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Publication date: 05/05/2023

Fit Curve

Fit Built-In Nonlinear Models to Your Data

The Fit Curve platform enables you to fit models that are nonlinear in the parameters. In many situations, especially in the physical and biological sciences, well-known nonlinear equations describe the relationship between variables. For example, pharmacological bioassay experiments can demonstrate how the strength of the response to a drug changes as a function of drug concentration. Sigmoid curves often accurately model response strength as a function of drug concentration. Another example is exponential growth curves, which can model the size of a population over time.

The Fit Curve platform provides predefined models, such as polynomial, logistic, probit, Gompertz, exponential, peak, pharmacokinetic, rate, and dissolution models. The use of predefined models means that you are not required to create model formulas or specify starting values for parameter estimates. To specify your own starting values and create model formulas, use the Nonlinear platform, which can fit specified nonlinear models. See Nonlinear Regression.

Figure 14.1 Example of Nonlinear Fit in the Fit Curve Platform 

Example of Nonlinear Fit in the Fit Curve Platform

Contents

Overview of the Fit Curve Platform

Example of the Fit Curve Platform

Launch the Fit Curve Platform

The Fit Curve Report

Model Comparison Report
Model Fit Report

Fit Curve Options

Model-Free Comparisons Reports

Model Fit Options

Test Parallelism
Compare Parameter Estimates
Equivalence Test

Additional Example of the Fit Curve Platform

Statistical Details for the Fit Curve Platform

Statistical Details for Fit Curve Models
Statistical Details for Dissolution Curve Analysis
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