Expected value/variance of continuous random functions?

In summary, to find the expected value of a probability density function, you need to take the integral of the function multiplied by x over the given interval. To find the variance, you need to take the integral of the function multiplied by x squared over the interval, and then subtract the square of the expected value. In this specific case, the expected value is 3/2 and the variance is 81/16. It is important to fully understand the formulas and concepts behind calculating the expected value and variance in order to successfully solve problems involving probability density functions.
  • #1
das1
40
0
For the following probability density function:

f(x) = [(x^2)/9] between 0 <= x <= 3
0 otherwise

calculate the expected value E(X) of this distribution, and also calculate the variance

I know I have to integrate the function but I don't know what else. Thanks!
 
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  • #2
Note that if a distribution has a probability density function $f(x)$, the expected value is computed by:
\[
E[X] = \int_\mathbb{R} x f(x) dx.
\]
You are given $f(x)$ on the interval $[0,3]$. You can work out the actual integral.
 
  • #3
I started writing this as magneto posted. I hope it doesn't trample on his post, but it's basically the same information, just worded less rigorously perhaps. :)

For a continuous distribution function: \(\displaystyle E[X]=\int_{-\infty}^{\infty}xf(x)dx\). Here we don't need to go all the way from negative infinity to infinity though in practice to calculate this integral. What should this integral be you think?

The variance can conveniently be rewritten as \(\displaystyle \text{Var}[X]=E[X^2]-(E[X]])^2\). You will calculate $E[X]$ in the first part, so squaring that is easy. Now you have one more thing to find. Any idea how to find $E[X^2]$?
 
  • #4
So I can integrate that as x^3/27
Substitute in 3 for x and get 27/27 = 1.
That much I understand, but what next? I subtract something right? I don't know what though
 
  • #5
Try again. You're close but you forgot an $x$.

\(\displaystyle \int_{0}^{3}x \cdot \frac{x^2}{9}dx\)

Ok, I can explain to you what you do in this problem in one second but you writing "subtracting something" makes me worry that you don't understand the idea behind calculating the variance. What is the formula you have seen to calculate this? :) We want you to fully understand everything so you can ace your courses.
 
  • #6
The formula I've found is:

integrate x^2 f(x) dx - u^2
 
  • #7
das said:
The formula I've found is:

integrate x^2 f(x) dx - u^2

Yep, that's exactly it!

You are calcualting $\mu$ in the first part, so just square whatever answer you get. Now you just need to find \(\displaystyle \int_{0}^{3}x^2f(x)dx\) and you can find the variance too.
 
  • #8
For u I got 9/2, and squaring that, I get 81/4

So x2 * f(x) is x4/9 right?
Integrating that is x5/45
Substitute in 3, and you get 243/45 - 81/4?
But this is a negative number. What am I doing wrong?
 
  • #9
das said:
For u I got 9/2, and squaring that, I get 81/4

So x2 * f(x) is x4/9 right?
Integrating that is x5/45
Substitute in 3, and you get 243/45 - 81/4?
But this is a negative number. What am I doing wrong?
\(\displaystyle \int_{0}^{3}x \cdot \frac{x^2}{9}dx=\frac{3^4}{36}=\frac{9}{4}\)

Here's what I get when I plug it into Wolfram, but double check my input.
 
  • #10
Right, my calculation was wrong there.
So instead of 81/4 it's 81/16?
243/45 - 81/16 is .3375.
Is this the variance?
 
  • #11
That's what I get, yes. It's \(\displaystyle E[X^2]-(E[X]])^2\).
 
  • #12
OK! thank you

- - - Updated - - -

There's one other related one I don't get how to do, I'll start a new thread though.
 

FAQ: Expected value/variance of continuous random functions?

What is the expected value of a continuous random function?

The expected value of a continuous random function is the average value that the function would take if it were repeated an infinite number of times. It is calculated by taking the integral of the function over its entire domain, weighted by the probability density function.

How is the expected value of a continuous random function calculated?

The expected value of a continuous random function is calculated by taking the integral of the function over its entire domain, weighted by the probability density function. This can be written mathematically as E(X) = ∫xf(x)dx, where f(x) is the probability density function of the function X.

What is the significance of the expected value of a continuous random function?

The expected value of a continuous random function is an important measure in probability theory as it gives an idea of the central tendency of the function. It is also used in many real-world applications, such as finance and insurance, to estimate the average outcome of a situation.

What is the variance of a continuous random function?

The variance of a continuous random function is a measure of how much the function deviates from its expected value. It is calculated by taking the integral of the squared difference between the function and its expected value, weighted by the probability density function. Mathematically, Var(X) = ∫(x-E(X))^2f(x)dx.

How is the variance of a continuous random function related to its expected value?

The variance of a continuous random function is related to its expected value through the formula Var(X) = E[(X-E(X))^2]. This means that the variance is equal to the expected value of the squared difference between the function and its expected value. In other words, the variance measures how much the function varies from its expected value.

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