Here, at each step, a single predictor is eliminated from the current regression model if its removal would increase the sum-of-squares differences between the observed and the predicted values, by not more than F out (some properly chosen constant, typically between 2 and 4) times the residual mean square RSS n−p , where n is the number of observations and p is the number of predictors.
if they do, small errors or variations in the values of sample observations can have a large effect on the weights in the performance function (1).