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.

Data set


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    $\begingroup$ 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. $\endgroup$ – Nick Cox Nov 3 '15 at 16:47
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    $\begingroup$ @gung's comment clearly applies to mine. If the dash means comma, not minus, then the quantity is not negative at all. $\endgroup$ – Nick Cox Nov 3 '15 at 18:08
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    $\begingroup$ 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. $\endgroup$ – Nick Cox Nov 4 '15 at 11:20
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    $\begingroup$ 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"? $\endgroup$ – Nick Cox Nov 4 '15 at 11:46
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    $\begingroup$ 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. $\endgroup$ – whuber Nov 4 '15 at 13:22

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