Introduction Lecture 13 This lecture deals with the modeling of dependent variables that are event count count data. An event count refers to the Poisson Regression number of times an event occurs and is Using Stata he realization of a nonnegative integer- valued random variable. Variables that count the number of times that something has happened are common in the social This lecture covers Some examples of count variables are the number of times in a year that persons The Univariate Poisson Distribution visit the doctor. the number of car accidents that occur each day in a city, the The Poisson Regression Model number of love affairs that occur to a The Negative binomial distribution niversity student during the four years, Negative Binomial Regression the number of industrial injuries in the Comparing the Poisson and Negative workplace in a day, the number of Binomial Regression Models cigarettes a person smokes in a day, etc1 1 Lecture 13 Poisson Regression Using Stata 2 This lecture covers • The Univariate Poisson Distribution • The Poisson Regression Model • The Negative Binomial Distribution • Negative Binomial Regression • Comparing the Poisson and Negative Binomial Regression Models 2 3 Introduction • This lecture deals with the modeling of dependent variables that are event count or count data. An event count refers to the number of times an event occurs, and is the realization of a nonnegative integer￾valued random variable. Variables that count the number of times that something has happened are common in the social sciences. 4 • Some examples of count variables are the number of times in a year that persons visit the doctor, the number of car accidents that occur each day in a city, the number of love affairs that occur to a university student during the four years, the number of industrial injuries in the workplace in a day, the number of cigarettes a person smokes in a day, etc