Multinomial Logistic Regression: small groups I am implementing a Multinomial Logistic Regression, but I am encountering the possible issue of having very small groups when I create a frequency table of the dependent variable Y and one of the independent variables X (see table). Would this affect my results in a problematic way? Or should I for example just
exclude this variable X in my regression (and with that ignore that these 2 variables seem to "correlate" to some extent)?
-------- y0 --- y1

x0: --- 180 ---   2

x1: ---  50 ---   2

x2: --- 300 ---   4

x3: ---  50 --- 350

x4: ---  50 ---  20

 A: There is nothing wrong with taking different analytic and reporting approaches for different predictors.  For predictors other than X you might report regression coefficients, while for X you might simply say that Y1 events were rare when X was 0-2, uncommon when X = 4, and very common when X = 3.  But honestly the X-Y relationship seems so obvious, has such an interocular effect, that one might wonder why an investigation of it through regression would be necessary.  Sometimes analysts feel the need to include whatever variables can add to their model's fit, even if some such variables are more or less proxies for the outcome and thus add little in the way of explanation, or even hinder it.  
You will probably obtain more helpful answers if you can say what is your topic, what X and Y are, how many and what sorts of other predictors you are using, and whether you are analyzing for prediction or for explanation.
A: Working on very small groups can cause various technical issues. In that case you might want to take a look at penalised regression techniques (such as Firth's penalisation of the logistic regression) to deal with issue of data separation and finite-sample estimation bias.
