Perform stepwise regression for Binary Logistic Regression (Stepwise tab)

On the Stepwise tab of the Binary Logistic Regression dialog box, select the stepwise regression method.
Stepwise
Method
Stepwise regression removes and adds terms to the model for the purpose of identifying a useful subset of the terms. For more information, go to Basics of stepwise regression.
Select one of the following stepwise methods that Minitab uses to fit the model:
  • None: Fit the model with all of the terms that you specify in the Model dialog.
  • Stepwise: This method both adds and removes predictors for each step. Minitab stops when all variables not in the model have p-values that are greater than the specified alpha-to-enter value and when all variables in the model have p-values that are less than or equal to the specified alpha-to-remove value.
  • Forward selection: This method starts with an empty model and adds the most significant term for each step. Minitab stops when all variables not in the model have p-values that are greater than the specified alpha-to-enter value.
  • Backward elimination: This method starts with all potential terms in the model and removes the least significant term for each step. Minitab stops when all variables in the model have p-values that are less than or equal to the specified alpha-to-remove value.
Alpha to enter
Enter the alpha value that is used to determine when a new term is entered in the model. You can set this value when you choose the Stepwise of Forward selection methods.
Alpha to remove
Enter the alpha value that is used to determine when a term is removed from the model. You can set this value when you choose the Stepwise or Backward elimination methods.
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