# How to interpret this normal qq-plot? I have tried to fit a glm to some weather data and I got this weird qq-plot. What could this possibly mean? I am aware of how various skewed error distribution qq-plots should look like, but what I have here seems more bizarre.

• What kind of GLM was it? GLMs shouldn't necessarily have normal residuals (see here). – gung - Reinstate Monica Apr 16 '15 at 21:06
• What is nF? Is it a limited range DV? – iacobus Apr 16 '15 at 21:28
• Is this a possible duplicate? (However, gung's point is relevant; the interpretation may not matter much, depending on omitted details about the GLM) – Glen_b Apr 17 '15 at 4:53
• Is this maybe a Poisson regression with small counts? – Glen_b Aug 27 '15 at 7:35
• Simulate data from the estimated regression model, calculate residuals, repeat 1000 times, and plot those simulated residuals as a comparison distribution. Then we can see if this is typical residuals from your model, or not. There is in general no reason to expect normal residuals from an GLM. – kjetil b halvorsen Aug 27 '15 at 10:35

## 1 Answer

You've got some non-normal residual tails over there, especially a very heavy right one. Might want to consider taking a transform of your dependent variable. A log transform might help quite a bit, but no way to tell for sure without seeing the actual variable distribution, along with the rest of the residual plots (for both dependent and independent variables).

Some resources that might help you:

http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html

https://www.youtube.com/watch?v=X9_ISJ0YpGw

• Standard transformations won't work, because they will not be sufficiently non-differentiable at a particular value. We should instead suspect some form of a mixture or perhaps heteroscedasticity, and model it accordingly. – whuber Apr 16 '15 at 21:17
• @whuber - fair point. I think I rushed into the suggestion because I was just working on a problem that had an initial qqplot similar to the one above and an exponentially-distributed dependent variable. To your point however, heteroscedasticity was present and it took the introduction of polynomials and interaction terms to deal with it. – habu Apr 16 '15 at 21:25
• This is actually a Poisson glm with log link. I've even tried quasi poisson, but that doesn't change much. – mackbox Apr 16 '15 at 22:28
• If you've used the log link, then the transformation is already factored in. As whuber said, this is one likely in need of dependent variable manipulation and/or introduction, so you'll have to dive deeper into the residual plots. – habu Apr 16 '15 at 22:32
• quasi-poisson doesnt change the estimated model parameters, it only affects hypothesis tests/standard errors/confidence intervals. – kjetil b halvorsen Aug 27 '15 at 10:36