Finding Expected Value with Joint Density: (3X+4Y)/(X+2Y)

In summary: The solution is E[Z]=5/2. Thanks for your help! In summary, the problem is to evaluate the expected value of Z, given a joint density function for (X,Y) and the definition of Z. After obtaining the marginal densities of X and Y, using the "theorem of the unconscious statistician" and integrating over suitable regions in the xy-plane, the solution is found to be E[Z]=5/2.
  • #1
nikki92
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Homework Statement


(X,Y) has joint density f_X,Y ( (x,y) = (3/16)(x+2y) for 0<y<x , 0< x<2

Evaluate E(Z) where Z = (3X+4Y)/(X+2Y)

Homework Equations


The Attempt at a Solution


Getting the marginal densities
f_X (x) =(3/8)x^2 for 0<x<2

f_Y (y) = (3/2)+(3/4)y for 0<y<2

Would I find the new distribution of 3X , 4Y, and 2Y then do the ratio distribution to solve for the distribution of Z? If this is correct, is there a shorter way?
 
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  • #2
E[3X+4Y/X+2Y]=3E[X/X+2Y]+4E[Y/X+2Y]

now use fx/z=f(x,z)/f(z) and integration over suitable rigions in the xy plane.
fy/z=f(y,z)/f(z)
recall that E[x/z]=integral(xfx/z)
 
  • #3
nikki92 said:

Homework Statement


(X,Y) has joint density f_X,Y ( (x,y) = (3/16)(x+2y) for 0<y<x , 0< x<2

Evaluate E(Z) where Z = (3X+4Y)/(X+2Y)

Homework Equations





The Attempt at a Solution


Getting the marginal densities
f_X (x) =(3/8)x^2 for 0<x<2

f_Y (y) = (3/2)+(3/4)y for 0<y<2

Would I find the new distribution of 3X , 4Y, and 2Y then do the ratio distribution to solve for the distribution of Z? If this is correct, is there a shorter way?

You don't need to find marginal distributions (which would be of no help anyway) and there is no need to find the distribution of Z (although doing so would not be harmful). Just use the so-called "theorem (or law) of the unconscious statistician"; use Google if you have never heard of this.

RGV
 
  • #4
I tought the symbol / denotes conditional variable,but maybe you ment fraction.
 
  • #5
Oh yeah! Using the marginals would lose information.

So E[Z]=integral 0 to 2 integral 0 to x (3x+4y)/(x+2y) (3/16)(x+2y) dy dx = integral 0 to 2 (3/16)(3x+4y) dx = 5/2 Am I using the Unconscious Statistician theorem correctly? Thanks for all the help! <3
 
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  • #6
nikki92 said:
Oh yeah! Using the marginals would lose information.

So E[Z]=integral 0 to 2 integral 0 to x (3x+4y/x+2y) (3/16)(x+2y) dy dx = integral 0 to 2 3x^2(3x+1)/8 dx = 5/2 Am I using the Unconscious Statistician theorem correctly? Thanks for all the help! <3

Well, you should write (3x+4y)/(x+2y), not (3x+4y/x+ 2y) --- because this last means
[tex] 3x + \frac{4y}{x} + 2y.[/tex]
Also, when I do the y-integral I do not get 3x^2(3x+1)/8. This last integrates to 11/2 as x goes from 0 to 2, so I don't know how you get 5/2. (The 5/2 is correct; your computation is not.)

RGV
 
  • #7
Ray Vickson said:
Well, you should write (3x+4y)/(x+2y), not (3x+4y/x+ 2y) --- because this last means
[tex] 3x + \frac{4y}{x} + 2y.[/tex]
Also, when I do the y-integral I do not get 3x^2(3x+1)/8. This last integrates to 11/2 as x goes from 0 to 2, so I don't know how you get 5/2. (The 5/2 is correct; your computation is not.)

RGV

Thanks again! I typed the function incorrectly, but wrote it down correctly.
 

FAQ: Finding Expected Value with Joint Density: (3X+4Y)/(X+2Y)

What is the expected value distribution?

The expected value distribution, also known as the mean, is a measure of central tendency in a probability distribution. It represents the average value of a random variable in a given population or sample.

How do you calculate the expected value distribution?

To calculate the expected value distribution, you multiply each possible outcome of a random variable by its probability of occurring, and then sum all of these products together. This can also be expressed as the sum of the product of each possible outcome and its corresponding probability.

What is the purpose of the expected value distribution?

The expected value distribution is used to summarize and describe the likelihood of different outcomes in a probability distribution. It can also be used to make predictions and decisions based on the expected outcome.

Can the expected value distribution be negative?

Yes, the expected value distribution can be negative. This can occur when the outcomes of a random variable have a higher probability of being negative than positive, or when there are extreme values in the distribution that skew the overall average.

How is the expected value distribution related to the variance?

The expected value distribution and the variance are both measures of central tendency in a probability distribution. The variance measures the spread or variability of the data around the mean, while the expected value distribution represents the average or central value of the data. Together, they provide a more complete understanding of the distribution of a random variable.

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