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- Creators: School of International Letters and Cultures
- Creators: Bean, Heather
Description
The NFL is one of largest and most influential industries in the world. In America there are few companies that have a stronger hold on the American culture and create such a phenomena from year to year. In this project aimed to develop a strategy that helps an NFL team be as successful as possible by defining which positions are most important to a team's success. Data from fifteen years of NFL games was collected and information on every player in the league was analyzed. First there needed to be a benchmark which describes a team as being average and then every player in the NFL must be compared to that average. Based on properties of linear regression using ordinary least squares this project aims to define such a model that shows each position's importance. Finally, once such a model had been established then the focus turned to the NFL draft in which the goal was to find a strategy of where each position needs to be drafted so that it is most likely to give the best payoff based on the results of the regression in part one.
ContributorsBalzer, Kevin Ryan (Author) / Goegan, Brian (Thesis director) / Dassanayake, Maduranga (Committee member) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
A global trend towards cashlessness following the increase in technological advances in financial transactions lends way to a discussion of its various impacts on society. As part of this discussion, it is important to consider how this trend influences crime rates. The purpose of this project is to specifically investigate the relationship between a cashless society and the robbery rate. Using data collected from the World Bank’s Global Financial Inclusions Index and the United Nations Office of Drugs and Crime, we implemented a multilinear regression to observe this relationship across countries (n = 29). We aimed to do this by regressing the robbery rate on cashlessness and controlling for other related variables, such as gross domestic product and corruption. We found that as a country becomes more cashless, the robbery rate decreases (β = -677.8379, p = 0.071), thus providing an incentive for countries to join this global trend. We also conducted tests for heteroscedasticity and multicollinearity. Overall, our results indicate that a reduction in the amount of cash circulating within a country negatively impacts robbery rates.
ContributorsChoksi, Aashini S (Co-author) / Elliott, Keeley (Co-author) / Goegan, Brian (Thesis director) / McDaniel, Cara (Committee member) / School of International Letters and Cultures (Contributor) / Department of Economics (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This paper seeks to highlight the strong correlation and potential causation between the presence of physical community bank branches in rural communities and local economic outcomes like payroll, employment, and establishments in a given region. To do this, I conduct a two-part analysis involving a fixed effects model with data from across the US and a regression discontinuity model of a subset of the data in parts of Delaware and Maryland. Overall, my results show a significant strong correlation between the number of bank branches in a region and the expected percent changes in economic outcomes, but I lack the results to claim causality between the opening or closure of a bank branch and changes in the local economy. This has relevance in understanding the need for physical bank branches as changes in the financial industry since the 2008 Financial Crisis, like online banking, have continued to accelerate.
ContributorsRodriguez, Luke (Author) / McDaniel, Cara (Thesis director) / Kuminoff, Nicolai (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution & Social Change (Contributor) / School of International Letters and Cultures (Contributor) / Economics Program in CLAS (Contributor)
Created2022-12
Description
Machine learning(ML) has been on the rise in many fields including agriculture. It is used for many things including crop yield prediction which is meant to help farmers decide when and what to grow based on the model. Many models have been built for various crops and areas of the world utilizing various sources of data. However, there is yet to exist a model designed to predict any crop’s yield in Yuma Arizona, one of the premier places to grow crops in America. For this, I built a dataset from farm documentation that describes the actions taken before, during, and after a crop is being grown. To supplement this data, ecological data was also used so data such as temperature, heat units, soil type, and soil water holding capacity were included. I used this dataset to train various regression models where I discovered that the farm data was useful, but only when used in conjunction with the ecological data.
ContributorsJohnson, Nicholas (Author) / Kerner, Hannah (Thesis director) / Bandaru, Varaprasad (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
One out of ten women has a difficult time getting or staying pregnant in the United States. Recent studies have identified aging as one of the key factors attributed to a decline in female reproductive health. Existing fertility diagnostic methods do not allow for the non-invasive monitoring of hormone levels across time. In recent years, olfactory sensing has emerged as a promising diagnostic tool for its potential for real-time, non-invasive monitoring. This technology has been proven promising in the areas of oncology, diabetes, and neurological disorders. Little work, however, has addressed the use of olfactory sensing with respect to female fertility. In this work, we perform a study on ten healthy female subjects to determine the volatile signature in biological samples across 28 days, correlating to fertility hormones. Volatile organic compounds (VOCs) present in the air above the biological sample, or headspace, were collected by solid phase microextraction (SPME), using a 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coated fiber. Samples were analyzed, using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS). A regression model was used to identify key analytes, corresponding to the fertility hormones estrogen and progesterone. Results indicate shifts in volatile signatures in biological samples across the 28 days, relevant to hormonal changes. Further work includes evaluating metabolic changes in volatile hormone expression as an early indicator of declining fertility, so women may one day be able to monitor their reproductive health in real-time as they age.
ContributorsOng, Stephanie (Author) / Smith, Barbara (Thesis advisor) / Bean, Heather (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2018