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.

A Chi-Square Test of Independence compares frequencies of one categorical variable for different values of a second categorical variable. The null hypothesis is that the relative proportions of one variable are independent of the second variable. The alternate hypothesis is that the relative proportions of one variable are associated with the second variable.

Analysis of variance assesses whether the means of two or more groups are statistically different from each other. This analysis is appropriate whenever we want to compare the means (quantitative variables) of groups (categorical variables). The null hypothesis is that there is no difference in the mean of the quantitative variable across groups (categorical variable), while the alternative is that there is a difference.

After implementing useful data management decisions, it is time to create visual representations of our data that help us better display our findings by graphing the variables we study. According to this week's assignment I use visual tools to display the variables and the relationships between them.

The following Python code is an example of how to make and implement data management decisions. In the previous assignment I already created categories for the relevant variables, as the dataset I study mostly have continuous data. I revise and alter some of the categories to be more similar to those used by World Bank and WHO.