What Is the Expected Value of Y Squared for a Transformed Uniform Variable?

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Discussion Overview

The discussion revolves around calculating the expected value of \( Y^2 \) for a transformed uniform variable, specifically where \( Y = 1 - X^2 \) and \( X \) follows a uniform distribution on the interval \( (0, 1) \). Participants explore various statements regarding the expected value, variance, and relationships between these quantities, engaging in mathematical reasoning and calculations.

Discussion Character

  • Mathematical reasoning
  • Technical explanation
  • Exploratory

Main Points Raised

  • Some participants propose that \( E(Y^2) \) can be computed as \( E(1 - X^2)^2 = E(1 + X^4 - 2X^2) \), leading to the expression \( 1 + E(X^4) - 2E(X^2) \).
  • One participant calculates \( E(X^2) \) using the integral definition of expected value, resulting in \( E(X^2) = \int_0^1 x^2 \cdot 1 \, dx \).
  • Another participant provides specific values for \( E(X^2) = \frac{1}{3} \) and \( E(X^4) = \frac{1}{5} \), leading to the conclusion that \( E(Y^2) = \frac{8}{15} \), which contradicts the first two proposed statements regarding \( E(Y^2) \).
  • There is a discussion about the variance of \( Y \), with one participant stating \( var(Y) = var(1 - X^2) = 0 - var(X^2) \) and calculating \( var(X) = \frac{1}{12} \).
  • Another participant questions how to derive the expected values \( E(X^2) \) and \( E(X^4) \) using a general formula.

Areas of Agreement / Disagreement

Participants express differing views on the values of \( E(Y^2) \) and the validity of the proposed statements. There is no consensus on the correctness of the initial claims regarding \( E(Y^2) \) or the variance of \( Y \), indicating multiple competing views remain.

Contextual Notes

Participants rely on specific calculations and definitions of expected value and variance, but there are unresolved steps in the derivations and potential dependencies on assumptions that are not fully articulated.

Who May Find This Useful

This discussion may be of interest to those studying probability theory, particularly in the context of transformations of random variables and the computation of expected values and variances.

Francobati
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Hello.
Let $ Y=1-X^2 $, where $ X~ U(0,1) $. What statement is TRUE?
-$ E(Y^2)=2 $
- $ E(Y^2)=1/2 $
- $ var(Y)=1/12 $
- $ E(Y)=E(Y^2) $
-None of the remaining statements.
Solution:
I compute: $ E(Y^2)=E(1-X^2)^2=E(1+X^4-2X^2)=1+E(X^4)-2E(X^2) $, then?
 
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Francobati said:
Hello.
Let $ Y=1-X^2 $, where $ X~ U(0,1) $. What statement is TRUE?
-$ E(Y^2)=2 $
- $ E(Y^2)=1/2 $
- $ var(Y)=1/12 $
- $ E(Y)=E(Y^2) $
-None of the remaining statements.
Solution:
I compute: $ E(Y^2)=E(1-X^2)^2=E(1+X^4-2X^2)=1+E(X^4)-2E(X^2) $, then?

Apply the definition of expected value.
That is:
$$EZ = \int z f_Z(z) \, dz$$
So with $X\sim U(0,1)$:
$$E(X^2) = \int_0^1 x^2 \cdot 1 \, dx$$
 
$ E(X^2)=\frac{1^3-0^3}{3*1} $
$ E(X^4)=\frac{1^5}{5}$
$ E(Y^2)=1+\frac{1}{5}-2*\frac{1}{3}=1+\frac{1}{5}-\frac{2}{3}=\frac{15+3-10}{15}= \frac{8}{15}\ne2\ne\frac{1}{2} $, so first and second are false.
$ var(Y)=var(1-X^2)=var(1)-var(X^2)=0-var(X^2) $
$ var(X)=\frac{(b-a)^2}{12}=\frac{(1-0)^2}{12}=\frac{1}{12} $
But what formula I must appky in $E(X^2)$ and in $E(X^4)$ to obtain these values?
 
I take it you mean $var(X^2)$?

To find it, apply the definition of variance:
$$var(Z) = E\big((Z-EZ)^2\big) = E\big(Z^2\big) - (EZ)^2$$
 
Yes and I obtain $E(X^2)=var(X)+(E(X))^2=\frac{(b-a)^2}{12}+(\frac{a+b}{2})^2=\frac{1}{12}+(\frac{1}{2})^2=\frac{1}{12}+\frac{1}{4}=\frac{1}{3}$
This result equal to this $E(X^2)=\frac{1^3-0^3}{3(1)}= \frac{1}{3}$, how I can translate this $E(X^2)=\frac{1^3-0^3}{3(1)}$ in a general formula?
 

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