Poisson Regression
Save
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?
Poisson regression is used to model the number of events that occur within a fixed interval of time or space. It 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. Poisson regression can also be used to model the number of occurrences of a particular event over a period of time, such as the number of website visits or the number of customer purchases.
How Does Poisson Regression Work?
Poisson regression 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. This relationship is expressed in the following equation:
log(E(Y)) = β0 + β1X1 + β2X2 + ... + βkXk
where:
