# Interpreting very small Exp(B) in multinomial logistic regression

I am having trouble interpreting the Exp(B) value in a multinomial logistic regression in which my outcome variable is categorical (3 categories) and my predictor is a scale variable. The Exp(B) value for Category 3 (reference is Category 1) is 8.849E-51, with confidence intervals of 2.734E-51 - 2.865E-50. There is a similar value for Category 2. This is the first time I have performed multinomial logistic regression so I might be misunderstanding but this very small Exp(B) doesn't appear to make sense from looking at my data. I have read this post but can't make sense of this result based on this.

• That estimate is nearly zero. The fact that the confidence interval includes a negative quantity is just a side-effect of using a method not especially suited to extremes. Nov 3, 2015 at 16:47
• @gung's comment clearly applies to mine. If the dash means comma, not minus, then the quantity is not negative at all. Nov 3, 2015 at 18:08
• Unless you show more information about your data and model, I don't see how we can easily comment further. It could be anything from coding error through misunderstanding what the model does to fitting an inappropriate model to your data. Nov 4, 2015 at 11:20
• Thanks. I don't see a model statement here. It looks as if this comes from some menu-driven software, say SPSS. 15 observations, 3 categories, 1 continuous predictor is a really small sample for this kind of exercise. More generally, the problem is to predict category from what you are calling "response"? Nov 4, 2015 at 11:46
• You have perfect separation between category 1 and the other two categories based on response. That would explain the lack of convergence of mlogit experienced by @NickCox as well as many of the strange results here, such as perfect goodness of fit tests and the huge coefficient of response.
– whuber
Nov 4, 2015 at 13:22

## 1 Answer

Answered in comments by whuber:

You have perfect separation between category 1 and the other two categories based on response. That would explain the lack of convergence of mlogit experienced by @NickCox as well as many of the strange results here, such as perfect goodness of fit tests and the huge coefficient of response.