Nonlinear Regression
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Stat > Regression > Nonlinear Regression

Use Nonlinear Regression to mathematically describe the nonlinear relationship between a response variable and one or more predictor variables. Specifically, use nonlinear regression instead of ordinary least squares regression when you cannot adequately model the relationship with linear parameters. Parameters are linear when each term in the model is additive and contains only one parameter that multiplies the term. Use this procedure for fitting models that are nonlinear in the parameters, storing regression statistics, examining residual diagnostics, generating point estimates, and generating prediction and confidence intervals.  

See Understanding Nonlinear Regression for an overview.

Dialog box items

Response: Enter the column containing the Y, or response variable.

Expectation Function: Enter the function that describes the relationship between the predictor(s) and the expected response value using one of these methods:

·    <Use Catalog>: Click to choose a predefined function.

·    <Use Calculator>: Click to use the calculator to enter a custom function.

·    Edit directly: Enter the function directly into the text box.

See Specifying an Expectation Function - Nonlinear Regression for more information.

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