MS, University of Cincinnati, 2021, Medicine: Biostatistics (Environmental Health)

Bone damping is a non-invasive measure of bone fragility and is identified as a better predictor of osteoporosis (OP) related bone fracture/fragility than bone mineral density (BMD). Subject with higher damping value demonstrates a heightened resistance to fracture. The purpose of this study was to identify the predictors of bone shock absorption (BSA) capacity measured as a damping factor by using model selection and multivariate multiple regression (MMR) method as well as regression tree. The main dataset was from an existing Cincinnati Lead Study (CLS) cohort. It is a prospective and longitudinal study examined early and late effects of childhood lead exposure on growth and development. The results of this study indicated that cortical vBMD, cortical thickness, endosteal circumference, cortical section modulus, current weight, and the number of pregnancies carried until the 3rd trimester were significant predictors of bone damping factor based on the method of model selection and MMR. Among the predictors with top nine highest variable importance values in regression tree method, four are the same as significant predictors from MMR analysis. Those are current weight, cortical section modulus, cortical vBMD, and endosteal circumference. Cortical section modulus and cortical vBMD have positive relationship with damping factor; however weight and endosteal circumference have negative relationship with damping factor. All variables' relationships with damping factor are clinically significant. Lack of dataset from normal people to compare the differences and the missingness of the data are the limitation of the study. Current weight, cortical section modulus, cortical vBMD, and endosteal circumference are significant predictor of damping factor based on the study results. They are biologically relevant to damping and statistically significant in the damping model.

Committee: Amit Bhattacharya (Committee Member); Marepalli Rao Ph.D. (Committee Chair) Subjects: Biostatistics

Master of Science (MS), Bowling Green State University, 2021, Applied Statistics (Math)

K-pop, or Korean pop music, is a genre originating from South Korea that features various musical styles such as hip hop, R&B, and electronic dance. Modern K-pop started with Seo Taiji and Boys in 1992 and has since evolved through stylistic eras called 'generations' to become a worldwide sensation. K-pop's global popularity can be recognized by the success of groups such as BTS and BlackPink. How do the musical qualities of K-pop songs contribute to the genre's popularity? Furthermore, how have the musical qualities contributed to the evolution of becoming the global phenomenon it is today? To explore these questions and more, multivariate analysis will be performed on a curated dataset of 12,012 K-pop songs and their audio features. The audio features, collected with Spotify's Web API, include variables such as Danceability, Loudness, Acousticness, and Valence. The audio features contribution and trends in the evolution of K-pop will be analyzed with nonparametric statistical approaches, Multiple Linear Regression (MLR) and Logistic Regression models. MLR and Logistic Regression will also be used to examine the relationship between the audio features and popularity. Finally, dimension reduction of the audio features performed by Principal Components Analysis paired with K-means clustering will be utilized to explore the possibility of optimizing song clusters within K-pop.

Committee: John Chen Dr. (Advisor); Junfeng Shang Dr. (Committee Member) Subjects: Music; Statistics