I want to know what is the best way to analyze a data set where my response variable is count data and my explanatory variables are continuous variables. All my variables are not normally distributed. Are GLMs a good option?

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#### Best Answer

They are. You may want to look at *Poisson regression* (in R: `glm(..., family=poisson, ...)`

) or, if you have overdispersion, *Negbin regression* or, if you have "too many" zeros, *ZIP regression* (Zero-Inflated Poisson).

Whether the *predictors* are normally distributed does not matter. (Except for analyses of influential data points.) What you probably have in mind is whether *residuals* are normally distributed. This is an important assumption in Ordinary Least Squares – more specifically: for inference in OLS. However, your data are counts, so residuals will not be normal and you are not thinking about OLS, anyway.

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