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Curve Fitting in NCSS

Introduction

Curve fitting refers to nonlinear-regression techniques for fitting curved lines to X-Y data. You can select a standard model from the large list of pre-coded models or you can enter your own. If you do not have a specific model in mind, the program can search through hundreds of possible models looking for the one that best fits your data. The program will fit models with up to four independent variables. You can compare the fitted equations across groups of data using randomization tests and bootstrap confidence intervals.

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Features

> Bleasdale-Nelder model
> Bootstrap confidence intervals
> Double-Exponential model
> Exponential model
> Farazdaghi model
> Gompertz models
> Goodness of fit measures
> Group comparisons
> Holliday model
> Logistic model Lognormal model
> Marquart algorithm
> Michaelis-Menten
> Monomolecular model
> Morgan-Mercer model		 > Nonlinear regression
> Normal model
> Piecewise polynomials
> Probability plots
> Randomization test
> Ratio of polynomials
> Reciprocal model
> Richards model
> Scatter-plot matrix
> Search for best fit
> Sum of functions regression
> Transformation bias correction
> User-supplied models
> Weibull model

NCSS

Support Info

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