ABSTRACT
Curve fitting is normally used when the data has substantial inherent error, such as data gathered from experimental measurements. The aforementioned function or polynomial can then be used for interpolation purposes; that is, to find estimates of values at intermediate points where the data is not directly available. If the relationship between the independent and dependent variables is not linear, curve-fitting techniques other than linear regression must be used. The user-defined function quadratic regression uses the quadratic least-squares regression approach to find the second-degree polynomial that best fits a set of data. With quadratic splines, a second-degree polynomial is employed to interpolate over each interval between data points. The most commonly used splines are cubic splines, which produce very smooth connections over adjacent intervals. In cubic splines, third-degree polynomials are used to interpolate over each interval between data points.
