DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting).
Version: 1.7.31DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting).
License: Free To Try $15.00
Operating System: Windows
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.
Version 1.7.31: Internal optimization of algorithms
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