The waiting times for poisson distribution is an exponential distribution with parameter lambda. But I don't understand it. Poisson models the number of arrivals per unit of time for example. …

Poisson regression would be more suitible in this case because your response is the count of something. Putting things simply, we model that the distribution of number of awards for an …

I understand that for certain datasets such as voting it performs better. Why is Poisson regression used over ordinary linear regression or logistic regression? What is the mathematical …

I have count data (demand/offer analysis with counting number of customers, depending on - possibly - many factors). I tried a linear regression with normal errors, but my QQ-plot is not …

May 17, 2019 · A Poisson process can do much better since indeed it answer to the question : What is the probability to receive k k call per unit of time ? But also to the question : what is the …

Apr 7, 2020 · The arrival times of customers in a bakery can be modeled by a Poisson process (Nt)t≥0 with some rate λ > 0. On average, there are four arrivals per unit of time. I want to ask …

Here is an example of application. Poisson regression is typically used to model count data. But, sometimes, it is more relevant to model rates instead of counts. This is relevant when, e.g., …

I know of normality tests, but how do I test for "Poisson-ness"? I have sample of ~1000 non-negative integers, which I suspect are taken from a Poisson distribution, and I would like to …

This was in discussions of interpreting logistic regression coefficients, but Poisson regression is similar if you use an offset of time at risk to get rates. You add first all the coefficients …

Brief Summary Why is it more common for logistic regression (with odds ratios) to be used in cohort studies with binary outcomes, as opposed to Poisson regression (with relative risks)? …