# Count regression model Results

I am trying to draw some conclusions about the fitting of one model, but after looking at some examples in the internet I just can't get a hold of it, the interpretation of the results I mean. Since all of the example reach a different conclusion with results I see really similar. I have some count data with high variability so I tried fitting a negative binomial model to the data and got the following results

summary(monb)

Call:
glm.nb(formula = Counts ~ Hour + weekday, data = modtab, init.theta = 0.2910141397,

Deviance Residuals:
Min       1Q   Median       3Q      Max
-0.8156  -0.7714  -0.6790  -0.5941   2.5479

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)       -0.520449   0.284584  -1.829   0.0674 .
Hour              -0.007665   0.013999  -0.548   0.5840
weekdayDonnerstag  0.044863   0.343570   0.131   0.8961
weekdayFreitag    -0.742229   0.365964  -2.028   0.0425 *
weekdayMittwoch   -0.448842   0.381636  -1.176   0.2396
weekdayMontag     -0.493662   0.353680  -1.396   0.1628
weekdaySamstag    -0.006994   0.336181  -0.021   0.9834
weekdaySonntag    -0.235636   0.343460  -0.686   0.4927
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.291) family taken to be 1)

Null deviance: 383.88  on 633  degrees of freedom
Residual deviance: 375.59  on 626  degrees of freedom
AIC: 1067.4

Number of Fisher Scoring iterations: 1

Theta:  0.2910
Std. Err.:  0.0453

2 x log-likelihood:  -1049.4140


I think that the model does not fit the data properly but I really can say any valid arguments of why. Can you say what are the reasons for a bad fitting or if I am wrong what are the evidences for a good fitting by looking at this summary?

Thank you

Maybe you could plot the model with plot(monb) and look at the QQ plot and the residual vs. fitted plot.
If you have an alternative model, for instance glm.nb(Count~1, data=modtab), you can compare the AIC of both models.