The Estimates menu provides additional detail about model parameters. To better understand estimates, you might want to review JMP’s approach to coding nominal and ordinal effects. See Details of Custom Test Example, Nominal Factors in Statistical Details, and Ordinal Factors in Statistical Details).
Adds a report, called Prediction Expression, containing the equation for the estimated model. See Show Prediction Expression for an example.
Adds a report called Sorted Parameter Estimates that 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.
Adds a report that 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.) Adds a report called Indicator Function Parameterization, which gives parameter estimates when nominal effects in the model are parametrized using the classical indicator functions. See Indicator Parameterization Estimates.
Shows sums of squares as effects are added to the model sequentially. Conducts F tests based on the sequential sums of squares. See Sequential Tests.
Opens a window where you specify comparisons among effect levels. These 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. You can also conduct equivalence tests. See Multiple Comparisons.
For each main effect in the model, JMP produces a joint test on all of the parameters involving that main effect. This option is available only when the model contains interactions. See Joint Factor Tests.
For one or more values of the response, predicts values of explanatory variables. See Inverse Prediction.
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.