Y = B 1 + B 2 ∗ x + B 3 ∗ x 2 įor non-linear functions, an iterative non-linear least squares W approach is utilized to converge to the best fit curve. For linear least squares W methods, the function is considered linear if its coefficients (the parameters B1, B2, B3.) are linear (i.e. However, for non-linear functions, the solution typically needs to converge through an iterative approach. įor linear functions, the solution for a best fit curve is a defined closed solution that can be directed solved. The goodness of fit W can be measured in several capacities, including the common methods of the coefficient of determination W and the chi-square test W. This approach is known as the method of the least squares W. The main theory behind curve fitting data revolves around minimizing the sum of the squares of the residuals (where the residual of a curve fit for each data point is the difference between the observed data point and the predicted value as given by the function of the curve). The driving mathematical function can be linear or non-linear, and different approaches to curve fitting can be undertaken depending on the type of function being fit to the data. The small differences that arise between the observations and predicted values are then due to measurement errors and uncontrolled influencing factors.
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