# How to transform my sparse count data into normal distribution?

I am running glm on beetle counts data. My predictors are environmental variables and my response variable is the number of beetles.

I ran three glms:

1. The response variable $Y_1$ is the total number of beetles.

2. The response is a subset of $Y_1$ ($Y_2$).

3. The response is also a subset of $Y_1$ ($Y_3=Y_1-Y_2$). In this $Y_3$, there are many zeros in my distribution, so the residual distribution is very far from normal.

How can I transform $Y_3$ to meet the assumption of normality? Does anyone have an equivalent robust non-parametric test?

• If your outcome is a count is there any reason why you are avoiding Poisson regression? Commented May 29, 2018 at 10:56
• Or negative binomial regression, etc... Commented May 29, 2018 at 12:11
• thanks for your answers. I log-transformed to normalize my response variable and diagnosed the model by checking for residuals normality... what is wrong. I will do a simple one glm(Y~X, family=poisson) and check for the goodness-of-fit the model. Commented May 30, 2018 at 6:16