The results of my linear regression model indicated that the level of female education in a country was significantly and negatively associated with the number of children dying under the age of five. But what other variables might explain more of this association and the variability in child mortality?

This time I will test and interpret this relationship using basic linear regression analysis for the two variable. Simple linear regression is a statistical method that allows us to describe data and explain the relationship between two quantitative variables

My sample provides values for under-five child mortality rate (used as response variable), mean years in school for women, per capita total expenditure on health and income per person (explanatory variables, moderators) for 167 countries from 2009 and 2010.

Moderation is the basic concept of statistical interaction. In statistics, moderation occurs when the relationship between two variables depends on a third variable. The effect of a moderating variable is often characterized statistically as an interaction.

I want to compare the child mortality rates against years of schooling for women for the 154 countries in the data set. Both response variable (under-five mortality rate per 1,000 live births) and explanatory variable (mean years of schooling for women, age 15 to 44) are quantitative variables, thus Pearson correlation coefficient (r) can be used.