Nick's suggestion (below) is an important one. If the relation of
your dependent variable to age is nonlinear, the first step should be
to investigate the nature of the nonlinearity. If you have done that,
and concluded that a single cubic polynomial is a reasonable
description, OK. Often, mechanically adding a quadratic term (and a
cubic term) to the model is not appropriate. Many people do that, and
produce poor analyses as a result.
Related to Rich's comment, in a multiple regression (or similar)
model, the definition of each regression coefficient includes the list
of all the other predictors.
David Hoaglin
On Wed, Jan 30, 2013 at 3:20 PM, Nick Cox <njcoxstata@gmail.com> wrote:
>
> All that said, there are quite possibly better ways of doing what you
> want, such as cubic splines or fractional polynomials, which are well
> supported in Stata.
>
> Nick
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