JMP 11 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Specialized Models
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
Capabilities Index
Specialized Models
• Nonlinear Regression with Built-In Models
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Nonlinear Regression with Built-In Models
Analyze Models with the Fit Curve Platform
In many situations, especially in the physical and biological sciences, well-known nonlinear equations describe the relationship between variables. For example, bioassay experiments are conducted by pharmacology researchers to understand how the strength of the response to a drug changes as a function of drug concentration. The Logistic family of curves often accurately describes how the response strength is related to drug concentration. Another example is exponential growth curves, which can predict the size of a population over time.
The Nonlinear platform’s Fit Curve personality provides predefined models, such as polynomial, logistic, Gompertz, exponential, peak, and pharmacokinetic models. Fit Curve also enables you to compare different groups or subjects using a variety of analytical and graphical techniques.
You might prefer to create your own nonlinear models, which include a model formula and initial parameter estimates. See
Nonlinear Regression with Custom Models section
for details.
Example of Nonlinear Fit in the Fit Curve Personality
Contents
Introduction to the Nonlinear Fit Curve Personality
Example Using the Fit Curve Personality
Launch the Nonlinear Platform
The Fit Curve Report
Fit Curve Options
Model Formulas
Test Parallelism
Compare Parameter Estimates
Equivalence Test