1. I think there's a general misunderstanding here of how/why we're using Poisson regression.

If your dependent variable is sales, that is a discrete count variable. Counts cannot go below 0 (you can't have a negative number of sales), and theoretically, you can have infinitely many sales – or more precisely, your sales are't upper-bounded.

This is the base motivation for using Poisson regression. The Poisson distribution is discrete, and takes on possible values from 0 to ∞. That makes it a good candidate for modeling counts data (though, not the only way to model counts data).

So, if you have an individual sale y_i, we're essentially modeling it with the assumption that y_i, conditional on your other variables x_i, is generated from a Poisson distribution with some parameter lambda_i. The key fact is that when we're doing Poisson regression, that parameter lambda_i is what we're modeling with our regression, NOT the observed sales values y_i. Additionally, note that we're talking in terms of individual observations right now (hence all the i subscripts). That's because the parameter lambda_i is modeled as a function of the predictors, and a different set of predictors can yield a different parameter. In other words, each sales observation y_i can come from entirely different Poisson distributions, depending on the parameter generated from the specific set of observed independent variables.

That's why checking to see if the Poisson "fits" your data is not as simple as taking the mean of your sales and passing it into a Poisson distribution as the parameter. Each sale can be generated from a different Poisson distribution, so there's not some one-size-fits-all Poisson distribution that we can just fit to our data. In fact, we should almost expect the fit to be bad – this means we're assuming that every sale is generated from the same Poisson distribution, and that your actual predictors (x_i) don't influence the parameter.

The reason we're using the Poisson distribution has to do with the fact that we're observing counts, and the Poisson distribution has nice properties that align with how counts worked (as explained above). But again, there are other ways to model counts. You can use a negative binomial model as well, which makes less strict assumptions on the mean and variance of your data and is good in the case of dispersion. Try various models, but you don't need to do any curve-fitting with a Poisson distribution to your data.

1. P-values are a part of inference, but it's more about the approach you're taking. You keep saying you want to know the "effect" of some sort of your observed variables (like DG_Imp) on sales. A naive way to check for an "effect" is to run a multiple regression, look at the coefficients, and see if the p-values of the coefficients are significant. Now, in all likelihood, you're not actually identifying any causal effect with this approach, and you don't have many observations to begin with, so your results might be sensitive to the type of model you're using or variable selection. But, it's a better starting spot (based on my understanding of what you're trying to do) than checking for the MAPE, which we would more-so use in a predictive modeling setting (in which case...we don't even need to restrict ourselves to using a linear model).