May 1, 2024
4 minute read
Poisson regression is a statistical technique used to model the number of events that occur within a fixed interval of time or space. It is a type of generalized linear model (GLM) that assumes that the outcome variable is a count variable, and that the logarithm of the mean of the outcome variable is a linear function of the predictor variables. Poisson regression is commonly used in fields such as epidemiology, healthcare, insurance, and finance to model the occurrence of events such as the number of hospital admissions, the number of insurance claims, or the number of financial transactions over a given period of time.
What is Poisson Regression Used For?
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Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Poisson Regression.
Seminal work on generalized linear models, providing the theoretical foundation for Poisson regression.
Comprehensive overview of Poisson regression and related models, suitable for advanced undergraduates and graduate students.
Detailed treatment of Poisson regression in the context of epidemiology, including case studies and applications.
Comprehensive and modern approach to regression modeling, including Poisson regression.
Comprehensive textbook on regression models for non-continuous outcomes, including Poisson regression.
Accessible introduction to Poisson regression, ideal for beginners and non-statisticians.
Advanced econometric treatment of count data models, including Poisson regression.
Practical guide to statistical methods in medical research, including Poisson regression.
Accessible and practical guide to Poisson regression for social scientists, with a focus on understanding the results.
Advanced treatment of extreme value distributions, including applications in various fields.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/ubjo51/poisson