User Guide

414
Chapter 26
the value of the weighting variable is zero, negative, or missing, the case is excluded
from the analysis.
Linear Regression Va riable Selection Methods
Method selection allows you to specify how independent variables are entered into
the analysis. Using different methods, you can construct a variety of regression
models fro
m the same set of variables.
 Enter (Reg
ression).
A procedure for variable selection in which all variables in
a block are entered in a single step.
 Stepwise. At each step, the independent variable not in the equation which has
the smallest probability of F is entered, if that probability is sufficiently small.
Var iabl es
already in the regression equation are removed if their probability of
F becomes sufficiently large. The method terminates when no more variables
are eligible for inclusion or removal.
 Remove. A procedure for variable selection in which all variables in a block
are remov
ed in a single step.
 Backwar
d Elimination.
A variable selection procedure in which all variables are
entered into the equation and then sequentially removed. The variable with the
smallest partial correlation with the dependent variable is considered first for
removal.
If it meets the criterion for elimination, it is removed. After the first
variable is removed, the variable remaining in the equation with the smallest
partial correlation is considered next. The procedure stops when there are no
variabl
es in the equation that satisfy the removal criteria.
 Forwar
d S election.
A stepwise variable selection procedure in which variables are
sequentially entered into the model. The first variable considered for entry into
the equation is the one with the largest positive or negative correlation with the
depend
ent variable. This variable is entered into the equation only if it satisfies
the criterion for entry. If the first variable is entered, the independent variable not
in the equation that has the largest partial correlation is considered next. The
proced
ure stops when there are no variables that meet the entry criterion.
The sig
nificance values in your output are based on fitting a single model. Therefore,
the significance values are generally invalid when a stepwise method (Stepwise,
Forward, or Backward) is used.