The Estimates menu provides additional detail about model parameters. To better understand estimates, you might want to review how JMP codes nominal and ordinal effects. See Details of Custom Test Example, Nominal Factors in Statistical Details, and Ordinal Factors in Statistical Details).
Shows or hides the Prediction Expression report, which contains the equation for the estimated model. See Show Prediction Expression for an example.
Shows or hides the Sorted Parameter Estimates report, which can be useful in screening situations. If the design is not saturated, this report is the Parameter Estimates report with the terms, other than the Intercept, sorted in decreasing order of significance. If the design is saturated, then Pseudo t tests are provided. See Sorted Estimates.
Shows or hides the Expanded Estimates report, which expands the Parameter Estimates report by giving parameter estimates for all levels of nominal effects. See Expanded Estimates.
(Available only when there are nominal columns among the model effects.) Shows or hides the Indicator Function Parameterization report, which contains parameter estimates with the nominal effects in the model parametrized using the classical indicator functions. See Indicator Parameterization Estimates.
Shows or hides the Sequential (Type 1) Tests report that contains the sums of squares as effects are added to the model sequentially. Conducts F tests based on the sequential sums of squares. See Sequential Tests.
Enables you to specify comparisons among effect levels. These comparisons can involve a single effect or you can define flexible custom comparisons. You can compare to the overall mean, to a control mean, or you can obtain all pairwise comparisons using Tukey HSD or Student’s t. When you specify the Student’s t method, you can also perform equivalence tests to identify pairwise differences that are of practical importance. See Multiple Comparisons.
(Available only when there is one nominal term, one continuous term, and their interaction effect for the fixed effects.) Produces a report that enables you to compare the slopes of each level of the interaction effect in an analysis of covariance (ANCOVA) model. See Compare Slopes.
(Available only when the model contains interactions.) For each main effect in the model, shows or hides a joint test on all of the parameters involving that main effect. See Joint Factor Tests.
Enables you to predict values of explanatory variables for one or more values of the response. See Inverse Prediction.
(Available only when the model contains mixture effects.) Produces parameter estimates for the Cox mixture model. Using these to derive factor effects and estimate the response surface shape relative to a reference point in the design space. See Cox Mixtures.