It is my understanding that you can use linear least squares to fit a plethora of different functions (quadratic, cubic, quartic etc). The requirement of linearity applies to the coefficients (i.e B in (y-Bx)^2). It seems to me that I can find a solution such that a coefficient b_i^2=c_i, in other words I can just express the squared b_i with a linear c_i. So can't I always find a linear solution?(adsbygoogle = window.adsbygoogle || []).push({});

My gut feeling is that linearity is required because normality is required for the orthoganality principle to hold? But I am not sure.

Thanks

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# The linear in linear least squares regression

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