Poisson regression is similar to multinomial logistic regression in that the dependent variable can take only non-negative integer values. With multinomial logistic regression the dependent variable takes values 0, 1, …, r for some known value of r, while with Poisson regression there is no predetermined r value, i.e. any count value is possible.
Topics
- Basic Concepts
- Using Solver to estimate the coefficients
- Residuals and Goodness of Fit
- Predictions
- Using Newton’s method to estimate the coefficients
- Real Statistics data analysis tool
In addition, proofs of some properties can be found on the following webpage. These proofs use calculus.
References
Hintze, J. L. (2007) Poisson regression. NCSS
https://web.archive.org/web/20220925185213/https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Poisson_Regression.pdf
Nussbaum, E. M., Elsadat, S., Khago, A. H. (2007) Best practices in evaluating count data, Chapter 21: Poisson regression.
http://www.academia.edu/438746
Penn State (2017) Poisson regression. STAT 504: Analysis of discrete data.
https://online.stat.psu.edu/stat504/lesson/9
Hintze, J. L. (2007) Poisson regression. NCSS
https://web.archive.org/web/20220925185213/https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Poisson_Regression.pdf
(Archived by web.archive.org)
Thank you. I have updated the reference.
Charles