I have the following result of a logistic regression:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.34260 0.41116 8.130 4.30e-16 ***
AgeGr18-22 -0.61845 0.28974 -2.135 0.032799 *
AgeGr28-30 -0.46384 0.27474 -1.688 0.091361 .
AgeGr31-35 0.38351 0.28102 1.365 0.172352
AgeGr36-40 -0.24538 0.25113 -0.977 0.328525
AgeGr41-50 0.11316 0.23918 0.473 0.636140
AgeGr51-high 0.49277 0.29597 1.665 0.095924 .
AutomobileGr1 0.61832 0.17570 3.519 0.000433 ***
AutomobileGr2-high -0.07095 0.37665 -0.188 0.850590
The dummy "AutomobileGr2-high" (having 2 or more automobiles) have a P-value 0.85 and I would like to drop it from the model.
Would it make a difference if I just drop the dummy (i.e. create a regression formula without this group) or if I put it into the reference group (our reference group in the example above is AutomobileGr0 - ppl that do not have a car, hence the combined reference group will be AutombileGr0 & AutombileGr>=2 -> ppl either without a car or with at least 2).
From what I read so far (http://www.ats.ucla.edu/stat/mult_pkg/faq/general/dummy.htm) it should make a difference on the intercept hence on the whole resulting target log odds.
What approach is suggested as best practice (or what are the pros and cons of the two approaches)?
