Variance of Continous Random Variable

AI Thread Summary
The discussion revolves around calculating the variance of a continuous random variable Y defined as Y = 3X + 2, where X has a specific probability density function (PDF). The mean of Y was initially calculated incorrectly as 10, leading to confusion about the variance calculation. It was clarified that the correct relationship between the means and variances of X and Y should be used, specifically that Var(Y) = 9 * Var(X) since the constant term does not affect variance. After addressing the arithmetic mistake, the correct variance was determined to be 144 based on the variance of X. The conversation highlights the importance of correctly applying statistical formulas and relationships in probability theory.
Lancelot59
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I have the continuous random variable Y, defined such that:

<br /> Y=3X+2<br />
and the PDF of x is zero everywhere but:
f(x)=\frac{1}{4}e^{\frac{-x}{4}}, x&gt;0

I correctly got the mean like so:
\mu=E(h(x))=\int^{\infty}_{0} h(x)f(x)
and evaluated it to be 10.

I am unsure as to how I go about getting the variance now. In previous problems I use the usual formula of:

\sigma=\int^{b}_{a} (x-\mu)^{2}f(x)

or the shortcut of:
\sigma^{2}=E[X^{2}]-\mu^{2}

However I'm unsure what to do in the case where the random variable is dependent on another with a given PDF function for continuous random variables.
 
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Start with substiting in the expression for Y into Var(Y)

Seperate out Var(X)
 
Gullik said:
Start with substiting in the expression for Y into Var(Y)

Seperate out Var(X)

I'm not quite sure how to put that together. To start, I have:
Var(Y)=\int^{\infty}_{0} (y-10)^2*f(y)
 
I would start with Var(Y)=Var(3X+2)=Var(3X)+Var(2)
 
Lancelot59 said:
I'm not quite sure how to put that together. To start, I have:
Var(Y)=\int^{\infty}_{0} (y-10)^2*f(y)

If you want to use this form then \mu_Y=E(Y)=E(3X+2)=3E(X)+2
 
Gullik said:
If you want to use this form then \mu_Y=E(Y)=E(3X+2)=3E(X)+2

I see. So all I need to do is then find the expected value of the function defining the PDF of x?
 
Lancelot59 said:
I have the continuous random variable Y, defined such that:

<br /> Y=3X+2<br />
and the PDF of x is zero everywhere but:
f(x)=\frac{1}{4}e^{\frac{-x}{4}}, x&gt;0

I correctly got the mean like so:
\mu=E(h(x))=\int^{\infty}_{0} h(x)f(x)
and evaluated it to be 10.

I am unsure as to how I go about getting the variance now. In previous problems I use the usual formula of:

\sigma=\int^{b}_{a} (x-\mu)^{2}f(x)

or the shortcut of:
\sigma^{2}=E[X^{2}]-\mu^{2}

However I'm unsure what to do in the case where the random variable is dependent on another with a given PDF function for continuous random variables.

Neither X nor Y has mean 10, so you did something wrong. What is the mean of X? How is the mean of Y related to the mean of X? What is Var(X)? How is Var(Y) related to it?

RGV
 
Ray Vickson said:
Neither X nor Y has mean 10, so you did something wrong. What is the mean of X? How is the mean of Y related to the mean of X? What is Var(X)? How is Var(Y) related to it?

RGV

10 is what I got, and it matches the answer in the textbook...
 
Lancelot59 said:
10 is what I got, and it matches the answer in the textbook...
I calculated E[X] = 4. Please show how you got 10.
 
  • #10
Lancelot59 said:
10 is what I got, and it matches the answer in the textbook...

Well, EX (the mean of X) is not 10, and EY (the mean of Y) is not 10. However, E(3*X - 2) = 10, so maybe you just typed the question incorrectly, and maybe you really meant EY when you seemed to be talking about EX.

RGV
 
  • #11
jbunniii said:
I calculated E[X] = 4. Please show how you got 10.

Here is the integral I solved in order to get the mean:

\mu=\int^{\infty}_{0} \frac{1}{4}e^{\frac{-x}{4}}(3x-2)dx=10

from the identity where:

E[h(x)]=\int^{\infty}_{-\infty} h(x)f(x) dx

Anyhow, it turns out that in solving the variance I made an arithmetical mistake, and I did manage to get the correct answer. Thanks for the help!
 
  • #12
Lancelot59 said:
Here is the integral I solved in order to get the mean:

\mu=\int^{\infty}_{0} \frac{1}{4}e^{\frac{-x}{4}}(3x-2)dx=10

from the identity where:

E[h(x)]=\int^{\infty}_{-\infty} h(x)f(x) dx

Anyhow, it turns out that in solving the variance I made an arithmetical mistake, and I did manage to get the correct answer. Thanks for the help!

OK, so as I suspected, you computed EY = E(3X-2), but your question originally said Y = 3X+2!

BTW: if 'a' and 'b' are constants, Var(AX+b) = a^2 Var(X) [that is, the constant term 'b' drops out and the factor 'a' gets squared]. Since Var(X) = 4^2 = 16, we have Var(Y) = 3^2 * 16 = 144. This is a lot easier than computing
\int_0^{\infty} (y-10)^2 f_Y(y) \, dy.

RGV
 
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