Nonlinear Estimation Results - Quick Tab

Purpose of Nonlinear Estimation

Select the Quick tab of the Nonlinear Estimation Results dialog box to access the options described here.

Summary: Parameter estimates. Click the Summary: Parameter estimates button to create a spreadsheet containing the parameter estimates for the current model. Summary statistics for the current analysis will be displayed in the spreadsheet header.

Note: If the Asymptotic standard errors check box was selected on the Model Estimation dialog box - Advanced tab, the Summary: Parameter estimates button is replaced by the Summary: Parameters & standard errors button. Click this button to produce a spreadsheet containing the parameter estimates for the current model as well as the (asymptotic) standard errors of the parameter estimates, the respective t-values (parameters divided by standard errors), and the associated p-values.

Logistic Regression. Several additional summary statistics are displayed in the spreadsheet when logistic regression is performed. Odds ratios are displayed for each parameter estimate for a unit change in the predictor variable and for a change equal to the observed range of values of the predictor variable. If the Asymptotic standard errors check box was selected on the Model Estimation dialog box - Advanced tab, the spreadsheet will also show Wald's Chi-square statistics and associated p-values for testing the significance of the parameters. Upper and lower confidence limits for the parameter estimates and the odds ratios will also be displayed. These confidence limits are computed using the Alpha value specified in the Confidence intervals for parameter estimates box on the Results dialog box - Advanced tab.

Observed, predicted, residual vals. Click the Observed, predicted, residual vals button to produce a spreadsheet containing the residual values (i.e., observed values minus predicted values).

Fitted 2D function & observed values. Click the Fitted 2D function & observed values button to create a 2-dimensional graph of the observed values. This plot enables a 2-dimensional visual examination (i.e., qualitative evaluation) of the fit of the data to the model. It is useful for identification of outliers, which can then be marked in the Data Editor using the Brushing Tool button.

Fitted 3D function & observed values. Click the Fitted 3D function & observed values button to create a 3-dimensional graph with the observed values. This plot enables a 3-dimensional visual examination (i.e., qualitative evaluation) of the fit of the data to the model. It is useful for identification of outliers, which can then be marked in the Data Editor using the Brushing Tool button.