Ordinary Least Squares (OLS) regression aims to minimise the sum of squared y intercepts between the original and predicted points at each x value.

Robust regression does not necessarily minimize the distance of the fitting function to the input data points (least squares), but has alternative aims (non-parametric).

Polynomial coefficients with various properties are stored within this type.