Nonlinear regression analysis- DataFitting
- 14 years ago
Nonlinear regression analysis- DataFitting
DataFitting program performs statistical regression analysis to estimate the values of parameters for linear, multivariate, polynomial, exponential and nonlinear functions. The regression analysis determines the values of the parameters that cause the function to best fit the observed data that you provide. This process is also called curve fitting.
DataFitting gives engineers and researchers the power to find the ideal model for even the most complex data, by putting a large number of equations at their fingertips. DataFitting's built-in library includes a wide array of linear and nonlinear models from simple linear equations to high order polynomials. 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.
DataFitting program performs statistical regression analysis to estimate the values of parameters for linear, multivariate, polynomial, exponential and nonlinear functions. The regression analysis determines the values of the parameters that cause the function to best fit the observed data that you provide. This process is also called curve fitting.
DataFitting gives engineers and researchers the power to find the ideal model for even the most complex data, by putting a large number of equations at their fingertips. DataFitting's built-in library includes a wide array of linear and nonlinear models from simple linear equations to high order polynomials. 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.