GapMinder Project

The project I enjoyed working on the most was GapMinder. During this project I learned how to look at data and make a hypothesis about the data trend, and determine if the data was correlation or causation. This project made me look deeper into data that is put out in front of you and see what information you can take out.

In my group's project we compared average income to body mass index (BMI) in men. When we looked at the data we investigated a few countries to see what made them an outlier. For example Nauru was a small country that had few people but most of them were overweight causing them to be extremely far away on the graph than other countries with similar income. In our conclusion we found that there was a direct correlation between the average income per person and BMI in men.

Overall this project has helped me understand that graphs can present information that may look like it has a significance but there could be more variables that play a role in the data shown. One way this is shown is by comparing BMI to life expectancy. It shows a similar trend to BMI and income. The life expectancy is higher because the hidden variable is income. We think that the higher income usually means more food is available to eat, but they also have better health care so people live longer even though they have a higher BMI.