Distribution of Data

I have a question about glm model fitting of my data. The distribution shape is likely to follow a poisson distribution, but the response variable is not count/rate, but continuous decimals with both positive an negative values （as shown below).

Could you please give me some suggestions that which one should be more appropriate to my case?

Best

LE

• Regular OLS regression only assumes that the DV is continuous, not anything about its shape. It assumes that the error (as measured by the residual) is normal. – Peter Flom Feb 12 '14 at 14:35
• OK! Thank you very much! I am not sure whether a Gaussian distribution is appropriate for this case. Should I try to fit a Gaussian model and exam the variances? @PeterFlom – user26221 Feb 12 '14 at 15:21
• First fit an OLS model, then examine the residuals. In R you can get nice plots of the model by something like (pseudo code): m1 <- lm(DV~IV) plot(m1) – Peter Flom Feb 12 '14 at 15:34
• To extent @PeterFlom's point, you've presented the unconditional sample distribution of the response but for GLMs (and regression models), the distributional assumption is conditional (that is, at a given combination of the independent variables, the distribution hold). Depending on the arrangement of the various x-variables, the marginal response might look quite different from the conditional one. [Also, why do you say the shape 'is likely to follow a Poisson distribution'? Since you also say it's continuous and can be negative, we can be quite certain it's not Poisson.] – Glen_b Feb 13 '14 at 23:40
• Thanks you very much for your help! I am not familiar with the unconditional distribution so I cannot identify it. Could you please give me some suggestions to model the data. Since GLM is not appropriate, what kind of model can I try? @Glen_b – user26221 Feb 17 '14 at 13:46