Poisson Regression

Poisson regression is used when dependent variable has count data.

Application of Poisson Regression –
1. Predicting the number of calls in customer care related to a particular product
2. Estimating the number of emergency service calls during an event

The dependent variable must meet the following conditions
1. The dependent variable has a Poisson distribution.
2. Counts cannot be negative.
3. This method is not suitable on non-whole numbers

In the code below, we are using dataset named warpbreaks which shows the number of breaks in Yarn during weaving. In this case, the model includes terms for wool type, wool tension and the interaction between the two.

pos.model<-glm(breaks~wool*tension, data = warpbreaks, family=poisson) summary(pos.model)