To find the best straight line fit to a set of data points \(\left(x_{n},
y_{n}\right)\) in the "least squares" sense means the following: Assume that
the equation of the line is \(y=m x+b\) and verify that the vertical deviation
of the line from the point \(\left(x_{n}, y_{n}\right)\) is \(y_{n}-\left(m
x_{n}+b\right)\). Write \(S=\) sum of the squares of the deviations, substitute
the given values of \(x_{n}, y_{n}\) to give \(S\) as a function of \(m\) and \(b,\)
and then find \(m\) and \(b\) to minimize \(S\).
Carry through this routine for the set of points: \((-1,-2),(0,0),(1,3) .\)
Check your results by computer, and also computer plot (on the same axes) the
given points and the approximating line.