Applied Statistics Commons^™

The Effect Of Internal Consistency On Ncaa Women’S Gymnastics Scores, Jordan Williams

Mathematics Senior Capstone Papers

The aim of my research is to examine internal consistency in relation to the validity of scores in NCAA Women’s Gymnastics. This study focuses on scores from the now infamous 2024 Tennessee Collegiate Classic. Patterns from several different deviations and correlation coefficients are analyzed, and a “gold standard” score to test against is created. This allows me to identify several patterns in results that could signify invalid scores.

Faculty Diversity And Minority Enrollment In Advanced Stem Courses: A Case Study At Neville High School, Anaya Cormier

Mathematics Senior Capstone Papers

This study investigates the correlation between the diversity of STEM faculty and the enrollment rates of minority students in honors, Advanced Placement (AP), and dual enrollment STEM courses at Neville High School. Recognizing the long-standing underrepresented minority students in advanced STEM education, this research explores whether a more diverse faculty positively influences student participation in these courses. Using a quantitative correlational design, data on faculty demographics and student enrollment patterns were analyzed through descriptive statistics and chi-square tests of independence. The results are expected to provide information on the role of faculty diversity in fostering equitable academic opportunities. The findings …

An Analysis Of The Alaskan Salmon Harvest, Carson Allen

Mathematics Senior Capstone Papers

The objective of this paper is to analyze the annual Alaskan salmon harvest and the variables that effect the harvest differently each year. Every year, thousands of workers’ livelihoods depend on the annual salmon harvest to provide for themselves and their families. This paper takes this reality and aims to use data gathered from previous fishing seasons to understand what variables affect the salmon population. To analyze the salmon harvest, multiple linear regression will be used over a 41 year period with variables including water temperature, air temperature, yearly harvest, and species of salmon. By modeling these and other variables, …

Estimating Climate Risk In Financial Markets, Olanrewaju Oluwadamilare Olaniyan

Masters Theses

The growing impact of climate change on financial markets necessitates a rigorous approach to climate risk assessment. This thesis examines methods for quantifying climate-related financial risks, with a focus on distinguishing climate risk from broader market movements (represented by S&P 500). Using a factor model, we isolate climate risk factors to better understand sector-specific volatility. The insurance sector is used as a proxy for climate risk exposure, given its sensitivity to climate-related losses and regulatory changes. We apply Extreme Value Theory (EVT); the Block Maxima Method and the Peaks Over Threshold Method, to identify excess risk patterns in financial portfolios. …

Repositioning The Game: Traditional Positions Vs Tracking-Based Archetypes In Nba Performance Models, Jacob Floyd

Senior Theses

Driven by the rise of advanced analytics and player tracking technologies, the NBA has transitioned away from traditional positional roles and toward more fluid player archetypes. This investigation uses principal component analysis and k-means clustering to group players based on season-long tracking data, creating new pseudo-positions that more accurately reflect modern playing styles. Predictive models were then built using both the classic position system and the newly generated clusters to forecast player scoring performance. Across every model comparison, both in terms of fit and predictive accuracy, the cluster-based system significantly outperformed the traditional position-based model. These results reinforce the idea …

An In-Depth Analysis Of The Bellarmine University Student Athlete Experience, Mattingly E. Spalding

Undergraduate Theses

The goal of this thesis is to improve the student athlete experience at Bellarmine University through direct feedback from our current student athletes. By defining what parts of the student athlete experience they value most, Bellarmine can reflect on the support currently provided in these areas. Additionally, if it is concluded through the response that high valued areas are not being satisfied, Bellarmine can work to improve the support in these areas. Or, if there is abundant support in a low-valued area, Bellarmine could think to shift resources into a more needed concentration. Through the design sample, data was accumulated …

Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal

Doctoral Dissertations and Master's Theses

Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …

Value-Based Healthcare Reimagined: A Mixed-Methods Study On Behavioral Health Clinicians' Perspectives, Amanda L. Strickland

