An excellent 2 paragraph summary.
However, I find Stokes et al. kind of confusing (although it's good bcs
it gives lots of SAS code). I prefer Long, JS Regression models for
categorical and limited dependent variables Both these cover a lot
more than just dichotomous DVs. Another great book on logistic is
Hosmer & Lemeshow Applied Logistic Regression.
Also, people should be aware that there are some new things in
LOGISTIC, but that some of these have not made there way into the index
that get when you get help from within a SAS session, and it's better to
look at the chapter The Logistic Procedure in the online SAS-Stat User's
gude (e.g. the glogit option only shows up here not in the index
>>> Neerav <neerav_monga@CAMH.NET> 11/23/2004 12:06:38 PM >>>
As Peter said, the odds ratio explains the association between and an
IV with a DV. It is as its name suggests, a ratio of 2 odds. So for
example, if you're looking at smoking status (yes or no) predicting
birthweight baby (yes or no), with 'no' being the reference category,
an odds ratio of 2.0 for example would say: The odds of a smoker
a low birthweight baby is 2.0 times the odds of a non-smoker having a
low birthweight baby. This is the categorical case.
As for continuous, it is the same interpretation except you're talking
about an odds ratio for a one unit change in your predictor (eg. age).
Again , its a ratio of the odds of one event occuring to the odds of
another event (smoker vs non-smoker in my example). Hope that helps,
if you need more information, you can look at "Categorical data
anlaysis using the SAS system", by Stokes, Davis & Koch. A very good