# Tag Info

Accepted

### How to deal with overdispersion in Poisson regression: quasi-likelihood, negative binomial GLM, or subject-level random effect?

Poisson regression is just a GLM: People often speak of the parametric rationale for applying Poisson regression. In fact, Poisson regression is just a GLM. That means Poisson regression is justified ...
• 64.1k

### Is it possible that the AIC and BIC give totally different model selections?

It is possible indeed. As explained at https://methodology.psu.edu/AIC-vs-BIC, "BIC penalizes model complexity more heavily. The only way they should disagree is when AIC chooses a larger model than ...
• 20.7k

### Diagnostic plots for count regression

This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation ...
• 8,409
Accepted

### Interpreting Poisson regression coefficients

Let's say you were hired last year by a firm on a starting salary of 100,000 dollars. After one year of excellent performance on the job, you receive a raise and your new salary is 120,000 dollars. ...
• 20.7k
Accepted

### Are over-dispersion tests in GLMs actually *useful*?

In principle, I actually agree that 99% of the time, it's better to just use the more flexible model. With that said, here are two and a half arguments for why you might not. (1) Less flexible means ...
• 21.4k
Accepted

### Log-likelihood function in Poisson Regression

In Poisson regression, there are two Deviances. The Null Deviance shows how well the response variable is predicted by a model that includes only the intercept (grand mean). And the Residual Deviance ...
• 4,796

### Are over-dispersion tests in GLMs actually *useful*?

Although this is my own question, I'm also going to post my own two-cents as an answer, so that we add to the number of perspectives on this question. The issue here is whether or not it is sensible ...
• 130k
Accepted

### Poisson regression appropriate?

Poisson regression does not appear to be appropriate in your case. First off, Poisson regression models counts, and your events are binary, so if at all, logistic regression would be more appropriate....
• 129k

### Is it possible that the AIC and BIC give totally different model selections?

Short answer: yes, it is very possible. The two apply different penalties based on the number of estimated parameters (2k for AIC vs ln(n) x k for BIC, where k is the number of estimated parameters ...
• 549