4th moment, show that E[X-mu]^4 is equal to

  • Thread starter alias
  • Start date
  • Tags
    Moment
In summary, The problem involves proving a statement about a random variable X with moments E[X], E[X^2], E[X^3], and so forth. The expansion of (X-mu)^4 using the binomial theorem is needed, and no split for continuous/discrete cases is necessary. The question asks to show each case individually and involves working out specific summations and integrals.
  • #1
alias
46
0

Homework Statement


X is a random variable with moments, E[X], E[X^2], E[X^3], and so forth. Prove this is true for i) X is discrete, ii) X is continuous


Homework Equations


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

The Attempt at a Solution


I've been trying to generalize expanding the variance, E[(X-mu)^2], into the above result with no success. Not sure about the discrete and continuous proofs either. Anyone have any ideas?
 
Physics news on Phys.org
  • #2
EX = m is a number, not a random quantity. So, expand (X-m)^4 using the binomial theorem.

RGV
 
  • #3
I got the expansion, thanks. I'm not sure why I need to split the cases for continuous/discrete either. It's asked in the question and I don't know how to prove with continuous/discrete separately.
 
  • #4
alias said:
I got the expansion, thanks. I'm not sure why I need to split the cases for continuous/discrete either. It's asked in the question and I don't know how to prove with continuous/discrete separately.

No such split is needed.

RGV
 
  • #5
Ray Vickson said:
No such split is needed.

RGV

I know that the one equation will cover both the discrete and continuous cases, but the question specifically asks to show each case individually and I'm not sure what the specific summation and integral that I have to work out is.
 

FAQ: 4th moment, show that E[X-mu]^4 is equal to

What is the 4th moment in statistics?

The 4th moment is a measure of the variability or dispersion of a probability distribution. It is calculated as the expected value of the 4th power of the deviation of a random variable from its mean (μ).

What does the 4th moment tell us about a distribution?

The 4th moment provides information about the shape of a distribution. A higher 4th moment indicates a more spread out distribution, while a lower 4th moment indicates a more concentrated distribution.

How is the 4th moment calculated?

The 4th moment (μ4) is calculated by taking the expected value of the 4th power of the deviation of a random variable (X) from its mean (μ), or E[(X-μ)^4]. This can also be written as the 4th central moment, or the 4th moment about the mean.

Why is the 4th moment important in statistics?

The 4th moment is important because it provides a measure of the kurtosis, or the level of peakedness or flatness, of a distribution. It is also used in calculations for higher moments, such as the skewness and standard deviation.

How does the 4th moment relate to the other moments?

The 4th moment is one of the central moments, along with the 1st, 2nd, and 3rd moments. It is also related to the moments about the mean, which include the 2nd, 3rd, and 4th moments. These moments can be used to calculate other statistics, such as the skewness and kurtosis.

Similar threads

Back
Top