“…Let ( 0 , 0 ), ( 1 , 1 ), ( 2 , 3 ), ⋯, ( , ), the coordinates of a data set, such that = 0, 1, 2, … , , and the adjustment curve is = ( , 0 , 1 , 2 , ⋯ , ), where [ 0 , 1 , … , ] are adjustment constants. The least squares approximations [30,31,32,33,34,35] attempts to minimize the sum of squares from the vertical distances of values to the ideal model ( ) and obtain the model function ( 0 , 1 , 2 , ⋯ , ), which minimizes the square error defined by:…”