# Poisson assumptions

I am dealing with a dependent variable that is either 0, 1 or 2 in theory it is unbounded and it can take values more than 2 so I am motivated to test Poisson model first.

The frequency counts for these outcomes, 0, 1 or 2 is as follows

  Var1   Freq
0      433301
1      989
2      10


The mean, turns out to be 0.002323279 , mean <- sum(tempdf3$pi*as.numeric(as.character(tempdf3$Var1))) ; mean <- round(mean,0) ; mean

The standard deviation is 0.04867576, sqrt(sum(((as.numeric(as.character(tempdf3$Var1)) - mean)^2)*tempdf3$pi))

If i round the mean and standard deviation, mean <- round(mean,0), and standard deviation stdD <- round(stdD,0)both are 0

So is it safe to try Poisson regression on this dataset, since both the mean and variance are the same ?

tempdf3 <- structure(list(Var1 = structure(1:3, .Label = c("0", "1", "2"
), class = "factor"), Freq = c(433301L, 989L, 10L), pi = c(0.997699746718858,
0.00227722772277228, 0.0000230255583697905)), .Names = c("Var1",
"Freq", "pi"), row.names = c(NA, -3L), class = "data.frame")

• It's not clear to me why you think rounding two different numbers off to 0 means they are actually the same. – jbowman Aug 26 '18 at 16:59
• @jbowman, Good question, I am not sure how to interept the mean value of 0.002323279 and standard deviation 0.04867576 when the outcome variable takes on any value from 0 to 1 or 2 or 10 – Science11 Aug 26 '18 at 17:02
• Perhaps en.wikipedia.org/wiki/Expected_value will help? – jbowman Aug 26 '18 at 17:04
• @jbowman, i know what expected value means, and the theory behind it, The expected value in a die experiement is 3.5 etc.. I am not not understanding what you are saying :) – Science11 Aug 26 '18 at 17:14
• The Poisson distribution with this mean indicates you should obtain only about one value exceeding 1 in a dataset this size, whereas you observe 10 of them. That's a significant difference. However, all this analysis is irrelevant because your regression model would not assume the response variable has a Poisson distribution: it assumes that the response conditional on the explanatory variables has a Poisson distribution. Thus, it looks like you're generating irrelevant (and possibly misleading) information with this line of inquiry. Consider post hoc GoF testing of a Poisson regression. – whuber Aug 26 '18 at 20:06

On a more practical note, there are R packages for estimations Negative Binomial dependent variable (glm.nb() in {MASS}) , etc. Also, you can test Poisson regression model for overdispersion, i.e. "mean = variance" against "mean < variance" in R. Check the {AER} package for dispersiontest().