Logistic regression with categorical data I'm trying to apply logistic regression to the data with binary predictor. But some of my variables are numerical and some are categorical. If I just do this in R I get the model where for every categorical variable I have coefficients and p-values for for variable's possible values except for the first.
How can I interpret such model?
And what is the best way of finding best model for such problem? 
 A: Your coefficients (which are log odds-ratios) are for a particular level of each variable stated relative to a reference category. So there is only one coefficient for a two-level predictor, as the coefficient represents the difference in the log-odds between two groups. 
That is, if you are interested in modelling likelihood of having a disease (outcome) based on gender (Male/Female) and smoking status (smoker/non-smoker), then the coefficient for gender is the log odds ratio for e.g. Males relative to Females; and the coefficient for smoking is for smokers relative to non-smokers.
For factor variables, R chooses reference groups by default as the first level of that category, which does depend on the order of the levels. (When importing factors, R does this alphabetically, which is why female and non-smoker are the reference categories above. But sometimes the levels may have been applied in a different way, so it's important to check.) See relevel if you want to see how change reference categories.
