Talk:Method of least squares

This article contains actually a short description of linear regression, a subject described more extensively in linear regression.

I would make this a more general introduction to general least squares adjustment.

The article titled "least squares" now has links to linear regression and Gauss-Markov theorem, both of which are fairly good. Note that the linear regression article states that the model

$Y_{i}=\alpha _{0}+\alpha _{1}x_{i}+\alpha _{2}x_{i}^{2}+\varepsilon _{i}$

where the last term is the only one to the right of "=" that is random, and those are i.i.d. normally distributed with expectation 0, does not cease to be linear because of the presence of the quadratic term, precisely because the vector least-squares estimates of the three coefficients is linear in the vector of Y-values. I mention this because the language that was here before I redirected this article makes me wonder if that was appreciated. Michael Hardy 17:32 Mar 3, 2003 (UTC)

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