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- All Subjects: Regression
- 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
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