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
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
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.
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
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
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.
residual value as result of Least squares method.
Quadratic spline method to Interpolation method
Spline Interpolation method
drawing logics for synchronizing window size and graph and
texts size in drawing canvas.
fitting analysis program to MathPad