DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting).

Version:DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting).1.7.30

License:Free To Try$15.00

Operating System:Windows

Homepage: www.math-solutions.org

Developed by:

DataFitting determines the values of parameters for an equation, whose form you specify, that cause the equation to best fit a set of data values. DataFitting can handle linear, polynomial, exponential, and general nonlinear functions.

DataFitting performs true nonlinear regression analysis, it does not transform the function into a linear form.

As a result, it can handle functions that are impossible to linearize such as: y = (a - c) * exp(-b * x) + c

Quickly Find the Best Equations that Describe Your Data:DataFitting gives students, teachers, engineers, researchers and other professionals the power to find the ideal model for even the most complex data, by putting a large number of equations at their fingertips. It has built-in library that includes a wide array of linear and nonlinear models from simple linear equations to high order polynomials.

Graphically Review Curve Fit Results:Once your data have been fit, DataFitting automatically sorts and plots the fitted equations by the statistical criteria of Standard Error.

You can preview your graph and output publication-quality graphs in several different configurations. A residual graph as well as parameter output is generated for the selected fitted equation. Data, statistical and numeric summaries are also available from within the report-panel.

DataFitting has the following capabilities:

- A 38-digit precision math emulator for properly fitting high order polynomials and rationals.
- A robust fitting capability for nonlinear fitting that effectively copes with outliers and a wide dynamic Y data range.

What's New

Version **1.7.30**: Improved 38-digit precision math solver

Version **1.7.29**: Enhancement of user's interface

Version **1.7.28**: Enhancement of the underlying algorithms

Version **1.7.27**: Higher precision improvements

Version **1.7.26**: Enhancement of the underlying algorithms

Version **1.7.25**: Higher precision improvements

Version **1.7.24**: Higher accuracy and larger extents

Version **1.7.22**: Floating-point mechanism incorporated

Version **1.7.21**: Enhanced performance and internal optimization

Version **1.7.20**: Overhaul of the underlying algorithms