Electronic Theses and Dissertations

This study explores how behavioral health clinicians perceive Value-Based Healthcare (VBHC), a model designed by Porter and Teisberg (2006) to improve outcomes relative to costs. While widely promoted in healthcare reform, VBHC poses unique challenges when applied to behavioral health settings. Using an explanatory mixed-methods design, this study first assessed clinicians’ awareness of VBHC through a survey of 23 licensed clinicians at a Community Mental Health Center (CMHC) in Colorado. Quantitative findings revealed that one-third of participants were aware of VBHC with awareness differing by role prompting further exploration in a qualitative phase. Semi-structured interviews with eight clinicians provided deeper …

On Large Language Models In National Security Applications, William N. Caballero, Phillip R. Jenkins

Faculty Publications

The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from …

Linking Water Quality And Climate Change To Long-Term Trends In Species Abundance In Norwalk Harbor, Viktoria Savatorova, Aidan Kieft, Nicole C. Spiller, Kasey Burns

Spora: A Journal of Biomathematics

This study examines the effects of environmental changes on fish populations in Norwalk Harbor, focusing on winter flounder (Pseudopleuronectes americanus), cunner (Tautogolabrus adspersus), northern pipefish (Syngnathus fuscus), and naked goby (Gobiosoma bosci) as examples of species responding to climate-related shifts. We analyze how water temperature, salinity, and dissolved oxygen correlate with fish abundance. To assess statistically significant differences in catch per unit effort (CPUE) across harbor regions, we applied the Kruskal-Wallis test followed by Dunn's post-hoc test. Seasonal variations in CPUE were examined by comparing monthly catch data for each species. K-means …

Bayesian Nonparametric Hypothesis Testing Methods On Multiple Comparisons, Qiuchen Hai, Zhuanzhuan Ma

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we introduce Bayesian testing procedures based on the Bayes factor to compare the means across multiple populations in classical nonparametric contexts. The proposed Bayesian methods are designed to maximize the probability of rejecting the null hypothesis when the Bayes factor exceeds a specified evidence threshold. It is shown that these procedures have straightforward closed-form expressions based on classical nonparametric test statistics and their corresponding critical values, allowing for easy computation. We also demonstrate that they effectively control Type I error and enable researchers to make consistent decisions aligned with both frequentist and Bayesian approaches, provided that the …

Discounting Effect Size When Borrowing External Data In Clinical Studies, Zhuanzhuan Ma, Chul Ahn, Bin Wang, Xuefeng Li

Research Symposium

Background: When borrowing information from external data to augment a current trial, many available methods discount the sample size but retain the effect size from previous studies. Discounting the sample size is just one way to discount the prior information. It may not be appropriate if the underlying assumption of unbiased treatment effect does not hold, for example, when the treatment effect in the historical study is likely higher than the one expected in the current trial.

Methods: To tackle this potential issue, we study some methods to shrink the effect size from previous studies assuming that the prior effect …

Sparse Bayesian Variable Selection Using Global-Local Shrinkage Priors For The Analysis Of Cancer Datasets, Zhuanzhuan Ma

Research Symposium

Background: With a rapid development of data collection technology, high dimensional data, whose model dimension k may be growing or much larger than the sample size n, is becoming increasingly prevalent in different fields of study, such as ecology, genetics, among others. This data deluge is introducing new challenges to traditional statistical procedures and theories and is thus generating a renewed interest in the problems of variable selection and classification in high dimensional regression models. In large k, small n settings, variable selection is usually the first step for dimension reduction to uncover significant covariates, which contribute to …

Urban Heat Dynamics In Pune: The Influence Of Land Cover And Local Climate, Arpit Tiwari, Preethi Nanjundan, Ravi Ranjan Kumar, Ananya Karmakar, Satyaban Bishoyi Ratna

