# Moody’s Paper 1

This week, I started some preparation work for revising our team paper from Moody’s Math Challenge last spring.

According to the description on Moody’s official website, Moody’s Mega Math Challenge (M3 Challenge) is an applied mathematics competition for high school students. By asking students to solve complex, real-world issues with the power of mathematics, Moody’s Mega Math (M3) Challenge reminds high schoolers that math is more than just formulas and solving for “x.” The Internet-based contest, for which registration and participation are free, gives eleventh and twelfth grade students 14 hours to solve an open-ended, applied math-modeling problem focused on a relevant issue. Teams are judged based on their solution papers, and top teams are required to present their findings to a panel of judges.

In March, Westtown sent our first team, consisting of me and three other juniors, to compete in M3 challenge. We have spent 14 hours in a day within a school classroom, solved the challenge problem, and eventually submitted a 16 page paper. Now in Math Independent Research, I feel glad to revise and improve our paper.

At the beginning of this week, I re-read the actual problem, including three questions. I especially focused on the first question:

How big is the problem? Create a model for the amount of plastic that ends up in landfills in the United States. Predict the production rate of plastic waste over time and predict the amount of plastic waste present in landfills 10 years from today.

I then started to look up related data from the two websites that are provided at the end of the question:

http://www.epa.gov/epawaste/nonhaz/municipal/index.htm

Then I put down some of the data I have found in an Excel chart:

 Years Amount of Plastic Waste in America (Thousand tons) Amount of Plastic Waste recycled (Thousand tons) 2011 31840 2650 2010 31040 2550 2009 29830 2120 2008 30050 2120 2007 30730 2090 2006 29490 2040 2005 28910 1650 2004 2003 26650 1390 2002 2001 25380 1390 2000 24710 1340 1999 24710 1350 1998 21460 1110 1997 19760 1060 1996 18990 1000

After I put them into a Scatter chart, both of these data show as a linear line. Next week, I will pick up from here and try to insert what I get from the data into a Markov Chain to establish a model for question 1.

Shuangcheng Du

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