Least squares
Least squares is a way to find the best fit for a line when looking at data. It helps us figure out the line that is closest to all of the data points. To find the best fit line, we have to find the line that has the least amount of "squares" or boxes between each data point and the line. So we add up all of the little squares and try to make them as small as possible to get our best fit line.
Related topics others have asked about:
Best linear unbiased prediction,
Gauss–Markov theorem,
L2 norm,
Least-squares adjustment,
Least-squares spectral analysis,
Least absolute deviations,
Measurement uncertainty,
Minimum mean square error,
Orthogonal projection,
Proximal gradient methods for learning,
Quadratic loss function,
Root mean square