Least-Square-Fit-Method

Least Square fit method is used for Best curve fit for a scatter data. It is a mathematical procedure for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the offsets ("the residuals") of the points from the curve. The sum of the squares of the offsets is used instead of the offset absolute values because this allows the residuals to be treated as a continuously differentiable quantity. However, because squares of the offsets are used, outlying points can have a disproportionate effect on the fit, a property that may or may not be desirable depending on the problem at hand. Two types of Least square fit methods are discussed in this file. - Linear least square method. - Quadratic least square method. The linear least square method is used to fit curves of linear equations. In this, as an example, we take data between Cl (Co-efficient of lift) and Alpha ( Angle of Attack). The Quadratic least square method is used to fit curves of Quadratic equations. In this, as an example, we take data between Cl (Co-efficient of lift) and CD (Co-efficient of drag). The data used is included in this file. The Mathematical Reference can be found in the link below. https://mathworld.wolfram.com/LeastSquaresFitting.html