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. How i...

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 motivation ...

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 (including the intercept term) times eachcovariate values and then exponentiate the resulting sum.

Would like to know how confident I can be in my λ λ. Anyone know of a way to set upper and lower confidence levels for a Poisson distribution? Observations (n n) = 88 Sample mean (λ λ) = 47.18182 what would the 95% confidence look like for this?

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 really goo...

Sep 18, 2019 · This is because the CDF of Poisson distribution is related to that of a Gamma distribution. Hence the incomplete gamma function.

Moment Generating Function of Poisson Ask Question Asked 10 years, 9 months ago Modified 5 years, 5 months ago

Nov 6, 2023 · The Poisson distribution implies z ∼ N(0, 1) z ∼ N (0, 1) so a one-sample t t test can provide a P -value for testing Poisson vs. negative binomial. Another test for equidispersion is the Lagrange Multiplier (∑(μ2 i) − ny¯)2/(2 ∑μ2 i) (∑ (μ i 2) n y) 2 / (2 ∑ μ i 2) which follows a one-degree χ2 χ 2 distribution under the null.

Jul 29, 2022 · I did the over-dispersion test for my Poisson regression model in R, to check whether negative binominal is a better option. I used stats package for conducting Poisson regression and AER package for testing overdispersion.

Dec 11, 2014 · Poisson distribution a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.