SUMMARY
The expected value of \( Y^2 \) for the transformed uniform variable \( Y = 1 - X^2 \) where \( X \sim U(0,1) \) is calculated to be \( E(Y^2) = \frac{8}{15} \). The incorrect options presented were \( E(Y^2) = 2 \) and \( E(Y^2) = \frac{1}{2} \). The variance of \( Y \) is derived as \( var(Y) = -var(X^2) \), with \( var(X) = \frac{1}{12} \). The calculations for \( E(X^2) \) and \( E(X^4) \) utilize the definitions of expected value and variance, confirming \( E(X^2) = \frac{1}{3} \).
PREREQUISITES
- Understanding of expected value and variance in probability theory
- Familiarity with uniform distributions, specifically \( U(0,1) \)
- Knowledge of integration techniques for calculating expected values
- Ability to manipulate algebraic expressions involving random variables
NEXT STEPS
- Study the properties of uniform distributions and their transformations
- Learn about the calculation of expected values for non-linear transformations
- Explore variance calculations for transformed random variables
- Review integration techniques for probability density functions
USEFUL FOR
Mathematicians, statisticians, and students studying probability theory, particularly those focusing on transformations of random variables and their expected values.