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Polynomial regression help?

Hi guys, I've been a lurker for some time now, but have a question. If anyone could help it would be greatly appreciated.

I'm trying to fit a polynomial regression line to a set of data and I want to do it by hand instead of through a software package. I know that to estimate β in y = a + βx I use:

β = Σ( x- xbar)(y - ybar) / Σ(x - xbar)^2

But how can I estimate the β2 in y = a + β1x + β2x^2

Any help at all would be appreciated.
Yes, ordinary least squares will work for this -- it's still Ax = b, and the solution is still x=(A'A)^-1 A'b

You can do it in Excel -- just type "=X^2" in a second column, and then regress with both of these columns as independent variables, against whatever Y variable you want.

For the long formula with the subtracted means (x-xbar, etc) written out long-hand, look in a regression book for "multivariate regression" and use those formulas -- one good multivariate stat book is by Alvin Rencher, called Linear Models. Matrix notation is more compact, though, with the Ax=b.

Bastian Gross

German Mathquant
Least Squares Method


you need a Least Squares Method in a multidimensional model.

If you want do it by hand, you'll need to be in luck or you'll need an one-dimensional problem.
You've to find a curve which has the best fit to a series of data points like curve fitting or interpolation.
If you get the order of the equation as a second degree polynomial, like:

\(y = \alpha + \beta_{1}x + \beta_{2}x^{2}\)
You'll exactly fit three points. This is unconditional required to calculate your parameters \(\alpha, \beta_{1}\) and \(\beta_{2}\)

I would rather recommend to compute this not by hand.

Bastian Gross

German Mathquant
Least Squares Fit of a Quadratic Curve to Data

Yes Iulian,

this computes in an one-dimensional case polynomial regression:

(\begin{align}n & & \sum^{n}_{i=1}x_{i} & & \sum^{n}_{i=1}x_{i}^{2} & & \alpha & =& \sum^{n}_{i=1}y_{i}\\\sum^{n}_{i=1}x_{i} & & \sum^{n}_{i=1}x_{i}^{2} & & \sum^{n}_{i=1}x_{i}^{3}& &\beta_{1} & =& \sum^{n}_{i=1}x_{i}y_{i}\\\sum^{n}_{i=1}x_{i}^{2} & & \sum^{n}_{i=1}x_{i}^{3} & & \sum^{n}_{i=1}x_{i}^{4}& &\beta_{2} & =& \sum^{n}_{i=1}x_{i}^{2}y_{i}\\\end{align})

With n number of observations and i the i-th observation of variables x and output y.