Expected value/variance of continuous random functions?

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Discussion Overview

The discussion revolves around calculating the expected value and variance of a continuous random variable defined by a specific probability density function (PDF). Participants explore the integration process required for these calculations, addressing both the expected value and variance in the context of continuous distributions.

Discussion Character

  • Homework-related
  • Mathematical reasoning

Main Points Raised

  • One participant presents a probability density function and requests assistance in calculating the expected value and variance.
  • Another participant outlines the formula for expected value, emphasizing the need to integrate the product of x and the PDF over the relevant interval.
  • Further clarification is provided on the integration limits, noting that the integration does not need to extend to infinity for this specific case.
  • Participants discuss the formula for variance, indicating it can be expressed in terms of expected values of x and x squared.
  • One participant expresses confusion about the integration process and the subsequent steps needed to find variance.
  • Another participant corrects a calculation error and reinforces the importance of understanding the underlying concepts behind variance calculation.
  • Discrepancies in calculated values for expected value and variance are noted, with participants attempting to verify their results through integration.
  • Participants express uncertainty about the correctness of their calculations, particularly regarding the final variance value.

Areas of Agreement / Disagreement

Participants generally agree on the formulas and methods for calculating expected value and variance, but there is no consensus on the correctness of specific calculations or the final variance value. Multiple competing views and uncertainties remain regarding the integration steps and results.

Contextual Notes

Some participants express uncertainty about their integration results and the implications for variance calculation. There are unresolved mathematical steps and potential errors in the integration process that affect the final outcomes.

das1
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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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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.
 
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: $$E[X]=\int_{-\infty}^{\infty}xf(x)dx$$. 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 $$\text{Var}[X]=E[X^2]-(E[X]])^2$$. 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 $E[X^2]$?
 
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
 
Try again. You're close but you forgot an $x$.

$$\int_{0}^{3}x \cdot \frac{x^2}{9}dx$$

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.
 
The formula I've found is:

integrate x^2 f(x) dx - u^2
 
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 $$\int_{0}^{3}x^2f(x)dx$$ and you can find the variance too.
 
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?
 
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?
$$\int_{0}^{3}x \cdot \frac{x^2}{9}dx=\frac{3^4}{36}=\frac{9}{4}$$

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 $$E[X^2]-(E[X]])^2$$.
 
  • #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.
 

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