Northeast Journal of Complex Systems (NEJCS)

Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (T_max; 0.13℃ to 1.63℃ …

Analysis Of Systematic Trade-Offs Between Military And Healthcare Expenditure Alongside Gdp Growth Of Select Asian And Western Exporting Economies In The 21st Century, Rahul Balamurugan, Carlos Gershenson, Preethi Nanjundan, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality …

A New Measure Of Non-Parametric Correlation For Variables In The Likert Scale, Shubhabrata Das

Working Papers

We propose a new measure of nonparametric correlation that is especially suited for measuring association between variables measured in the Likert scale where data is ordinal and tied observations are extremely common. The proposed general structure of the measure is based on graded level of concordance and discordance between the pairs of metrics. The general form of the measure has all the desirable properties except the measure is not necessarily zero for independent variables. This limitation is acceptable given only ordinal nature of the metrics. Three versions of the measure are studied. The first is based on simple equi-distant weights. …

Leveraging Bayesian And Classical Techniques For Survival Analysis Using The Weibull-Rayleigh Distribution, Mahmoud Mansour, Rashad El-Sagheer, Nagwa Mohamed

Basic Science Engineering

This paper contributes to an extensive analysis of the Weibull-Rayleigh distribution (WRD), including Bayesian inference for randomly censored data. The WRD is a versatile model that fits various types of survival data, especially in situations including censoring, commonly found in biostatistics and engineering reliability research. The research investigates the derivation of the WRD’s probability density and cumulative distribution functions, employing maximum likelihood estimation (MLE) and Bayesian estimating techniques to accurately infer parameters. Gamma priors are utilized in Bayesian analysis, and approximate Bayesian estimates are derived by Gibbs sampling and Lindley’s approximation methods. An actual dataset that represents leukemia-free survival times …

Optimized Hiv/Aids Resource Allocation In Ohio: A Linear Programming Approach, Godfred Ahenkroa Kesse

Data Science and Data Mining

This study employs a linear and integer programming approach to optimize HIV resource allocation in Ohio, aiming to minimize new infections and enhance the impact of limited resources. With the advances in HIV prevention and treatment, Ohio faces challenges in addressing disparities in access to healthcare, particularly among high-risk populations. The proposed model integrates data on infection rates, transmission patterns, demographic factors, and cost-effectiveness to provide a decision-support framework for policymakers. Using epidemiological data and equity constraints, the model prioritizes high-risk regions and populations while ensuring fair resource distribution. Results indicate that increased funding allocations significantly enhance the potential to …

Robust Conic Satisficing, Arjun Ramachandra, Napat Rujeerapaiboon, Melvyn Sim

Working Papers

In practical optimization problems, we typically model uncertainty as a random variable though its true probability distribution is unobservable to the decision maker. Historical data provides some information of this distribution that we can use to approximately quantify the risk of an evaluation function that depends on both our decision and the uncertainty. This empirical optimization approach is vulnerable to the issues of overfitting, which could be overcome by several data-driven robust optimization techniques. To tackle overfitting, Long et al. (2022) propose a robust satisficing model, which is specified by a performance target and a penalty function that measures the …

Kroger Post-Pandemic Customer Segmentation, Mario Mata, Joey Truitt, Renn Spigelmyer, Dhanuja Kasturiratna, Lisa Holden, Nitish Baidya, Hanna Tafari

Posters-at-the-Capitol

The grocery retail industry landscape has changed greatly in the wake of the pandemic. Specifically, delivery and pickup services have become more popular and customer buying habits have evolved. At the same time, improvements in data collection and analysis have allowed grocery marketing strategies to become highly individualized.

We worked with 84.51, an analytics firm, to identify customer segments for the Kroger Company based on data from 2023. Using clustering techniques, we organized customers into groups, or segments, based on similar characteristics. We identified and profiled four distinct groups of customers. Three segments were characterized by high frequency and spending …