Stepwise regression
（重定向自Backward elimination）
![In this example from engineering, necessity and sufficiency are usually determined by F-tests.
For additional consideration, when planning an experiment, computer simulation, or scientific survey to collect data for this model, one must keep in mind the number of parameters, P, to estimate and adjust the sample size accordingly. For K variables, P = 1(Start) + K(Stage I) + (K2 − K)/2(Stage II) + 3K(Stage III) = 0.5K2 + 3.5K + 1. For K < 17, an efficient design of experiments exists for this type of model, a Box–Behnken design,[9] augmented with positive and negative axial points of length min(2, (int(1.5 + K/4))1/2), plus point(s) at the origin. There are more efficient designs, requiring fewer runs, even for K > 16.](/Images/godic/202412/22/Stepwise3323.jpg")
In statistics, stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure. Usually, this takes the form of a sequence of F-tests or t-tests, but other techniques are possible, such as adjusted R-square, Akaike information criterion, Bayesian information criterion, Mallows's Cp, PRESS, or false discovery rate.