Shows or hides a plot of the X and Y variables for models with only one X variable. The model shown on the plot is based on the current values of the parameters. To change the current values of the parameters, use the sliders or edit boxes beneath the plot. If you specify a Group variable at launch, then a curve shows for each group.

Tells JMP that if an ingredient column to the formula is a column that itself has a formula, to substitute the inner formula, as long as it refers to other columns. To prevent an ingredient column from expanding, use the Other column property with a name of “Expand Formula” and a value of 0.

Shows the derivatives of the nonlinear formula in the JMP log. See Notes Concerning Derivatives, for technical information about derivatives.

Shows the Prediction Profiler. The Profiler lets you view vertical slices of the surface across each x-variable in turn, as well as find optimal values of the factors.

Create a grid of values around the solution estimates and compute the error sum of squares for each value. The solution estimates should have the minimum SSE. When the option is selected, the Specify Grid for Output report is shown with these features:

Displays the minimum parameter values used in the grid calculations. By default, Min is the solution estimate minus 2.5 times the ApproxStdErr.

Displays the maximum parameter value used in the grid calculations. By default, Max is the solution estimate plus 2.5 times the ApproxStdErr.

When you click Go, JMP creates the grid of points in a new table. A highlighted row marks the solution estimate row if the solution is in the table.

Estimates the X value for a given Y value. It also calculates a standard error for the estimated X. JMP must be able to invert the model. The standard error is based on the first-order Taylor series approximation using the inverted expression. The confidence interval uses a t-quantile with the standard error, and is a Wald interval.

Saves asymptotic confidence limits for the model prediction. This is the confidence interval for the average Y at a given X value.

Saves asymptotic confidence limits for an individual prediction. This is the confidence interval for an individual Y value at a given X value.

Saves the standard error for a model prediction. This is the standard error for predicting the average Y for a given X. The formula is of the form Sqrt(VecQuadratic(matrix1,vector1)). matrix1 is the covariance matrix associated with the parameter estimates, and vector1 is a composition of the partial derivatives of the model with respect to each parameter.

Saves the standard error for an individual prediction. This is the standard error for predicting an individual Y value for a given X value. The formula is of the form Sqrt(VecQuadratic(matrix1,vector1)+mse). matrix1 is the covariance matrix associated with the parameter estimates, vector1 is a composition of the partial derivatives of the model with respect to each parameter, and mse is the estimate of error variance.

Saves the formula to calculate the confidence interval for a model prediction. This is a confidence interval for the average Y for a given X.