When you have a lot of predictors, the stepwise method can be useful by automatically selecting the "best" variables to use in the model. The stepwise method starts with a model that doesn't include any of the predictors. At each step, the predictor with the largest F to Enter value that exceeds the entry criteria (by default, 3.84) is added to the model. The variables left out of the analysis at the last step all have F to Enter values smaller than 3.84, so no more are added. This table displays statistics for the variables that are in the analysis at each step. Tolerance is the proportion of a variable's variance not accounted for by other independent variables in the equation. A variable with very low tolerance contributes little information to a model and can cause computational problems. F to Remove values are useful for describing what happens if a variable is removed from the current model (given that the other variables remain). F to Remove for the entering variable is the same as F to Enter at the previous step (shown in the Variables Not in the Analysis table).