How To Solve Least Squares Approximation. Do a least squares regression with an estimation function defined by y ^ = α 1 x + α 2. The matrix ata in the theorem is a symmetric, square matrix of size m m.
Method of least squares is in the best fit value of b(the least important of the two parameters), and is due to the different ways of weighting the errors. Given m data point, f(x i;y i)gm i=1, nd the equation of polynomial: And k= z b a w(x)[˚ k(x)]2 dx:
