LCKurtz said:
Isn't that what I said above?
You wouldn't expect them to be exactly on the best least squares fit.
When I do it in Maple I get ##a = 183/56, b = -495/15, c = 5017/56##, so my ##c## is a bit different from what you wrote.
If, instead of a least-squares solution we minimize the absolute error solution
\sum_{i=1}^8 |Y_i - a - b X_i - c X_i^2|
we obtain ##a = 77, b = -30,\, c = 3##. The errors are all zero except at the point x = 11, where the absolute error is 60. Basically, the mean absolute-deviation method automatically de-emphasizes the point x = 11 and gives an exact fit to the rest of the data.
For the benefit of the OP: the least-absolute solution can be obtained by solving a so-called linear programming problem:
\text{minimize } \sum_{i=1}^8 z_i\\<br />
\text{subject to the constraints}\\<br />
z_i \geq Y_i - a - b X_i - c X_i^2, \: i=1, \ldots, 8\\<br />
z_i \geq a + b X_i + cX_i^2 - Y_i, \: i = 1, \ldots, 8
Here, the variables are ##a,b,c, z_1, z_2, \ldots, z_8##. Such problems can be solved using the EXCEL Solver tool, or using the (free) trial version of LINGO (from LINDO Systems, Inc.) plus numerous free "linear programming solution" tools available from the web.