First Central Moment: Proving Intuitive Equality

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In summary: No, because the integral on the right is just a number. The inner integral on the left (which is the function you are trying to integrate) is a function of y.
  • #1
gnome
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The "first central moment" of a real-valued function

[tex]\mu_1 \equiv \int_{-\infty}^\infty (x - \mu) f(x)\,dx = 0[/tex]

where

[tex]\mu \equiv \int_{-\infty}^\infty x\, f(x)\,dx[/tex]

so we have

[tex]\int_{-\infty}^\infty (x - \left ( \int_{-\infty}^\infty x\, f(x)\,dx \right ) ) f(x)\,dx = 0[/tex]

Intuitively, it seems to make sense, but how do we manipulate those integrals to prove this equality?
 
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  • #2
This equality, as you have expressed it, is not true. I can easily come up with a counterexample. There are at least a couple of things you can change to make a true equality.

This looks like homework. You need to show some work before people here will help you.
 
  • #3
D H said:
This equality, as you have expressed it, is not true. I can easily come up with a counterexample. There are at least a couple of things you can change to make a true equality.

This looks like homework. You need to show some work before people here will help you.

1. It's not homework.

2. Perhaps I should have specified that f(x) is a probability distribution. As for it not being true, I have read it in a number of places such as
http://mathworld.wolfram.com/Moment.html
and
http://en.wikipedia.org/wiki/Moment_(mathematics)
and elsewhere.

The the reason I posted the question is that I realized that I have no idea how to manipulate an integral that contains an integral as part of the integrand, so trying to "show some work" would be pointless. If you know how, I would appreciate any help or examples of how to handle such a function.
 
  • #4
gnome said:
Perhaps I should have specified that f(x) is a probability distribution.

Perhaps you should have. You said [itex]f(x)[/itex] is a real-valued function. The relation is true only if [itex]\int_{-\infty}^{\infty}f(x)dx = 1[/itex].

Use the fact that f(x) has unit area.
 
  • #5
Sorry, I still don't see it.

I want to show that

[tex]\int_{-\infty}^\infty x f(x)\,dx = \int_{-\infty}^\infty \left(\int_{-\infty}^\infty x\, f(x)\,dx \right ) f(x)\,dx[/tex]

That would be obvious (given the fact that [itex]\int_{-\infty}^\infty f(x)\,dx = 1[/itex] if I could say

[tex]\int_{-\infty}^\infty \left(\int_{-\infty}^\infty x\, f(x)\,dx \right ) f(x)\,dx = \int_{-\infty}^\infty f(x)\,dx \cdot \int_{-\infty}^\infty x\, f(x)\,dx[/itex]

but what gives me the right to do that? The only thing I've seen that allows that is Fubini's theorem, which I thought is only valid over a rectangle. I can't call this a rectangle, can I?
 
  • #6
What gives you the right to do that is that "dx" is a dummy variable. The inner integral is just a number.

Look back to your original post:

[tex]\mu_1 \equiv \int_{-\infty}^{\infty}(x-\mu)f(x)dx[/tex]

[itex]\mu[/itex] is is just a number. Thus

[tex]\mu_1 = \int_{-\infty}^{\infty}xf(x)dx - \mu\int_{-\infty}^{\infty}f(x)dx[/tex]

The first term is just [itex]\mu[/itex] by definition. The second term is also [itex]\mu[/itex] since [itex]f(x)[/itex] is a probability distribution function. Thus [itex]\mu_1 = 0[/itex].
 
  • #7
Heh heh...

I tried to tell myself that before posting the original question, but my self was not convinced. What's not quite clear to me is exactly when is the inner integral "just a number"?

Is the inner integral "just a number", and can I always do this...

[tex]\int_a^b \left(\int_c^d f(x)\,dx \right ) f(y)\,dy = \int_a^b f(y)\,dy \cdot \int_c^d f(x)\,dx[/tex]

as long as c and d are not functions of y?

(Surely it's not ALWAYS just a number, or there would be no need for Fubini, right?)
 
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  • #8
gnome said:
What's not quite clear to me is exactly when is the inner integral "just a number"?

It's not a number when it's a function.

In other words, this is not valid:

[tex]\int_a^b\int_c^d f(x,y) dy\; g(x) dx = \int_c^d f(x,y) dy \int_a^bg(x) dx[/tex]

because [itex]\int f(x,y) dy[/itex] is a function of x.
 
  • #9
I think we're saying essentially the same thing with different examples.

Compromising...in

[tex]\int_a^b\int_{h_1(x)}^{h_2(x)} f(y) dy\; g(x) dx = \int_c^d f(x,y) dy \int_a^bg(x) dx[/tex]

isn't

[tex]\int_{h_1(x)}^{h_2(x)} f(y) dy[/tex]

also a function of x?
 

FAQ: First Central Moment: Proving Intuitive Equality

What is the "First Central Moment" and why is it important?

The First Central Moment is a mathematical concept used in statistics and probability theory to measure the dispersion or spread of a set of data around its mean. It is important because it helps us understand the variability of a data set and make comparisons between different data sets.

How is the "First Central Moment" calculated?

The First Central Moment is calculated by taking the difference between each data point and the mean, raising it to the first power, and then finding the average of these values. This can be expressed as Σ(xi - μ)1 / n, where xi is the data point, μ is the mean, and n is the number of data points.

What is the intuitive equality in relation to the "First Central Moment"?

The intuitive equality is a concept that states that the First Central Moment of a data set is equal to zero when all the data points are equal to the mean. This may seem counterintuitive at first, but it is true because it means that there is no dispersion or spread around the mean.

How can the intuitive equality be proven?

The intuitive equality can be proven using mathematical equations and properties. One way to prove it is by showing that when all the data points are equal to the mean, the First Central Moment is equal to zero. This can be done by substituting the values into the formula for the First Central Moment.

Why is it important to understand the intuitive equality in relation to the "First Central Moment"?

Understanding the intuitive equality helps us better understand the concept of variability in data sets and how the First Central Moment is affected by the spread of data around the mean. It also allows us to make accurate comparisons between different data sets and draw meaningful conclusions from our analyses.

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