Matlab's polyfit function is very handy to find least-squares regression. It minimizes the (L2-norm) of the estimation errors, by solving a linear system. <https://www.mathworks.com/help/matlab/ref/polyfit.html>
An often overlooked way to deal with these situations is to use Least Absolute Deviations (LAD) line fitting. It minimizes the L1-norm of the residuals, thus it is less sensitive to outliers that fall far away from the underlying model https://en.wikipedia.org/wiki/Least_absolute_deviations
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You are given two vectors X and Y (coordinates of observations on a plane). Return a row vector P = [a, b] with the coefficients of the best-fit line, in the L1-norm sense. I.e., find a and b that minimize sum( abs( Y - a*X - b ) ) .
(compare your results with polyfit on the test suite!)
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