A binary logistic regression analysis was performed
to predict high school
students’ cigarette smoking behavior from selected
predictors from 2009 CDC Youth
Risk Behavior Surveillance Survey. The specific target student behavior of interest was
frequent cigarette use. Five predictor variables included in the model were: a) race, b)
frequency of cocaine use, c) initial cigarette smoking age, d) feeling sad or hopeless, and
e) physically inactive behavior. The results of the logistic regression analysis showed
that the full model, which considered all the five independent variables together, was statistically significant. . The strongest predictors of youth smoking behavior were race,frequency of cocaine use and physically inactive behavior. For example, the odds of
smoking are increased by a factor of 5.0 if the student is White compared to an African
American, controlling for other variables in the model. The logistic model employed
explained about 31% of the variance in current frequent cigarette use among the high
school students. It correctly classified 93% of the
cases. The key finding is that the
selected variables are important correlates of frequent cigarette use among high school
students.