Nonlinear Regression

Nonlinear regression unveils complex data relationships effortlessly, facilitating precise insights for researchers
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Easily fit, assess, plot and report on any nonlinear model

Prism has over 100 pre-built nonlinear models such as this dose-response curve.

Whatever your appliaction for a nonlinear model, Prism can help.

Prism makes it easy to fit a model to your data

Nonlinear Regression in Prism

Prism automatically graphs the best-fit curves from nonlinear regression
1

Enter Your Data

Prism offers easy-to-understand data table formats to enter the values from your experiment.
Prism can even handle data with missing values by leaving the cells blank.
2

Run Your Analysis

Prism makes it straightforward to fit a nonlinear model.

Gain insights and guidance at every step so you make the right analysis choices, understand the underlying assumptions, and accurately interpret your data along the way.

3

Custom Visualizations

Go from data to elegant, publication-quality graphs - with ease.
Prism makes it easy to collaborate with colleagues, receive feedback from peers, and share your research with the world.

Involve your team at any step in the process.

Share your data, analyses, and graphs in one click.

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Nonlinear Regression Highlights

Prism is purpose-built for scientists, and when it comes to nonlinear regression, Prism is the industry favorite tool.

With a consistently clear, practical, and well-documented interface, Prism gives you the controls you need to fit your data.

In addition to offering an extensive library of prebuilt nonlinear models, Prism lets you customize models as needed. You can easily constrain parameters, identify outliers, assess statistical significance, and visualize report results.

Nonlinear regression is used for modeling a wide array of physical, chemical, and biological processes such as:

  • Modeling dose-response curves or answering other pharmacokinetic questions.
  • Fitting growth or decay models for populations of interest.
  • Modeling enzyme inhibition for drug development.
  • Evaluating a saturation binding experiment, wherein you vary the concentration of radioligand.

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