I decided to analyze Economic Data from the Federal Reserve Economic Data. This data included unemployment rate, inflation rate, presidential approval, and consumer confidence for the years 1948 to 2016. After using Google Fusion tables to look at relationships among the data, I decided to focus on presidential approval and consumer confidence in this shaded line graph because there is a clear correlation between these two elements. Presidential approval is the measure of the average approval poll rating for the incumbent president, and consumer confidence is measures the degree of optimism that consumers feel about the overall state of the economy and their personal financial situation.
When looking at this visualization, it’s easy to see the correlation between these two categories. The peaks and the valleys match up pretty consistently, and it makes sense that these two categories would be correlated. However, looking at the spreadsheet, I wasn’t able to see the relationship between them until I put it into a chart. Consumer confidence is on a higher scale than presidential approval is, so their numbers don’t match up, which makes it hard to see their relationship in the data set. It is easy to see through this chart that their slopes and changes over time do match up, suggesting that how much people approve of their present is related to how confident they are in the state of the economy. It is important to note that this visualization reveals a correlation and does not suggest any type of causation, but this information is still significant and shows how powerful a data visualization can be.
Written by Risha Sanikommu
Wow, that’s really striking, isn’t it? It reminds me of this slogan from a presidential campaign long ago and far away!
It is a very effective demonstration of how data visualization can reveal patterns not easily observable by looking at data only. I agree with you that the visualization can only indicate the correlation, rather than the causation, between the two variables.
I really enjoyed this post. Of all the different data types presented to you, it was interesting to see you relate presidential approval and consumer confidence. It correlated well, and I am curious to see what other correlations can be seen in this data. I am also interested in seeing whether any causations can be found through the data alone.
I really appreciate the part you included about correlation not equaling causation. Often when it comes to graphs people can just view similar trends and jump to conclusions (see here: http://www.tylervigen.com/view_correlation?id=359). But for data like this I think there definitely could be a causation component, it’s just something that a political scientist or economist would have to study, not something that can be revealed by a single visualization.