Predicting the Probability of Being a Smoker: A Probit Analysis

This paper explains the probability of being a smoker, based on 23 variables, using a probit analysis model. Specifically, age, gender, marital status, location, race, risky behavior, health insurance coverage, obtaining routine medical care and highest degree obtained are the basis of the construction of the model. They are hypothesized to be significant factors. 14 variables are individually significant at the 5% and 1% level. The regressors are jointly significant at both levels. However, the probit marginal effects demonstrate that race and possessing a high school degree can affect the probability of smoking by -10% to 34%.