# Moderately Similar and Stuff

THIS IS MY LAST GRADE NINE MATH POST. I REPEAT, MY LAST GRADE NINE MATH POST. Wow, it’s been a long year. It’s the end of an era. Math 9, what a class. To go out on a bang, our last project was pretty much entirely up to us. Yup.

Correlation. Causation. Similar, but not the same.

We started off this project by looking at a bunch of correlating data. Correlation is when to unrelated points of data follow the same trends. For example, this graph that there is a photo of right here. As you can probably figure, Nicholas Cage movies and the number of people who died by falling into the pool have nothing to do with each other. But the data follows relatively the same pattern. Correlation! Some correlating data has an outside reason that effects them both, but the don’t have to.

Causation, as I fore mentioned, is similar. But this time, instead of them being unrelated, the directly impact each other. You see, in causation, one data point causes the other. For example, this graph of temperature and ice cream sales. They do follow the same trend, but what makes them causation is that the rise in temperature directly causes the ice cream sales to rise.

Starting off also, we watched this TED talk that used correlation and causation in their explanation. They used an application called Gapminder, that some of us used in our projects.

For this project, we had to create to questions, one correlation, one causation. I worked with the wonderful Tamara Soltys-Lee. Our questions were as follows.

“Does the cost of university have a correlation with homelessness?”

And

“Does the time it takes a person to walk to school have a causation with the amount of music they listen to per week?”

(I just found this feature and I think it’s really cool)

To do this, we had to delegate. I was to research the homeless population from 1992-2014 (because that’s the time frame I could find), and Tamara was to research university tuition. I ended up searching for quite a while, until I found the stats for Calgary’s homeless population. So, I used that data. Tamara, on the other hand, found her information on Pen State University. So they’re really not related. Anyways, we took the information that we found and put it into a chart on numbers. Now, numbers has many great features, including a feature that makes it so you can automatically input your data into a graph.

(My YouTube is being annoying right now but here there will be a video showing this.)

Our data resulted that these two things follow the same pattern, so, they are correlated!

Now, for the latter of the two!

To get the information for this question, we created a google survey. We then proceeded to send the link to people, via airdrop and social media. We got 20 people to complete our survey!

With this data, and a bit of other research, I was able to put each of these things into a graph. Now, this is where it took a while. I took me three tries to graph the data into a form that would show correlation. Finally, we asked the teacher, and he suggested this.

There would be a best fit line, but I couldn’t find one.

After we collected all this data, we presented it in a keynote!

If I were to do this project again, I would do it exactly the same. Everything went very well.