Take the tour ×  Suppose I run a bidirectional stepwise in R
with the model:  step(glm(y ~ a + b + c + d, poisson))  And the
result may be:  y ~ d + c null deviance: 263.6 100 residual
degrees of freedom residual deviance: 132.9 AIC: 648.3  I read
that if you run the line:  1-pchisq(residual deviance, residual
df)  and the result is "significant" (below 0.05), you need a
better model.  But, if the stepwise() choose the better model
using the Akaike criterion, it means that I can't have a better
model? what if I don't have any other variable or arrange of
variables?  The "best" model chosen by the stepwise it is not
necessarily a good model? How can I know this?  Maybe is a very
basic question, but I dont get it. Can anyone help me to
understand the basics of this?  share|improve this question
Stepwise regression has been discussed here a lot, including one
post from yesterday. See also search on stepwise. Stepwise does
not necessarily give the "best" model for any definition of
"best"; all subsets regression