In-class Projects (Only the ones selected as future examples by the lecturers are listed here)
1. Title: The Social Effects on US Foreclosure Rate
Author: Xiaoyi Yang
Introduction:The house is one of the most important properties in people's life. The status of a house is highly related to the owner's economic, social and even cultural capitals. Because of the higher prices of houses compared to the general commodities, nowadays, people prefer to apply for loans in order to get their dream houses. It is an attractive choice for most of the people since they can enjoy the house before they actually completely own it. However, the loans are connected with risks. Once the borrower fails to pay on time, the house will be considered as a delinquent mortgage, and if the owner fails to pay in a certain time period, the lender can start a legal process called foreclosure and force the borrower to sell the house.
It is hard breaking for a family to lose their house. However, the foreclosure rate has a far more profound effect on society. In fact, a neighborhood with higher foreclosure rate may suggest the instability of the neighborhood, and it may lead to higher divorce rate and even higher criminal rate. Therefore, we are interested in the factors that may lead to or caused by higher foreclosure rate.
Key Words: Foreclosure rate, Social factors, Unemployment rate, Race, Principle Component Analysis
2. Title:NFL Team Performance Regression Analysis
Author: Xiaoyi Yang, Patrick Seng, and Derek Norton
Introduction: For the past 30 years, American football has been deemed the most popular sport in the United States. With the popularity exploding particularly in recent years, the multimillion dollar industry has become very important to more than just the fans and athletes themselves. The success of organizations has become truly larger than just a game affecting revenue streams for entire cities. With all that’s riding on the output of these teams the question that begs to be addressed is; what makes a football team successful?
To answer this question, the following report takes into account a multitude of on-field, play-to-play statistics that could be associated with a professional football team’s success. The use of play-to-play statistics are felt to be good predictors of performance versus “drive” statistics as they occur more frequently during a game / season, and are more focused performance aspects an NFL team could aim to improve upon, instead of a more general “score more touchdowns” approach.
To measure the regular season success, win percentage was first considered. However, in a regular season, each NFL team plays 16 games, such that win percentage can only be divided into 16 categories. Instead, point differential was chosen as the response variable rather than season win % as this is much closer to a continuous variable, and more likely to satisfy linear regression assumptions. Point differential was a good candidate to represent the success of teams, as it was found to have an extremely high correlation of 0.93 with win percentage.
Key Words: NFL, Linear regression, Point differential, Model selection
Note: The full content of the projects are available on request