Predicting the Effect of Parental Education and Income on Infant Mortality Through Statistical Learning

Parental education, income per capita and health service indicators are the three most important determinants of child mortality. In this paper, we explored the influence of parental education and per capita income on infant mortality rate (IMR) using higher degree polynomial ridge regression. The polynomial regression analysis draws valid inferences about IMR based on an analysis of a representative sample of infants. Results from such analysis can be generalized to the larger population which is a predictive model in the form of a set of equations. This study estimated the comparative importance of mean years of male schooling, female schooling and per capita income on reducing the IMR with the statistical learning from the regression perspective. Results and analysis shows the importance of the parental education levels in reducing IMR. Moreover, female education, especially in lower grades are found significantly important in reducing IMR.