Least Squares Regression Matrix for Rational Functions So first off no, this isn't a homework problem.
Second, I'm trying to understand how this works, NOT find a program that will do it for me.
Okay so I've known for a while how to use Gaussian-Jordan Elimination to interpolate a polynomial.  Recently I came across this article, which explains how to use multiple linear regression to fit a polynomial
Similarly I also learned how to interpolate for Rational Polynomials.  So now I'm trying to figure out how to fit a rational function to a set of data.  Specifically, I'm trying to understand how to setup the Matrix so that I can use Gaussian-Jordan elimination to find the coefficients.
Because I know interpolation and regression are similar (and in case it wasn't clear from my question), when I refer to interpolation, I mean only having just enough points to solve for the variables.  When I say regression, I mean you have more that enough points, and you're trying to find a function that adequately fits all of them
Thanks for any help you can provide.
 A: You can't use linear regression to 
try to minimize $\sum_j \left(\dfrac{p(x_j)}{q(x_j)} - y_j \right)^2$: the equations you get are nonlinear.  What you can do is minimize
$\sum_j (p(x_j) - y_j q(x_j))^2$, where to keep things nontrivial you require e.g. $q(0)= 1$.  If you write $p(x) = \sum_{i=0}^n p_i x^i$ and $q(x) =  \sum_{i=0}^m q_i x^i$ with $q_0 = 1$, the equations are 
$$\eqalign{\sum_j (p(x_j) - y_j q(x_j)) x_j^i &= 0, \ i = 0\ldots n \cr
          \sum_j (p(x_j) - y_j q(x_j)) y_j x_j^i &= 0, \ i = 1 \ldots m\cr}$$
Thus for equation $i$ in the first group of equations, the coefficient of
$p_k$ is $\sum_j x_j^{k+i}$ and the coefficient of $q_k$ is $- \sum_j y_j x_j^{k+i}$.
For equation $i$ in the second group, the coefficients are 
$\sum_j y_j x_j^{k+i}$ and $-\sum_j y_j^2 x_j^{k+i}$.  Since $q_0 = 1$ is
constant, its coefficients are moved over to the right side.
EDIT:  For example, with $n = 3$ and $m=2$, the system is
$$ \pmatrix{ \sum_j 1 & \sum_j x_j & \sum_j x_j^2 & \sum_j x_j^3 & - \sum_j y_j x_j & - \sum_j y_j x_j^2\cr
 \sum_j x_j & \sum_j x_j^2 & \sum_j x_j^3 & \sum_j x_j^4 & - \sum_j y_j x_j^2 & - \sum_j y_j x_j^3\cr
 \sum_j x_j^2 & \sum_j x_j^3 & \sum_j x_j^4 & \sum_j x_j^5 & - \sum_j y_j x_j^3 & - \sum_j y_j x_j^4\cr
 \sum_j x_j^3 & \sum_j x_j^4 & \sum_j x_j^5 & \sum_j x_j^6 & - \sum_j y_j x_j^4 & - \sum_j y_j x_j^5\cr
\sum_j y_j x_j & \sum_j y_j x_j^2 & \sum_j y_j x_j^3 & \sum_j y_j x_j^4 & - \sum_j y_j^2 x_j^2 & - \sum_j y_j^2 x_j^3\cr
\sum_j y_j x_j^2 & \sum_j y_j x_j^3 & \sum_j y_j x_j^4 & \sum_j y_j x_j^5 & - \sum_j y_j^2 x_j^3 & - \sum_j y_j^2 x_j^4\cr} \pmatrix{p_0\cr p_1\cr p_2\cr p_3\cr q_1\cr q_2\cr} = \pmatrix{\sum_j y_j\cr
 \sum_j y_j x_j \cr \sum_j y_j x_j^2 \cr \sum_j y_j x_j^3\cr \sum_j y_j^2 x_j\cr \sum_j y_j^2 x_j^2\cr}$$ 
