The Why Behind Sum of Squared Errors in a Linear Regression I'm just starting to learn about linear regressions and was wondering why it is that we opt to minimize the sum of squared errors. I understand the squaring helps us balance positive and negative individual errors (so say e1 = -2 and e2 = 4, we'd consider them as both regular distances of 2 and 4 respectively before squaring them), however, I wonder why we don't deal with minimizing the absolute value rather than the squares. If you square it, e2 has a relatively higher individual contribution to minimize than e1 compared to just the absolutely values (and do we want that?). I also wonder about decimal values. For instance, say we have e1 = 0.5 and e2 = 1.05, e1 will be weighted less when squared because 0.25 is less than 0.5 and e2 will be weighted more.   Lastly, there is the case of e1 = 0.5 and e2 = 0.2. E1 is further away to start, but when you square it 0.25 is compared with 0.4. Anyway, just wondering why we do sum of squares Erie minimization rather than absolute value.
Simple google search on "stats why regression not absolute difference" would give you good answers. Try it yourself!

I can quickly summarise:


Your regression parameters are solutions to the maximum likelihood optimisation. That involves derivative, but absolute difference doesn't have a derivative at zero. There's no unique solution for least absolute regression.
Least absolute regression is an alternative to the regular sum of squares regression, commonly classified as one of the robust statistical methods.
You'd prefer least absolute regression if you care about outliers, otherwise the regular regression is generally better.


You might want to read about L1 vs L2:


  https://stats.stackexchange.com/questions/45643/why-l1-norm-for-sparse-models