I have a lot of $x,y$ data. I was considering using linear regression to fit the equation $y=mx+c$, but I want to find a value for $m$ that makes $c$ as near as possible to zero.
Can I therefore use the equation $y=mx$ and merely divide the sum of all $y$ by the sum of all $x$ to obtain $m$?
Would it be appropriate to square the data before summing, and then square-root, so that there is least-squared error? This would however mean that $m$ will inevitably be positive, which may be wrong.
Edit: C is actually an error term which I would like to be zero. When I have new data for x, and I want to predict y, would it be better to use m from fitting y=mx, or would it be better to use m from fitting y=mx+c and pretend that c is zero?