CIA Global Demographics

Team C has chosen to write about CIA Global Demographics Set. The task is to propose 30 or more observations of ratio and interval scale data. Five countries were chosen to focus on and compare. Analyzing Nigeria, United States, Canada, Norway, and Brazil ought to prove interesting as they each have varying gross national product, life expectancy, unemployment, and literacy rates, and other opportunities. The 5-7 references will be from individual websites on each country as we learn more about them doing this research. Identifying a problem, issue, or opportunity from the data sets will be the focus of Team C.

Purpose of the Research By studying the data set provided by CIA Global Demographics, team C hopes to understand the drivers
However, you would assume that if life expectancy is good you would automatically expect that the economic health of the given country would be good as well. The research will attempt to analyze the demographic data provided by CIA Global Demographics and see if life expectancy is in fact affected by the population size, literacy of each country, unemployment rate, their exports, imports, natural resources, etc. Life expectancy is important to understand because it not only measures the economic health of the country, but most importantly it measures the health of the society. For example, based on the CIA Global Demographics life expectancy for the United States is 78 years old. In the paper, Variation in Life Expectancy During the Twentieth Century in The United States, authors David M. Smith and Benjamin S. Bradshaw research showed that the increase in life expectancy is largely due to the improvements we have made in the world of medicine.
Problem
Brazil is a good case study country because it has been growing economically over the past decades and can provide an example of growing economic success leading to higher life expectancy rates. Further statistical analysis will be needed for the data points to show numerical correlation between life expectancy when compared to Unemployment, literacy, and area/population. It will be important to keep the research within the past three decades to account for advances in medicine. The goal will be to show the correlation between regions growing economic prosperity, population density, and literacy on life expectancy rates and to draw conclusions from the data points. Further research in the correlation between unemployment being an economic indicator would be needed in addition to research in how this correlates to access to quality health care. The effects of overpopulation should be researched as well to show how population/area can affect life expectancy rates. Finally, how literacy rates can change economic success should be researched to show if attacking illiteracy can eventually lead to better life expectancy rates. While the research is intended to find correlations, it should be noted that one should not draw a broad conclusion on correlation of data points meaning there are other significant factors