Probability Density Function - Need Help

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The discussion revolves around finding the probability density function (PDF) of W = X + Y, given the joint PDF of X and Y. The initial attempt at solving the problem involved a double integral but yielded incorrect results, specifically a negative value for the PDF when w < 1/2. After reevaluation, the correct regions for the cumulative distribution function (CDF) were established, leading to a proper derivation of the PDF. The final results indicate that the PDF is fw(w) = w for 0 ≤ w ≤ 1, fw(w) = 2 - w for 1 ≤ w ≤ 2, and fw(w) = 0 otherwise. The solution was confirmed to be correct by multiple participants in the discussion.
vptran84
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Probability Density Function -- Need Help!

Hi,

Can someone please check my work if i did the problem correctly? thanks in advance.

Here is the problem:

Find the PDF of W = X + Y when X and Y have the joint PDF fx,y (x,y) = 2 for 0<=x<=y<=1, and 0 otherwise.

here is my solution:
<br /> \int_{0}^{1} \int_{0}^{w-y} 2dxdy<br />

I work through the integral and get fw (w) = 2w-1 for w>0, and 0 for w<0.
 
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Your answer is obviously wrong. f(w) is <0 for w<1/2. Moreover, the integral should be 1 - yours is 0.
 
ok, i did a little more thinking :-p and this is what i got now...

For region w>0, the region of integration is outside so CDF Fw (w) is 0

For region 0<=w<=1, i used double integration, and i get w^2/2

For region 1<=w<=2, i get 2w-1-w^2/2

For region w>2, i get 1.

So to find PDF, i take the derivative, and i get the following:

fw(w) = w for 0<=w<=1
fw(w) = 2-w for 1<=w<=2
fw(w) = 0 otherwise.

Please let me know if i did anything wrong.
 
Before I looked at your latest post, I worked it out myself. I got the same result as you did.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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