  Curve Fitting
Language JAVA - SDK1.6
Version 0.2.1
Last Update 2009.01.29 Description

 Data is often given for discrete values along a continuum. However, you may require estimates at points between the discrete values. MathPad Curve Fitter describes techniques to fit curves to such data in order to obtain intermediate estimates. In addition, you may require a simplified version of a complicate function. One way to do this is to compute values of the function at a number of discrete values along the range of interest. Then a simpler function may be derived to fit these values. Both of these applications are known as curve fitting. There are general approaches for curve fitting that are distinguished from each other on the basis of the amount of error associated with the data. First, where the data exhibits a significant degree of error or "noise," the strategy is to derive a single curve that represents the general trend of the data. Because any individual data point may be incorrect, we make no effort to intersect every point. Rather, the curve is designed to follow the pattern of the points taken as a group. One approach of this nature is called least-squares regression. Second, where the data is known to be very precise, the basic approach is to fit a curve of a series of curves that pass directly through each of the points. Such data usually originates from tables. Examples are values for the density of water or for the heat capacity of gases as a function of temperature. The estimation of values between well-known discrete points is called interpolation. MathPad Curve Fitter is a web based application for data/function analysis, fitting and plotting. It can be used by scientists and engineers to analyze their measurements and the mathematical models  Scientists, engineers,  or students can define any mathematical function and use it again to plot a curve and to model their data (see MathPad Curve Plotter in this site), finding by linear or nonlinear curve fitting the function parameters that best describe their observations. Capability

 MathPad Curve Fitter allows you fit X-Y data to a curve you select. Curve types supported are Interpolation(Quadratic spline, Cubic spline), and least squares regression(polynomial, log-polynomial, and Fourier series) In case of the Quadratic spline which is one of the Interpolation method, you can fix a differential coefficient between first two points and last two points. In case of the polynomial curve types, one of the least-squares method, you are allowed to select a curve order of from 1 through 10, and for the Fourier series curve type, you can choose the order of from 1 through 100.  You can get a result function of explicit type(Y=f(X)) if the least squares regression is applied, and you can confirm the result through MathPad Curve Plotter we also offer you. It also supports cut and paste operations input and result data to be exchanged with most spreadsheet and wordprocessing programs. Version History

 Update date Version Description 2000.12.07 0.2.2 Display residual value as result of Least squares method. 2000.06.23 0.2.1 Append Quadratic spline method to Interpolation method 1999.09.19 0.2.0 Append Spline Interpolation method 1999.06.10 0.1.0 Change drawing logics for synchronizing  window size and graph and texts size in drawing canvas. 1999.02.10 0.0.0 Append Curve fitting analysis program to MathPad