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.