My independent variables were highly skewed, so to normalise the distribution they were log transformed. Also since there were zeros in the data, I've added + 1 to transform the variables. This is what the model looks like (negative binomial regression):
Dependant_var ~ log(Independent_var_1 + 1) + log(Independent_var_2 + 1)
Est. Std. Err. z-value sig. log(Independent_var_1 + 1) 0.031907 0.004701 6.787 1.14e-11 *** log(Independent_var_2 + 1) -0.019007 0.004735 -4.015 5.96e-05 ***
log(Independent_var_1 + 1) 1.0324219 log(Independent_var_2 + 1) 0.9811724
Now, I'm having problems understanding how to interpret the results. If the data were not log transformed, I would interpret this as follows:
If everything else is held constant, a one unit increase in
Independent_var_1 would result in the decrease by
0.031 units of
Dependent_var. And for IRRs – a one unit increase of
Independent_var_1 will result in an expected increase of the
Dependent_var by a factor of 1.032 (everything else constant).
However, I'm confused since I don't have "units" anymore, but log transformed vars. Thanks.