Is it possible for more than one "linear regression" line to fit a given set of points? (i.e.... "least squares" is minimised equally in both cases)

I'm assuming a simple one-variable regression in which we have a single "dependent" variable for a given x-value. It may be that regression is forced "through the origin" for the purpose of the Q (if that matters), so, I know it wouldn't be a great fit.

Or is there something intrinsic about a regression line that means it is unique? I am interested in a 'proof' (could be just an intuitive proof) if it exists, that a regression line must be unique.


No. Under usual conditions you have a unique solution to OLS equation.

You can always come up with degenerate cases, e.g. $$y=\beta_0+\beta_1 x_1+\beta_2x_2+e$$

If $x_1=x_2$, then any $\beta_1'=\beta_1-c,\beta_2'=\beta_2+c$ will work as well.

The reason is simple. Consider OLS equation: $y=X\beta+e$, you minimize squared residuals $e=y-X\beta$ (vector notation): $$e^2=y'y+\beta'X'X\beta-2y'X\beta$$ This is a quadratic (on unknown $\beta$) equation, convex, you can apply linear algebra to show that it has a unique solution. Intuitively it's convex, so there's a bottom, just one point. Again, this is under certain reasonable conditions.

Obviously, here I'm skipping the discussion of errors vs. residuals.

  • $\begingroup$ What happens if you have more features than observations? $\endgroup$ – Sycorax Mar 5 '16 at 2:11
  • $\begingroup$ @user777, you may end up with infinite number of solutions. Imagine in the model in my example you have only one observation: $y=1,x_1=1,x_2=1$, so you get $1=\beta_0+\beta_1+\beta_2$, and you can plug infinite number of combinations to satisfy it $\endgroup$ – Aksakal Mar 5 '16 at 2:16

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