# Poisson and Negative Binomial Regression

Poisson regression models count variables that assumes poisson distribution. When the count variable is over dispersed, having to much variation, Negative Binomial regression is more suitable.

## Introduction

A count variable is something that can take only non-negative integer values. Some examples of count variables could be: 1. Number of vehicles manufactured. 1. Number of phone calls arriving at a call center. 1. Number of patents granted.

## How to Implement Poisson Regression?

Poisson regression can be implemented in a similar manner as other Generalised Linear Models (GLMs), by adjusting the family argument to poisson.

library (MASS)
poissonModel <- glm(countResponse ~ pred1 + pred2, family="poisson", data=inputData) # poisson Model
summary (poissonModel) # model summary
predict(poissonModel, newdata, type="response") # predict on new data 

## How to Implement Negative Binomial Regression?

library (MASS)
negBinomModel <- glm.nb(countResponse ~ pred1 + pred2, data = inputData)) # negative Binomial model
summary (negBinomModel) # Model summary
predict (negBinomModel, newdata, type="response") # predict on new data