Seriously. I will need it for work. I haven’t been able to understand that shit from any online resource until now. Please ELI5
In: 380
Lots of people have explained you’re trying to draw a line with roughly same number of points above and below that line (i.e. line of best fit). I don’t think anyone has explained the following though:
For every data point, the distance between the line you drew, and each point, is a RESIDUAL. In simple linear regression, the model you’re fitting is attempting to minimize the SUM OF SQUARED RESIDUALS.
What does this look like? [Here’s a visualization.](https://ibb.co/kQ303jx) Don’t worry what the data are. The image on the left shows what the residuals look like, the one on the right shows the SQUARED residual. Add all the squares up, and that’s the sum of squares. You want that sum of squares to be as low as possible.
This demonstrates why a point that’s really far away from the line has a huge impact on the line’s trajectory – because the square gets a lot bigger.
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