Categorical Variable and Logistic Regression I ran a really simple logistic regression and want to know the X% increase in odds for each of my categorical variables. My equation is $Honors=f(Status)$ such that $Status$ is Low, Medium or High. The output of my results shows the following
model$coeffiecents
(Intercept)   Low   Medium
1.07366       0.284  0.241

I transformed the coefficients into odds using $e^{coefficient}$. So holding all things constant, we see the odds of being in Honors for Low is 27.3%($e^{0.284}-1$). The odds for being in Honors for Medium is 32.85%. Does this mean the odds for being in Honors for High is 1-(27.3%+32.85%) = 39.89%?
What if I wanted to get the probability of being in Honors given you are High? I can get Medium and Low as $\frac{e^{coefficient}}{1+e^{coefficient}}$ but how would I get High?
 A: In your case, "High" is taken as the reference level, and the log-odds, or coefficients are calculated relative to that. So the probability for High will be $\frac{e^{intercept}}{1+e^{intercept}}$ whereas that for other group is $\frac{e^{intercept + coef}}{1+e^{intercept + coef}}$ .
We can simulate data with known probabilities, high = 0.74, medium = 0.79 and low = 0.81:
set.seed(111)
true_p = c(0.74,0.79,0.81)
y = rbinom(1500,size=1,prob=rep(true_p,each=500))
type = rep(c("High","Medium","Low"),each=500)

fit = glm(y ~ type,family=binomial)
    Coefficients:
(Intercept)      typeLow   typeMedium  
     1.0356       0.4944       0.4015

The probability for high is:
exp(coefficients(fit)[1])/(1+exp(coefficients(fit)[1]))
(Intercept) 
      0.738

Which is close to what we simulated. You can work out for low:
exp(1.0356+0.4944)/(1+exp(1.0356+0.4944))
[1] 0.8220063

or you can fit without an intercept, and it gives you the log-odds:
Call:  glm(formula = y ~ 0 + type, family = binomial)

Coefficients:
  typeHigh     typeLow  typeMedium  
     1.036       1.530       1.437 

fit = glm(y ~ 0+type,family=binomial)
exp(coef(fit))/(1+exp(coef(fit)))
  typeHigh    typeLow typeMedium 
     0.738      0.822      0.808 

