Calculating the mean and variance from a moment generating function

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The discussion revolves around calculating the expected value and variance of a squared-Chi-distributed random variable using its moment generating function (mgf). The user initially misinterprets the second derivative of the mgf, mistakenly equating it to the variance instead of the second moment. The correct approach shows that the variance can be derived from the relationship var(X) = E(X^2) - (E(X))^2. After correcting the calculations, it is established that the variance of a squared-Chi distribution is indeed 2k, aligning with textbook information. This clarification resolves the confusion regarding the variance calculation.
Charlotte87
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Homework Statement


Assume that X is squared-Chi-distributed, which means that the moment generating function is given by:

m(t)=(1-2t)^{-k/2}

Use the mgf to find E(X) and var(X)

The Attempt at a Solution


I know that m'(0)=E(X), and m''(0)=var(X).

So I find:

m'(t)=k(1-2t)^{-(k/2)-1}
which gives m'(0)=k

Similarily, I find

m''(t)=(k^{2}+2k)(1-2t)^{-(k/2)-2}
which gives m''(0)=k^2+2k

However, in my textbook, it says that the variance of a square-chi distribution should be 2k, not k^2. Where do I go wrong?
 
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Charlotte87 said:

Homework Statement


Assume that X is squared-Chi-distributed, which means that the moment generating function is given by:

m(t)=(1-2t)^{-k/2}

Use the mgf to find E(X) and var(X)

The Attempt at a Solution


I know that m'(0)=E(X), and m''(0)=var(X).

Your mistake is right there. m''(0) = E(X2), not var(X)

So I find:

m'(t)=k(1-2t)^{-(k/2)-1}
which gives m'(0)=k

Similarily, I find

m''(t)=(k^{2}+2k)(1-2t)^{-(k/2)-2}
which gives m''(0)=k^2+2k

However, in my textbook, it says that the variance of a square-chi distribution should be 2k, not k^2. Where do I go wrong?
 
Of course. Then var(X)=E(X^2)-(E(X))^2 =k^2+2k-k^2=2k.

Thank you!
 
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