Moment generating function of a continuous variable

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The discussion revolves around finding the moment generating function (MGF) for the given probability density function f(x) = 0.15e^{-0.15x}. The user initially struggles with evaluating the integral and determining the limits, particularly as x approaches infinity. After clarification, it is established that the limit of e^{-kx} as x approaches infinity is zero when k > 0, leading to the correct MGF of 0.15/(0.15 - t) for t < 0.15. The conversation then shifts to calculating the variance, where the user realizes the need to subtract the square of the mean from the second moment to align with the book's answer. Ultimately, the user confirms their understanding of the relationship between the MGF and the moments of the distribution.
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Homework Statement



Find the moment generating of:

f(x)=.15e^{-.15x}

Homework Equations



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

The Attempt at a Solution



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

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

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:

\frac{.15}{.15-t}
 
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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.
 
That's what I'm having trouble with. How do I evaluate it at infinity?
 
What's the limit of e-kx, k>0, as x goes to infinity?
 
-inf?
 
Instead of guessing, try plotting the function or plugging numbers into your calculator to see if you can see what the limit is.
 
I'm not guessing. I plotted it out, and as x goes to -inf, e goes to inf.

Oh. typo above.
 
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?
 
Oh. I see. My head wouldn't release the reading error.

\frac{-.15e^{(t-.15)\infty}}{t-.15} - \frac{-.15e^{(t-.15)0}}{t-.15}

0-\frac{.15}{t-.15} = \frac{-.15}{t-.15}=\frac{.15}{.15-t}

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 = \frac{.15}{(.15-t)^2}

Second derivative = \frac{.3}{(.15-t)^3}

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 \sigma ^2
 
  • #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.
 

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