Applying Modified Chi-Square and Logistic Regression to Predict Smoking Status Based on Socio-Demographics

Smoking is still a major public health concern, and little is known about how sociodemographic characteristics like age, income, and education affect smoking behavior. In order to close these gaps, data from 1,691 respondents in the United Kingdom were analyzed using a modified chi-square test combined with logistic regression. By integrating the probabilities associated with each variable, logistic regression enhances the accuracy of the chi-square test and permits a more in-depth examination. The findings indicate a robust correlation between smoking status and sociodemographic characteristics, with greater smoking rates being strongly connected with older age, lower income, and education levels. According to these results, smoking prevalence may be significantly decreased by public health initiatives that focus on wealth inequality and education.