Data Analytics Workouts

Responses to a post on the correlation between country-average IQ and responding yes to a question on if religion matters are inversely correlated, but not strongly so, prompted me to dig up a more significant issue, the relationship between religiosity and inequality, as measured by the Gini coefficient. The correlation is quite high, at about .7, although this really says nothing about the cause, if religious countries tend toward inequality because of general tendencies, or if inequality drives people to religion, as a salve against suffering. In truth, they could both be reflective of some other aspect of a country, and not in any way causative. Example Code # Correlations on ReligionMatters and Gini Coeficients oecdData <- read.table("OECD - Quality of Life.csv", header = TRUE, sep = ",") #names(oecdData) religionMattersVector <- oecdData$ReligionMatters giniVector <- oecdData$Gini cor.test(giniVector, religionMattersVect...

At a correlation approaching 0.7, the relationship between infant mortality and inequality is quite high. One can argue causality, but the existence of the relationship, and there are others of varying magnitude, is a powerful indictment: Example Code oecdData <- read.table("OECD - Quality of Life.csv", header = TRUE, sep = ",") gini.v <- oecdData$Gini death.v <- oecdData$InfantDeath cor.test(gini.v, death.v) plot(gini.v, death.v, col = "blue", main = "Infant Death v Gini" , abline(lm(death.v ~ gini.v)) , cex = 1.3, pch = 16, xlab = "Gini", ylab = "Infant Death") Example Results Pearson's product-moment correlation data: gini.v and death.v t = 4.2442, df = 19, p-value = 0.0004387 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.3805316 0.8679275 sample estimates: cor 0.69762 Example Graph Sample ...