# Interpretation of Multiple Logistic Regression with Categorical Variable

I'm currently trying to interpret multiple logistic regression with a categorical variable.

Description of variables:

• region = the beneficiary’s residential area in the US; a factor with levels northeast, southeast, southwest, northwest.

• charges_cat = which takes the value 0 (low) when charges are less than 10000 dollars and the value 1 (high) in all other cases.

• bmi = body mass index of primary beneficiary in Kg/m2.

> logm2<-glm(charges_cat~bmi+region, family=binomial)

Coefficients:
Estimate
(Intercept)     -0.754605
bmi              0.026294
regionnorthwest -0.180464
regionsoutheast -0.244276
regionsouthwest -0.292365


My interpretation for b2 = regionnorthwest is:

> exp(-0.180464)
[1] 0.8348827


Given that southeast and southwest regions (dummy variables) and also bmi is fixed, the odds of charges being more than 10000 dollars is 16.51% lower than the odds of charges being more than 10000 dollars for a beneficiary who lives in the northeast region of the US.

My question is: in multiple logistic regression should I state the factor levels of the region are fixed, such as “southeast and southwest regions are fixed.” or there is no need to state the dummy variables of the region fixed?

Also, any other way to interpret b2?