### Extra_Credit :)

My favorite statistics benchmark
was when we did a benchmark investigating correlation. For the benchmark we had to
investigate a problem. I decided to do my project on the graduation rate in
Philadelphia and the possible things that could affect the graduation rate. Some of the possibilities that i chose was the attendance of the students, the amount of low-income students, the amount of students
that are non-English speaking, the number of suspensions, the
neighborhood that school is located in, and the demographic make up of the school. I decided to do this project for our
quarter two benchmark because Philadelphia itself seems to be experiencing
problems within their school system. The average graduation rate for the School District of Philadelphia is
63%. I investigated all 60 public high schools in Philadelphia.

I learned while doing this benchmark is that the sla core values are heavily correlated to my benchmark. I
started with **inquiry** by coming up with questions such as what affects the graduation
rate?. My next step I did required **research**. So with the variables I came up with
I researched for each of the 60 high schools in the Philadelphia area.
**Presentation** was that I analyzed all my data in a word document and used graphs to
show how these different variables had a negative or positive affect on the
schools graduation rate. **Collaboration** was used in my benchmark for a few of the
graduation rates I had worked with a peer to come up with. We worked together
because we were both investigating Philadelphia’s high school graduation rates
but we had different variables. So collaboratively we helped each other find
the graduation rates. I presented my benchmark by doing a word document with
graphs and my calculations of each variable. My calculations included the mean,
median, mode, min, max, correlation, spread, and standard devation. These are all things that we have
learned over the year when working with univariate and bivariate data. The last core value is **reflection** and at
the end of the project I wrote a conclusion about what work still has to be
done in the School District of Philadelphia and I even gave possible solutions
to the problems.

The correlation between the graduation rate and the amount of suspensions is a strong linear negative correlation. The fewer amounts of suspensions you have the higher the graduation rate will be. For the simple fact when students are suspended they miss school, and when you miss school you miss work. I think this is a reliable source to use when you trying to find out how much a school suspensions may affect their overall graduation rate.