Cookies And Guns

To cap off the year, our final math project was based around the topic of correlation vs. causation. I have never learnt about this before so I was curious as to what the unit would consist of. We started by learning what the two terms actually mean. We learned that causation refers to when one statistic directly influences another to fluctuate in the same direction as it does, and that correlation refers to when there are two or more statistics that fluctuate in reasonably the same way, however the similar fluctuation is not caused by one another.

To grasp a better understanding of the terms, we looked at some graphed examples of both causation and correlation. The example that really made things easier to understand for me was the correlation one that was about shark attacks and ice cream sales. Obviously ice cream sales don’t directly impact the number of shark attacks, and because they look the same on a graph, there is a correlation between them. Also, in this case, there is an outside force that is most likely the reason the two stats move the same way; the fact that summer brings more ice cream sales as well as shark attacks.

Once we understood these terms, are job was to create, in partners, a video or formal presentation where to showcase an example of both causation and correlation. I partnered up with Lucas for this project, and we got to brainstorming. We quickly came up with the idea to look at a possible causation between AR-15 sales and the number of school shootings, as well as a correlation between twinkies and diabetes (with the outside force being the number of people who eat unhealthy).

After we got in to some research, we found out that there was little data about gun sales at all, let alone AR-15 sales specifically. This prompted me to take a look at the number of Background Checks performed, because this is the closest one can get to the number of gun sales. Also, we found out that there wasn’t enough data on school shootings by year, so we switched that stat to the number of firearm suicides (age 65+). This way we were able to see pretty similar graph lines, showing the causation.

For our correlation topic, what I figured would happen did, and that’s the fact that we couldn’t find data for twinkie sales. So, after looking up other potential candidates for the spot, we settled on cookie sales. The data we found for this one was based in Canada, so we made sure to find data for number of people with diabetes in Canada too, that way our correlation as a whole just makes more sense.

After all the struggles of finding good data, we needed to actually put it in to a graph. I took the lead of making the graphs and used pages and SuperImpose to make them. I used pages because it has a very user-friendly way of making graphs. I simply entered the data points in to two separate graphs to begin. Then things got a little tricker because I wanted to overlay the two but because the y axis numbers were so much different, I needed to put another y axis on the opposite side. To do this I screenshot both graphs and put them in SuperImpose where I overplayed them to create the graphs we were looking for.

Now that the graphs were done, it was time to start making our presentation. We knew we wanted to do a Keynote because we figured a formal presentation would better suit the project. I wanted to make the Keynote pretty simple and clean. We made sure to include the important points on the slides while our aural presenting would describe the project in detail. All in all the project and presentation went really well and I’m super happy with the way everything turned out. It was a great way to end the year.