Calculating the mean and variance from a moment generating function

In summary, the conversation discusses using the moment generating function to find the expected value and variance of a squared-Chi distribution. The mistake in finding the variance is identified and corrected, resulting in a variance of 2k.
  • #1
Charlotte87
21
0

Homework Statement


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

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

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:

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

Similarily, I find

[itex]m''(t)=(k^{2}+2k)(1-2t)^{-(k/2)-2}[/itex]
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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  • #2
Charlotte87 said:

Homework Statement


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

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

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:

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

Similarily, I find

[itex]m''(t)=(k^{2}+2k)(1-2t)^{-(k/2)-2}[/itex]
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?
 
  • #3
Of course. Then var(X)=E(X^2)-(E(X))^2 =k^2+2k-k^2=2k.

Thank you!
 

Related to Calculating the mean and variance from a moment generating function

What is a moment generating function (MGF)?

A moment generating function (MGF) is a mathematical function that is used to calculate moments of a random variable. It is defined as the expected value of e^tx, where t is a real number and x is the random variable. The MGF can be used to calculate the mean and variance of a random variable.

What is the purpose of calculating the mean and variance from a moment generating function?

The mean and variance are important measures of central tendency and variability, respectively, in a dataset. They provide valuable information about the distribution of the data. Calculating them from the MGF allows for a more efficient and accurate way to obtain these measures, especially for complex distributions.

How is the mean calculated from a moment generating function?

The mean of a random variable can be calculated by taking the first derivative of the MGF and evaluating it at t=0. This is also known as the moment generating function's first moment. In mathematical notation, the mean is represented as E(X)=M'(0), where X is the random variable and M(t) is the MGF.

How is the variance calculated from a moment generating function?

The variance of a random variable can be calculated by taking the second derivative of the MGF and evaluating it at t=0. This is also known as the moment generating function's second moment. In mathematical notation, the variance is represented as Var(X)=M''(0)-[M'(0)]^2, where X is the random variable and M(t) is the MGF.

What are some common distributions for which the mean and variance can be calculated from the moment generating function?

The MGF can be used to calculate the mean and variance for a variety of distributions, including the normal, exponential, Poisson, and gamma distributions. It is particularly useful for calculating the mean and variance of complex distributions, such as the chi-squared and beta distributions.

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