quietrain said:
i see, i check up wiki
http://en.wikipedia.org/wiki/Moment_(mathematics )
and it says the first moment is usually the average ? expectation value?
More precisely, the first moment is the numerator in calculating an average. When we multiply exam scores by the number of students who received each score and then add up those terms [ N_{1}x_{1} + ... + N_{n}x_{n} ] , we are calculating the first moment of the score distribution. We then divide that by the number of students (sometimes called the "zeroth moment" because we can be said to be adding N_{1}x_{1}^{0} + ... + N_{n}x_{n}^{0} ) to obtain the "class average", or the mean \mu.
the 2nd moment is the variance?
Here, we have to be a little more careful. In a statistical distribution, the "variance" is more completely called the "variance about the mean". The expression for that is \sigma^{2} = \frac {N_{1}(x_{1}-\mu)^{2} + ... + N_{n}(x_{n}-\mu)^{2} }{N_{1} + ... + N_{n} } . The square root of this is the standard deviation, \sigma.
When we calculate the second moment of some physical distribution of, say, mass, we are generally finding that about some reference point. For rotational inertia, this is the radius at the rotation axis, r = 0 , hence, \int_{0}^{R} r^{2} dm ; for the mean-squared radius, we measured outward from the center of the sphere.
To obtain the mean-squared radius, we then divided the second moment of mass by the mass of the sphere. Since this involved a volume integral also, we would properly refer to our result as a "volume-averaged" mean-squared radius. In other sorts of calculations, different results can be obtained, depending upon what quantity the average of the moment was taken over.
so from what you said, in the electron case, if i were to use the 2nd moment 3/5 R2, that would give the the variation of the electron position in the sphere right?
Not really. It is simply a measure of how the distribution is arranged. To cover the remainder of your questions, the various moments and their averages can be used as a way to provide more information about the nature of a distribution.
As an example, if I tell you that the average annual salary of employees in a company is $80,000 , does that mean that everyone is receiving just about $80,000 a year or that a few are receiving much more than that and most of the employees rather less than $80,000? The average alone can't answer that for you. (This is how averages are sometimes used deceptively.) I could get more information about the distribution if I knew the variance about that average. Let's say the square root of the variance, the standard deviation, is $40,000 . Now I see that there is a lot of spread around the average, so many people are making much more or much less than $80,000 year. But is that symmetrical around the average, skewed toward the high end, toward the low end? I still don't know. I could then ask for a third moment, etc. in order to build up a better understanding of the "shape" of the distribution.
This kind of moment analysis has its uses, but it does have a limitation. Notice that each moment (or averaged moment) only gives us one number, when what might give us a clearer picture of the distribution would be a histogram. But for some kinds of physical measurements, moments or averaged moments are all an experiment can give you. In the example of the electron distribution, we can't actually compile information on the location of electrons (probing will put momentum and energy into the electron, altering what we're trying to measure). But some experiments can indirectly measure quantities such as the mean-squared radius of the distribution (because we are looking at the collective behavior of a lot of atoms at once), so moment measurement sometimes is what we have to be satisfied with.