Probability- expected value of Z, where z= X/(1+y)^2

In summary, the problem involves finding the expected value of the random variable Z, which is equal to X divided by (1+Y)^2 where X and Y are independent random variables with a uniform distribution between 0 and 1. Using the formula E[g(X,Y)] = \iint{g(x,y)f_{X,Y}(x,y)dxdy}, we can calculate the expected value by integrating over the range of X and Y. We can also use the theorem to transform the random variable W = 1/(1+Y)^2 and find its density function in order to solve the problem.
  • #1
Roni1985
201
0

Homework Statement



X & Y are independent r.v.s with uniform distribution between 0 and 1.

Z= X/(1+Y)^2

find E[Z].

Homework Equations





The Attempt at a Solution



Here is what I did.

E[Z]= E[X]*E[1/(1+Y)^2]
E[X]=1/2
E[1/(1+Y)^2]=?
I think that once I know the distribution of (1+Y)^(-2), I'll be able to find the answer. Is it 1/(1+Y)^2~ U(1,2) ?
 
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  • #2
You can also use this theorem:

[tex]E[g(X,Y)]=\iint{g(x,y)f_{X,Y}(x,y)dxdy}[/tex]

So, in your case, this comes down to

[tex]\int_0^1\int_0^1\frac{x}{1+y^2}dxdy[/tex]
 
  • #3
micromass said:
You can also use this theorem:

[tex]E[g(X,Y)]=\iint{g(x,y)f_{X,Y}(x,y)dxdy}[/tex]

So, in your case, this comes down to

[tex]\int_0^1\int_0^1\frac{x}{1+y^2}dxdy[/tex]


Hey,

thanks for the answer.

Is 1+y^2 a typo in your answer?
so adding a constant to a uniform r.v. doesn't change it's distribution?
 
  • #4
Roni1985 said:
Is 1+y^2 a typo in your answer?

Yes

so adding a constant to a uniform r.v. doesn't change it's distribution?

Obviously it does change the distribution. But I don't see how that is important here.
 
  • #5
Roni1985 said:

Homework Statement



X & Y are independent r.v.s with uniform distribution between 0 and 1.

Z= X/(1+Y)^2

find E[Z].

Homework Equations





The Attempt at a Solution



Here is what I did.

E[Z]= E[X]*E[1/(1+Y)^2]
E[X]=1/2
E[1/(1+Y)^2]=?
I think that once I know the distribution of (1+Y)^(-2), I'll be able to find the answer. Is it 1/(1+Y)^2~ U(1,2) ?

If W = 1/(1+Y)^2, then for any w > 0 we have P{W <= w} = P{1/(1+Y)^2 <= w} = P{-sqrt(w) <= 1/(1+Y) <= sqrt(w)}. The left-hand inequality -sqrt(w) <= 1/(1+Y) holds automatically because Y >= 0, so P{W <= w} = P{1/(1+Y) <= sqrt(w)} = P{Y >= -1 + 1/sqrt(w)}. Since Y ~ U(0,1), we can easily get P{W <= w} and hence can get the density function of W. Alternatively: just apply the standard formula for the density of a transformed random variable.

RGV
 
  • #6
micromass said:
Obviously it does change the distribution. But I don't see how that is important here.
Well I was thinking that now its uniformly distributed from 1 to 2... isn't it?

EDIT:
Oh right its still uniform from 0 to 1...
omg... I'm so rusty :\

Thanks.
 
Last edited:
  • #7
Roni1985 said:
Well I was thinking that now its uniformly distributed from 1 to 2... isn't it?

EDIT:
Oh right its still uniform from 0 to 1...
omg... I'm so rusty :\

Thanks.

You have all the information you need to work out the answer for yourself.

RGV
 

1. What is the formula for calculating the expected value of Z?

The formula for calculating the expected value of Z is E(Z) = E(X/(1+y)^2), where E() represents the expected value, X is the random variable, and y is a constant.

2. How is the expected value of Z related to probability?

The expected value of Z is a measure of central tendency and is used to calculate the average value of a random variable X. It is related to probability because it takes into account the probabilities of different outcomes of X occurring.

3. Can the expected value of Z be negative?

Yes, the expected value of Z can be negative. This means that the average value of the random variable X is less than 0.

4. What does the expected value of Z tell us about the distribution of X?

The expected value of Z can tell us about the center or average of the distribution of X. If the expected value is close to 0, it means that the distribution is centered around 0. If the expected value is positive, it means that the distribution is skewed towards positive values, and vice versa.

5. How can the expected value of Z be used in decision making?

The expected value of Z can be used in decision making to determine the potential outcomes of a situation. For example, in a business decision, the expected value of Z can help in estimating the potential profits and making a decision based on that information.

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