Minimum Mean-Square Error Curve Fit

Minimum Mean-Square Error Curve Fitting
The commonly-used measure of “fit” in curve fitting is mean-squared error (MSE).” In minimum
mean-squared error (MMSE) curve fitting (also called least-square curve fitting), the parameters
of a selected function are chosen to minimize the MSE between the curve and the measured
values.
Polynomial regression is a form of MMSE curve fitting in which the selected function is a
polynomial and the parameters to be chosen are the polynomial coefficients.
Linear regression is a form of polynomial regression in which the polynomial is of first degree.
The two parameters of a linear equation, representing a straight line curve, are chosen to minimize
the average of the squared distances between the line and the measured data values.