal to 0 (its smallest possible value if the hypothesis were true). (A small p-value is taken as an indication that the hypothesis is probably false.) Because X has a standard normal distribution when 渭 = 0, the p-value that results when X = x is (x). Therefore, the preceding shows that the expected p-value that results when the true mean is 渭 is 2 .

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