# OLS Assumptions - Errors are normally distributed

I am currently working on a research project based on the data of a big survey. I derived a variable set, which I would like to investigate. Before starting with it, I would like to check the assumptions of the OLS.

I read quite a bit about these assumptions but I am still not sure how I can interpret my output. I am currently hanging on the assumption "errors are normally distributed" I read that this assumption is not important if the sample size is big enough. Is it correct in this case? What would be your interpretation of the model's output and my attached plots? Is it enough to test the assumption?

I can´t do a log transformation of my DV as it ranges from 0 to 26 and I obviously would lose the observations which contain 0.

• I agree broadly with @Hadi's answer. You should be concerned with whether the overall functional form of the model makes sense. A residual versus fitted plot and added variable plots would be higher up my list of diagnostics than checking normality of residuals. In Stata, which you are using, those are rvfplot and avplots. Zeros in the response are no problem to generalised linear models with logarithmic link. Does your model ever predict negative values for the response in the range of the data? Can you remove some predictors without weakening the model much? It isn't very parsimonious. – Nick Cox Sep 6 '16 at 8:45
• Thank you @NickCox. How do I do logarithmic link of my DV without losing with a 0? No, the DV never turns negativ. Is it an Issue? Most of them add to the R2. I will check on that. Thanks for the mentioned plots. I will have a look on that. Thank You! Best, Carsten – Michael Meyer Sep 6 '16 at 15:13
• Carsten/Michael Check out glm, link(log) or poisson in Stata. blog.stata.com/2011/08/22/… should interest non-Stata users too. – Nick Cox Sep 6 '16 at 15:23