The Stopping Rule determines which model is selected. For all stopping rules other than P-value Threshold, only the Forward and Backward directions are allowed. The only stopping rules that use validation are Max Validation RSquare and Max K-Fold RSquare. See Using Validation.
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Prob to Enter is the maximum p-value that an effect must have to be entered into the model during a forward step.
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Prob to Leave is the minimum p-value that an effect must have to be removed from the model during a backward step.
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Uses the minimum corrected Akaike Information Criterion to choose the best model. For more details, see Likelihood, AICc, and BIC in Statistical Details.
Uses the minimum Bayesian Information Criterion to choose the best model. For more details, see Likelihood, AICc, and BIC in Statistical Details.
Uses the maximum R-square from the validation set to choose the best model. This is available only when you use a validation column with two or three distinct values. For more information about validation, see Validation Set with Two or Three Values.Uses the maximum RSquare from K-fold cross validation to choose the best model. You can access the Max K-Fold RSquare stopping rule by selecting this option from the Stepwise red triangle menu. JMP Pro users can access the option by using a validation set with four or more values. When you select this option, you are asked to specify the number of folds. For more information about validation, see K-Fold Cross Validation.
Enters the term with the smallest p-value. If the P-value Threshold stopping rule is selected, that term must be significant at the level specified by Prob to Enter. See Forward Selection Example.
Removes the term with the largest p-value. If the P-value Threshold stopping rule is selected, that term must not be significant at the level specified in Prob to Leave. See Backward Selection Example.
Available only when the P-value Stopping Rule is selected. It alternates the forward and backward steps. It includes the most significant term that satisfies Prob to Enter and removes the least significant term satisfying Prob to Leave. It continues removing terms until the remaining terms are significant and then it changes to the forward direction.
Note: All Stopping Rules only consider models defined by p-value entry (Forward direction) or removal (Backward direction). Stopping rules do not consider all possible models.
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A hierarchy results when a variable is a component of another variable. For example, if your model contains variables A, B, and A*B, then A and B are precedent terms to A*B in the hierarchy.
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groups a term with its precedent terms and calculates the group’s significance probability for entry as a joint F test. Combine is the default rule. See Models with Crossed, Interaction, or Polynomial Terms.
restricts the terms that have precedents so that they cannot be entered until their precedents are entered. See Models with Nominal and Ordinal Effects and Example of the Restrict Rule for Hierarchical Terms.
enters only whole effects, when terms involving that effect are significant. This rule applies only when categorical variables with more than two levels are entered as possible model effects. See Rules.
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Creates a model for the Fit Model window from the model currently showing in the Current Estimates table. In cases where there are nominal or ordinal terms, Make Model can create new data table columns to contain terms that are needed for the model.
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Description of Current Model Statistics describes the statistics for the current model, which appear below the Stepwise Regression Control panel.
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Adjusts R2 to make it more comparable over models with different numbers of parameters by using the degrees of freedom in its computation. The adjusted R2 is useful in stepwise procedure because you are looking at many different models and want to adjust for the number of terms in the model.
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Mallow’s Cp criterion for selecting a model. It is an alternative measure of total squared error and can be defined as follows:
 where s2 is the MSE for the full model and SSEp is the sum-of-squares error for a model with p variables, including the intercept. Note that p is the number of x-variables+1. If Cp is graphed with p, Mallows (1973) recommends choosing the model where Cp first approaches p.
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Notice that the default selection for Direction is Forward.
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Click Step.
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From the top figure in Current Estimates Table for Forward Selection, you can see that after one step, the most significant term, Runtime, is entered into the model.
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Click Go.
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The bottom figure in Current Estimates Table for Forward Selection shows that all of the terms have been added, except RstPulse and Weight.
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Click Enter All.
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For Direction, select Backward.
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Click Step two times.
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The Current Estimates and Step History tables shown in Current Estimates with Terms Removed and Step History Table summarize the backwards stepwise selection process.
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Traditional test statistic to test that the term effect is zero. It is the square of a t-ratio. It is in quotation marks because it does not have an F-distribution for testing the term because the model was selected as it was fit.
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Significance level associated with the F statistic. Like the “F Ratio,” it is in quotation marks because it is not to be trusted as a real significance probability.
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As each step is taken, the Step History report records the effect of adding a term to the model. For example, the Step History report for the Fitness.jmp example shows the order in which the terms entered the model and shows the statistics for each model. See Example Using Stepwise Regression.
