Rewriting the fourth moment in terms of

In summary, the right hand side of the expression can be rewritten as E[(X-μ)^4] = E[X^4] - 4[E[X]][E[X^3]] + 6[E[X]^2][E[X^2]] - 3σ^4 + 2μ^4, involving μ, σ^2, S, and μ^4. Thank you again for your post and happy problem-solving!
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rewriting the fourth moment in terms of...

Homework Statement


X is a random variable with moments E[X], E[X^2], E[X^3]. Suppose var(X)= σ^2, and that skewness is given as, S=[E(X-μ)^3)]/(σ^3) Rewrite the right hand side of the expression(see below) in terms of μ, σ^2, S and μ4

Homework Equations



E[X-μ]^4 = E(X^4) - 4[E(X)][E(X^3)] + 6[E(X)]^2[E(X^2)] - 3[E(X)]^4


The Attempt at a Solution


E[(X^4)] = μ, that's all I've got.
I've been coming back to this all day and I can't see where σ^2, S and μ4 fit into the right side of the equation. Can anyone please give me an idea? Thanks.
 
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Dear fellow scientist,

Thank you for your post. The right hand side of the expression can be rewritten as follows:

E[(X-μ)^4] = E[X^4 - 4X^3μ + 6X^2μ^2 - 4Xμ^3 + μ^4] (using the binomial expansion of (X-μ)^4)

= E[X^4] - 4E[X^3]μ + 6E[X^2]μ^2 - 4E[X]μ^3 + μ^4 (using linearity of expectation)

= E[X^4] - 4[E[X]][E[X^3]] + 6[E[X]^2][E[X^2]] - 4[E[X]^3] + [E[X]^4] (using properties of expectation and the given moments)

= E[X^4] - 4[E[X]][E[X^3]] + 6[E[X]^2][E[X^2]] - 3[E[X]^4] + 2[E[X]^4] (adding and subtracting [E[X]^4])

= E[X^4] - 4[E[X]][E[X^3]] + 6[E[X]^2][E[X^2]] - 3[E[X]^4] + 2μ^4 (since E[X]^4 = μ^4)

= E[X^4] - 4[E[X]][E[X^3]] + 6[E[X]^2][E[X^2]] - 3[E[X]^4] + 2μ^4 (using the given definition of skewness)

= E[X^4] - 4[E[X]][E[X^3]] + 6[E[X]^2][E[X^2]] - 3σ^4 + 2μ^4 (since var(X) = σ^2)

Therefore, the right hand side of the expression can be rewritten as E[(X-μ)^4] = E[X^4] - 4[E[X]][E[X^3]] + 6[E[X]^2][E[X^2]] - 3σ^4 + 2μ^4. This expression involves μ, σ^2, S, and μ^4, as desired.

I hope this helps clarify the solution for you. Let me know
 

What does "rewriting the fourth moment in terms of " mean?

Rewriting the fourth moment in terms of a variable means expressing the fourth moment of a probability distribution in terms of that variable. This allows for easier analysis and comparison of different distributions.

Why is rewriting the fourth moment important in statistics?

Rewriting the fourth moment in terms of a variable allows for simplification and generalization of statistical formulas, making them applicable to a wider range of distributions. It also helps in determining the properties of a distribution, such as its skewness and kurtosis.

What is the relationship between the fourth moment and the standard deviation?

The fourth moment is related to the standard deviation through the formula σ^4 = μ4 - 3(σ^2)^2. This formula shows that the fourth moment is a measure of the spread of the distribution, with a higher fourth moment indicating a more spread out distribution.

How is the fourth moment used in hypothesis testing?

The fourth moment is used in hypothesis testing to determine the shape and characteristics of a distribution. It is also used in calculating the skewness and kurtosis of a distribution, which can be compared to theoretical values to test the validity of a hypothesis.

Can the fourth moment be negative?

Yes, the fourth moment can be negative. This indicates a distribution with a negative kurtosis, also known as a platykurtic distribution. This means that the distribution has thinner tails and a flatter peak compared to a normal distribution.

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