Standard normal distribution help

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SUMMARY

The discussion focuses on calculating the expected values E(Z^3) and E(Z^4) for a standard normal distribution where Z has a mean (μ) of 0 and a standard deviation (σ) of 1. The probability density function is defined as f(z) = e^(-z^2/2)/sqrt(2π). The user attempted to compute these expected values using integrals from -∞ to ∞, specifically ∫Z^3 * f(z) dz and ∫Z^4 * f(z) dz, but expressed uncertainty about the correctness of their approach.

PREREQUISITES
  • Understanding of standard normal distribution properties
  • Knowledge of expected value calculations
  • Familiarity with integration techniques
  • Basic grasp of probability density functions
NEXT STEPS
  • Review the derivation of E(Z^3) and E(Z^4) for standard normal distributions
  • Study the properties of odd and even moments in probability distributions
  • Learn about the use of integration by parts in calculating expected values
  • Explore the implications of symmetry in standard normal distributions
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Students studying statistics, mathematicians working with probability distributions, and educators teaching concepts related to normal distributions and expected values.

whitedragonxx
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Homework Statement



Let Z follow a standard normal distribution. Compute E(Z^3) and E(Z^4)

Homework Equations



I know that mu is 0 and sigma is 1. And f(z) = e^(-z^2/2)/sqrt(2pi)

The Attempt at a Solution



I tried to take the integral of ∫Z^3 * f(z) from the integral from -∞ to ∞
and did the same for E(Z^4) where the Z^3 is replaced by Z^4, but I don't know if I am doing the problem right or wrong.
 
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whitedragonxx said:

Homework Statement



Let Z follow a standard normal distribution. Compute E(Z^3) and E(Z^4)

Homework Equations



I know that mu is 0 and sigma is 1. And f(z) = e^(-z^2/2)/sqrt(2pi)

The Attempt at a Solution



I tried to take the integral of ∫Z^3 * f(z) from the integral from -∞ to ∞
and did the same for E(Z^4) where the Z^3 is replaced by Z^4, but I don't know if I am doing the problem right or wrong.

Well, show us what you did.

RGV
 

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