Abstract
Business analysts often encounter data on variables which take values 0, 1, 2, … such as the number of claims made on an insurance policy; the number of visits of a patient to a particular physician; the number of visits of a customer to a store; etc. In such contexts, the analyst is interested in explaining and/or predicting such outcome variables on the basis of explanatory variables.
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Notes
- 1.
The maximum log likelihood when μ i = y i is given by: ∑i(y i log(y i) − y i − log(y i!)).
- 2.
http://www.statistics.ma.tum.de/fileadmin/w00bdb/www/czado/lec6.pdf. Accessed on May 11, 2018.
- 3.
https://www.casact.org/pubs/proceed/proceed59/59159.pdf. Accessed on May 11, 2018.
- 4.
ftp://cran.r-project.org/pub/R/web/packages/AER/AER.pdf. Accessed on May 11, 2018.
- 5.
http://people.stern.nyu.edu/jsimonof/AnalCatData/Data/Comma_separated/. Accessed on May 11, 2018.
- 6.
https://onlinecourses.science.psu.edu/stat504/node/170. Accessed on Apr 15, 2018.
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Krishnan, T. (2019). Count Data Regression. In: Pochiraju, B., Seshadri, S. (eds) Essentials of Business Analytics. International Series in Operations Research & Management Science, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-68837-4_13
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DOI: https://doi.org/10.1007/978-3-319-68837-4_13
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