Contents
How are parameter estimates different from linear equations?
For nonlinear equations, determining the effect that each predictor has on the response can be less intuitive than it is for linear equations. Unlike the parameter estimates in linear models, there is no consistent interpretation for the parameter estimates in nonlinear models.
What are the parameter estimates for nonlinear regression?
If your nonlinear model contains only one predictor, assess the fitted line plot to see the relationship between the predictor and response. In these results, there is one predictor and seven parameter estimates. The response variable is Expansion and the predictor variable is temperature on the Kelvin scale.
When to use fitted line plot in nonlinear regression?
The correct interpretation for each parameter depends on the expectation function and the parameter’s place in it. If your nonlinear model contains only one predictor, assess the fitted line plot to see the relationship between the predictor and response.
When to use confidence intervals in nonlinear regression?
If you need to determine whether a parameter estimate is statistically significant, use the confidence intervals for the parameters. The parameter is statistically significant if the range excludes the null hypothesis value. Minitab cannot calculate p-values for parameters in nonlinear regression.
How are nonlinear equations different from linear equations?
Unlike linear regression, a nonlinear regression equation can take many forms. For nonlinear equations, determining the effect that each predictor has on the response can be less intuitive than it is for linear equations.
Can a Minitab calculate the null hypothesis for a nonlinear regression?
Minitab cannot calculate p-values for parameters in nonlinear regression. For linear regression, the null hypothesis value for every parameter is zero, for no effect, and the p-value is based on this value.