Moment generating function of a continuous variable

In summary, the homework statement is to find the moment generating function for the function f(x). The Attempt at a Solution says to take the second derivative of the function and find the variance.
  • #1
exitwound
292
1

Homework Statement



Find the moment generating of:

[tex]f(x)=.15e^{-.15x}[/tex]

Homework Equations



[tex]M_x(t)= \int_{-\infty}^{\infty}{e^{tx}f(x)dx}[/tex]

The Attempt at a Solution



I get down to the point (if I've done my calculus correctly) and gotten:

[tex]\frac{.15e^{(t-.15)x}}{t-.15} \Bigr| _{0}^{\infty}[/tex]

and I don't know how to evaulate this. Am I right this far? And if I am, how does this equate to the answer in the back of the book:

[tex]\frac{.15}{.15-t}[/tex]
 
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  • #2
Remember that x is your variable of integration, not t. Assume t<0.15, otherwise the integral won't converge, and just plug in the limits like you would normally do.
 
  • #3
That's what I'm having trouble with. How do I evaluate it at infinity?
 
  • #4
What's the limit of e-kx, k>0, as x goes to infinity?
 
  • #5
-inf?
 
  • #6
Instead of guessing, try plotting the function or plugging numbers into your calculator to see if you can see what the limit is.
 
  • #7
I'm not guessing. I plotted it out, and as x goes to -inf, e goes to inf.

Oh. typo above.
 
  • #8
I think you're screwing up the signs. You have a function of the form e-kx where k>0 (this is why you need t<0.15). What does it do as x goes to positive infinity, which is the upper limit of the integral?
 
  • #9
Oh. I see. My head wouldn't release the reading error.

[tex]\frac{-.15e^{(t-.15)\infty}}{t-.15} - \frac{-.15e^{(t-.15)0}}{t-.15}[/tex]

[tex]0-\frac{.15}{t-.15} = \frac{-.15}{t-.15}=\frac{.15}{.15-t}[/tex]

Do I have it yet? I'm way too tired to think clearly.
 
  • #10
Yup, you got it!
 
  • #11
Okay. I have to take the second derivative of that to find the Variance of the pdf.

First derivative = [tex]\frac{.15}{(.15-t)^2}[/tex]

Second derivative = [tex]\frac{.3}{(.15-t)^3}[/tex]

I've even confirmed the second derviative with Wolfram Alpha. However, when I plug 0 into it (to find the second moment, or the variance), I get 88.88. But the book says 44.44. Any idea what I'm doing wrong?
 
  • #12
I think the book is wrong. I multiplied f(x) by x2 and integrated to calculate E(x2) and got the same answer you did.
 
  • #13
Oh, wait. Were you looking for E(x2) or the variance? If it's the latter, you need to subtract off the square of the mean.
 
  • #14
Well, V(X) = E(x2)-[E(X)]2 but it also says that if I take the 2nd derivative of the moment generating function, I get V(X) or [itex]\sigma ^2[/itex]
 
  • #15
That's not correct. The moment generating function only allows you to calculate E(xn) by taking the n-th derivative.
 
  • #16
So if E(x2) is the second derivative, I have to subtract off [E(X)]^2, or 44.44, which makes it 44.44 just like the book says.

I think I understand that now. Thanks.
 

What is a moment generating function (MGF)?

A moment generating function is a mathematical function that provides a way to calculate the moments (mean, variance, etc.) of a continuous random variable. It is a useful tool in probability and statistics for analyzing and describing the characteristics of a random variable.

What is the formula for calculating the MGF of a continuous variable?

The moment generating function of a continuous random variable X is defined as M(t) = E[etX], where E is the expected value operator. In simpler terms, it is the expected value of etX, where t is a real number.

How is the MGF related to the probability density function (PDF)?

The MGF and PDF are closely related. In fact, the MGF is the Fourier transform of the PDF. This means that the MGF can be used to find the PDF and vice versa.

What is the significance of the MGF in probability and statistics?

The MGF is a powerful tool for analyzing the characteristics of a continuous random variable. It allows us to calculate moments and other statistical properties of a variable, such as the mean, variance, and higher order moments, without having to directly evaluate the PDF. It also has applications in hypothesis testing and confidence intervals.

Can the MGF be used for all types of continuous random variables?

Yes, the MGF can be used for all types of continuous random variables, including normal, exponential, and gamma distributions. However, it may not exist for some distributions, such as the Cauchy distribution.

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