Proofing Varience in Gamma Distribution

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    Distribution Gamma
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Homework Help Overview

The discussion revolves around proving the variance of the function 1/y in the context of the gamma distribution. The original poster presents a specific probability density function and seeks to find the expected value E(1/y^2) and prove that Var(1/y) equals λ^2 / 4.

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants explore the relationship between the gamma distribution and the expectations of functions of the random variable y. There are attempts to set up integrals for E(1/y^2) and clarify misunderstandings about variance calculations. Some participants question the setup of the integrals and the interpretation of the functions involved.

Discussion Status

Participants are actively engaging with the problem, with some providing insights into the integration process for expectations. There is recognition of misunderstandings in the calculations, and a few participants are attempting to clarify the steps involved in finding E(1/y^2) and its implications for variance.

Contextual Notes

There appears to be confusion regarding the distinction between the variance of y and the variance of 1/y, as well as the proper setup of integrals for expected values. Participants are working within the constraints of the problem as posed, with a focus on mathematical rigor.

Philip Wong
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Homework Statement


Fy(y) = 1/2 λ^3 y^2 e^(-λy)

Assume E(1/y) = λ/2
Find E(1/y^2) and prove Var(1/y) = λ^2 / 4


Homework Equations


E(y) = k/λ
Var (y) = k/ λ^2

The Attempt at a Solution


Hi guys,

From the equation given from gamma distribution, I understands clearly that to get Var(y) what I have to do is to square y. i.e. 1/y^2 = (λ/2)^2 = λ^2 / 4. Which is equivalent to E(1/y^2)

but I'm no clue on how to set up a proof to show this is correct. I needed help on this, as I have no idea at all on how to proof this.
 
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the problem doesn't ask fior var(y) it asks for var(1/y)

the expectation of any function of y, say g(y), can be written
E(g(y)) = \int g(y) f_Y(y)dy

so for E(1/y^2) this gives
E(\frac{1}{y^2}) = \int \frac{1}{y^2} f_Y(y)dy
 
Ok I've retried to do E(X). Can you please check if I have done it correctly before I moved on trying to work out the variance.

E(1/y^2) = ∫1/y^2 fy(y) dy
= ∫ 1/4 * λ^2 * 1/2 * λ^3 * y^2 * e^-λy dy
=∫1/8 * λ^5 * y^2 * e^-λy dy
=1/8 * 1/3 * λ^5 * y^3 * e^-λy - 1/λ * λ^6 * y^2 * e^-λy
=1/8 * 1/3 * λ^5 * y^3 * e^-λy - λe^-λy * λ^5 * y^2
=1/24 * λ^5 * y^3 * e^-λy - λ^6 * y^2 * e^-λy
=1/24 * λ^5 * y^2 * e^-λy (y-λ)

Up to this point I knew I did something wrong. But I run over my calculation again, but doesn't seem to be able to pick out where I did wrong.

It would be nice if you can pick out where I did wrong. thanks
 
what happened to the 1/y^2 in the 2nd line?
 
ar! I see where I get wrong now. I misunderstood it as (EX)^2 and substitute in the its value as 1/y^2. I got it now. should be

∫1/y^2 * fx(x) dy
∫1/y^2 * 1/2 λ^3 * y^2 * e(-λy) dy
∫1/2 * λ^3 * e(-λy) dy

= 1/2 λ^3 - 1/λ e(-λy)
 
bit hard to read or follow, but if you mean times (-1/gamma) you're on the right track
 

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