George, Edward. The tests themselves are biased, since they are based on the same data. Doi :.1214/aos/ Donoho, David., Johnstone, Jain. Usually the two thresholds are set to the same value.

Price, part 3: transformations of variables. The Risk Inflation Criterion for Multiple Regression. When there are no variables left to enter whose F -to-enter statistics are above the threshold, it checks to see whether the F -to-remove statistics of any variables added previously have fallen below the F -to-remove threshold. The variables are only read in once, and their correlation matrix is then computed (which takes only few seconds even if there are very many variables). Whenever a variable is entered, its new F -to-remove statistic is initially the same as its old F -to-enter statistic, but the F -to-enter and F -to-remove statistics of the other variables will generally all change. 15 16 Wilkinson and Dallal (1981) 17 computed percentage points of the multiple correlation coefficient by simulation and showed that a final regression obtained by forward selection, said by the F-procedure to be significant.1, was in fact only significant. Hence, this process is myopic, looking only one step forward or backward at any point. Prediction error and its estimation for subsetselected models.

Although stepwise regression is popular, many statisticians (see here and here ) agree that its riddled with problems and should not be used.

Stepwise regression can be achieved either by trying out one independent variable at a time and including it in the regression model if.

Stepwise regression is a systematic method for adding and removing terms from a multilinear model based on their statistical significance.

Stepwise regression is a semi-automated process of building a model by successively adding or removing variables based solely on the t-statistics.