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  Nonlinear analysis - DataFitting 1.7.29   
 
 
Short Description:
DataFitting performs regression analysis to estimate values of parameters.
Last Updated: 9/8/2018 | File Size: 2990 KB | Price: $15 | Downloads: 260 | Developer: Institute of Mathematics and Statistics
OS: Win2000,Win7 x32,Win7 x64,Win98,WinOther,WinVista,WinVista x...


Nonlinear analysis - DataFitting 1.7.29 Description:
DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting). 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.
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