What you’ll learn
- Poisson Regression analysis model is used for predictive analysis
- SAS Stat provides built-in functions to calculate and evaluate the Poisson regression model
- One of the use cases of a Poisson regression model would be predicting the number of leads that will convert to customers within a particular time frame
- Poisson regression is based on the concept of Poisson distribution
Poisson regression is based on the concept of Poisson distribution. It is another category belonging to the set of regression techniques that combines the properties of both Linear as well as Logistic regressions. However, unlike Logistic regression which generates only binary output, it is used to predict a discrete variable.
Poisson Regression in SAS is a type of regression analysis model which is used for predictive analysis where there are multiple numbers of possible outcomes expected which are countable in numbers. SAS provides built-in functions to calculate and evaluate the Poisson regression model. Poisson regression is useful to predict the value of the response variable Y by using one or more explanatory variable X. This is a preferred probability distribution which is of discrete type. One of the use cases of a Poisson regression model would be predicting the number of leads that will convert to customers within a particular time frame in an organization.
In Probability and Statistics, there are three distributions based on continuous and discrete data – Normal, Binomial, and Poisson Distributions. Normal Distribution is often seen as a Bell Curve. Poisson distribution is often referred to as the Distribution of rare events. This is predominantly used to predict the probability of events based on how often the event has happened. It allows for a given number of events occurring in a set of periods. It is used in many real-life situations.
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