Bachelor of Arts, Wittenberg University, 2017, Education

Students in the typical American school spend nine months in school, then three months away from school. Research has shown that students from low-income backgrounds, such as urban areas, lose ground in academic achievement over the summer, while students from "better-off" backgrounds continue to gain in academic achievement (Leefatt, 2015). Summer programming for these students have been shown to limit these negative effects, and even positively affect student achievement (2011). This study focused on how these summer programs communicate with schools in regards to student performance, especially focusing on students at risk for retention. A mixed methods study provided the following results: 1) there is little to no formal communication between summer programs and schools in the school studies, 2) teachers in this study received little valuable information on their students from the regular school year or summer school, 3) teachers in this study preferred to get to know their students personally, but also desired information regarding the families of their students and motivation tactics that work for them.

Committee: Brian Yontz Dr. (Advisor); Sally Brannan Dr. (Committee Member); Robert Welker Dr. (Committee Member) Subjects: Education

Master of Arts, Miami University, 2017, Economics

Significant attention in the financial economics literature is given to the usage of aggregated factors in their ability to explain variability in asset returns. Whereas the Capital Asset Pricing Model (CAPM) considers the excess return on the market portfolio as the dominant source of systematic variability in asset returns, the framework of Arbitrage Pricing Theory (APT) suggests that systematic risk can be further decomposed into numerous common risk factors that underlie co-movement in asset returns. Chen, Roll, and Ross (1986) popularized empirical evaluation of macroeconomic indicators in their relation to asset returns, finding that macro-economic indicators can be useful to price assets and carry statistically significant risk premiums in sample. Following the intuition of the Roll (1977) critique, I consider the pricing of risk derived from unexpected shocks, or innovations, to a wider set of macroeconomic and capital market variables. I find that information contained in shocks to common risk factors is significantly priced in the cross-section of asset returns and differs from information contained in the Fama-French-Carhart factors.

Committee: Thomas Boulton Ph.D. (Advisor); George Davis Ph.D. (Committee Member); Tyler Henry Ph.D. (Committee Member) Subjects: Economics; Finance

Master of Science (MS), Ohio University, 2015, Industrial and Systems Engineering (Engineering and Technology)

This study is an initial attempt to investigate the relationship between economic factors and monthly tire sales, using artificial neural networks (ANNs) and comparing the results to stepwise regression. Data for this research were collected through a privately held tire warehouse located in Wheeling, West Virginia. Research has shown that artificial neural network models have been successfully applied to many real world forecasting applications. However, up to this date no research has been found using artificial neural networks and economic factors to predict tire demand. The first part of this research describes why the chosen economic factors were selected for this study and explains the initial methodology with results. The next stage of the research gives details on why the methodology was revised and also clarifies why Google Trends and additional mathematical inputs were applied to the study. The final research focused on separating the master database into three different categories based on selling percentages. The results of the study show that the artificial neural network models were capable of forecasting the number of high selling tires, with a validation technique, but were unable to be applied sufficiently for the medium and low selling products.

Committee: Gary Weckman Ph.D. (Advisor) Subjects: Engineering; Industrial Engineering