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I mainly want to make sure that I'm making the correct interpretation here.

I built a negative binomial regression model predicting a count variable. There was evidence of overdispersion or I would have used regular Poisson. The predictor variable (also a count) was skewed, so I transformed it using a natural log.

The results are coef=0.341 or IRR=1.407 (Incident rate ratio)

So, holding all other variables in the model constant, for every 1 log unit increase in the predictor, the rate ratio increases by 1.407? Or for every 1 unit increase in the predictor, the outcome will increase by 0.341 units? Is that correct?

Can I backtransform the results for an easier interpretation?

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  • $\begingroup$ Why did you transform the predictor? $\endgroup$
    – dimitriy
    Commented Sep 17, 2014 at 22:54
  • $\begingroup$ It is very skewed with some large outliers (more than 4 SD above the mean) $\endgroup$
    – Joe
    Commented Sep 19, 2014 at 18:26
  • $\begingroup$ And what problem does that cause with the NBR? $\endgroup$
    – dimitriy
    Commented Sep 19, 2014 at 18:38

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