Find 5th degree polynomial equation from points? I have a set of estimated points, listed below, and I would like to know how to calculate the polynomial.
I've looked at Wikipedia, and I don't quite understand how at works. Example 1 on http://en.wikipedia.org/wiki/Lagrange_polynomial#Example_1 seems to be the closest to what I'm trying to do, but I don't know how to get the basis polynomials, i.e. turning the middle section into the right section. I can probably get it to the interpolating section from there.
My points are as follows:
800, 175; 600, 125; 400, 100; 200, 125; 0,0; -200, -125; -400, -100; -600, -125; -800, -175.
Eventually, I'm going to try and dynamically swap out the points above in the program I'm building, so I could really use a step by step description. I read Determining Coefficients of a Finite Degree Polynomial $f$ from the Sequence $\{f(k)\}_{k \in \mathbb{N}}$, but I don't quite follow it. It's very possible I'm out of my depth and should re-think what I'm trying to accomplish, but I'd really like to try this. Thanks!
 A: If you know for a fact that your points lie on a polynomial of degree 5, you can use finite differences. This is especially easy when the $x$-values are in arithmetic progression, as in your case. Write the $y$-values: 
175  125  100  125  0  -125  -100  -125  -175 
Then write the difference between each number and the next: 
-50  -25  25  -125  -125  25  -25  -50 
Again: 
25  50  -150  0  150  -50  -25 
Again: 
25  -200  150  150  -200  25 
Again: 
-225  350  0  -350  225 
And once more: 
575  -350  -350  575 
Now if your points were really from a polynomial of degree 5, that last line would have been constant, but it's not, so they're not. But after all, you said they were estimated points - they still might be close to some polynomial of degree 5. To find the polynomial of degree 5 that comes closest to your points, there is a method called least squares, and there are many expositions of it on the web.
EDIT: Here's a start on least squares: 
You want a polynomial $p(x)=ax^5+bx^4+cx^3+dx^2+ex+f$ such that $p(800)=175$, $p(600)=125$, etc. We already know that's impossible, so we settle for making all the numbers $p(800)-175$, $p(600)-125$, etc., small. In fact, we form the quantity $$(p(800)-175)^2+(p(600)-125)^2+\cdots+(p(-800)-175)^2$$ and we try to minimize it. This quantity is a function of the 6 variables $a,b,c,d,e,f$, and there are standard calculus techniques for minimizing such a function. It gets very messy, but fortunately you don't actually have to do it; someone has done it for you, in the most general case, and found that you can write down a very simple matrix equation which you can solve for the unknowns $a,b,c,d,e,f$. And that's what you'll find if you search for least squares polynomial fit.  
A: With 9 points your data will not fit a 5th degree polynomial exactly.  You need a least squares fit, which will smooth things.  More information is found in chapter 15 of Numerical Recipes (obsolete versions are free) or any numerical analysis textbook.  Your particular data is odd $(f(-x)=-f(x)$, so only $x, x^3, x^5$ terms will appear.  I rescaled your x values for the fit below.  You can rescale them back by dividing the coefficients by $200^{\text{power of x}}$.  It doesn't look like a very good fit, but Excel claims it is the best you will get.  The even order terms are roundoff error and should be ignored.

