Abstract
This study attempts to model smoking behavior in the United States using Current Population Survey data from 2010 and 2011. An array of demographic and socioeconomic variables is used in an effort to explain smoking behavior from roughly 139,000 individuals. Two regression techniques are employed to analyze the data. These methods found that individuals with children are more likely to smoke than individuals without children; females are less likely to smoke than males; Hispanics, blacks, and Asians are all less likely to smoke than whites; divorcees and widows are more likely to smoke than single individuals; married individuals are less likely to smoke than singles; retired individuals are less likely to smoke than working ones; unemployed individuals are more likely to smoke than working ones; and as education level increases after high school graduation, smoking rates decrease. Finally, it is recommended that encouraging American children to pursue higher education may be the most effective way to minimize cigarette smoking.
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10 August 2019
In the original version of this chapter, the name of the second author was misspelt. This has now been corrected to “Mitchell Reavis”.
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Kor, AL., Reavis, M., Lazarevski, S. (2019). Data Analytics: A Demographic and Socioeconomic Analysis of American Cigarette Smoking. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 998. Springer, Cham. https://doi.org/10.1007/978-3-030-22868-2_11
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