Applied Statistics Commons^™

Bridging The Gap Between Career Expectations Versus Labor Market Realities, Reinette P. Madrid, Grethel T. Ledesma, Ignatius Aryono Putranto, Jyro B. Triviño

Leadership and Strategy Faculty Publications

Most students lack awareness regarding the labor market outcomes for their chosen college majors. This study aims to answer what factors affect the career expectations of graduating students at Quezon City University and how these expectations align with the prevailing labor market situation. It employed descriptive, causal, and explanatory research using a sample of 108 respondents from fourth-year information technology students for the school year 2021 to 2022. Eight of the nine null hypotheses were rejected by employing multinomial logistic and linear regression. Student fixed effects and other labor market outcomes significantly predicted salary, estimated stability, and estimated skills in …

Predicting Passenger Demand On National Flights Departing From Hartsfield-Jackson Atlanta International Airport (Atl) In 2024, Brooklyn Gossett, Ana Yu Wen

Discovery Day - Daytona Beach

The aviation industry relies heavily on accurate demand forecasting to guide critical decisions regarding route planning, capacity management, and pricing strategy. Misjudging passenger demand can result in significant revenue loss and operational inefficiency, making it essential for airlines and analysts to identify the key drivers of flight patronage. This study investigates the factors that most significantly predict the number of passengers on domestic flights departing from Hartsfield-Jackson Atlanta International Airport (ATL) during the 2024 calendar year. Using passenger and route data sourced from the Bureau of Transportation Statistics (BTS) and the U.S. Department of Transportation (DOT), a multiple regression analysis …

What We Know About Accounting Ratios: Methodological Considerations, Wojciech Kuryłek, Oskar Kowalewski

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: This paper provides a comprehensive literature review of the methodological aspects of financial ratio analysis, consolidating dispersed knowledge on the computation, statistical properties, and appropriate usage of accounting ratios.

Design/Methodology/Approach: The study adopts a narrative literature review methodology, systematically surveying published research on financial ratio distributions, normality testing, data transformations, outlier handling, the proportionality assumption, dimensionality reduction techniques, compositional data analysis, and recommended ratio sets for corporate financial research.

Findings: Financial ratios predominantly deviate from normal distributions, exhibiting skewness, excess kurtosis, and sensitivity to outliers. Transformation techniques such as logarithmic, square root, and Box‑Cox methods yield mixed results in …

The Impatience Of Winning: An Analysis Of Time Discounting, Predictive Modeling, And The Nba Draft, Alec R. Plante

Business and Economics Honors Papers

This paper examines whether NBA draft decisions can be better explained by incorporating non-geometric time discounting into a model of general manager decision making. Using a dataset of 285 NBA draft prospects over a 12-year period, the impact of college statistics on Value Over Replacement Player (VORP) is determined, and these impact values are then used to create a “predicted” VORP for the first 4 seasons of each player’s career: a projection of what a general manager might think of a prospect’s future value given their college statistics. Following this, geometric and hyperbolic time discounting models are applied to estimate …

Financial Literacy And Inclusion Of Philippine Jeepney And Tricycle Drivers, Bryan N. Bernabe, Jyro B. Triviño

Leadership and Strategy Faculty Publications

The study investigated how the different elements of financial literacy influence the financial inclusion of jeepney and tricycle drivers in Caloocan, Metro Manila. Pearson correlation analysis revealed a positive correlation between financial inclusion and attitude, behavior, knowledge, and skills. Additionally, analysis of variance highlighted that education and age play significant roles in enhancing financial literacy. The linear regression findings also supported the idea that income acts as a positive moderator, augmenting the impact of financial literacy on financial inclusion. The study attempted to disaggregate its financial literacy components to understand their impact on financial inclusion, but its interrelationships also require …

Modeling Private Debt Using U.S. Consumer Expenditure Data, Stsiapan Dziamentsyeu

Honors Capstones

This project models private household debt among U.S. consumers using data from the Consumer Expenditure Survey (CES) between 2013 and 2023. The analysis focuses on identifying how demographic and economic characteristics, such as income, housing expenditures, education, and occupation, relate to non-mortgage “other” loan balances. After initial model development produced poor residual behavior due to zero-inflation from imputed debt values, the analysis was refined to include only households reporting verifiable debt. Multiple modeling techniques, including AIC-based variable selection and Lasso regularization, were compared under a five-fold cross-validation framework. The Lasso model achieved superior predictive accuracy (RMSE = 1.55, MAE = …

An Assessment And Comparison Of Expert System Performance And Large Language Model Performance, Carter A. Lange

ATU Honors Projects

This study compares the performance of knowledge-based expert systems (KBES) and large language models (LLMs) in narrow-domain tasks. Using Akinator as the representative KBES and ChatGPT as the representative LLM, fifty character-identification trials were conducted. Results show that both systems ultimately succeeded in identifying all characters, but their efficiency and accuracy differ. Akinator required fewer incorrect guesses and produced no identifiable total failures, or “errors,” while ChatGPT occasionally erred beyond possible continuation despite similar average guess counts. Statistical analysis revealed no significant difference in the number of questions required before success, but McNemar’s test indicated that ChatGPT made significantly more …

The Gender Pay Gap: An Occupational Analysis (2012-2023), Curtis Long

Harriet L Wilkes Honors College Theses

This thesis examines the gender wage gap across thirty major U.S. occupations from 2014 to 2023 using data from the Current Population Survey (CPS), more specifically, the U.S. Bureau of Labor Statistics (BLS). The analysis measures the difference in median weekly earnings between women and men in each occupation and applies two statistical tests: a yearly z-test to evaluate whether the average wage gap is significant, and a ten-year t-test to identify persistent occupational gaps. Results show significant wage differences in every year, with the largest disparities in finance, management, and law, and smaller gaps in education and healthcare. Consistent …

Trends And Predictive Modeling Of Real Estate Prices In Major Saudi Arabia Cities, Meshal S. Aldahas

Theses and Dissertations

his research examines historical trends and explanatory modeling of real estate prices in major Saudi cities, with a focus on Riyadh, Jeddah, and Dammam. Using a mixed-methods approach, the study integrates quantitative data from 2010–2023, including housing and macroeconomic indicators, with qualitative insights drawn from over 320 survey responses that captured consumer sentiment on affordability, job security, and housing policies. A combination of descriptive statistics, ARIMA and Exponential Smoothing techniques was applied to detect long-term patterns, seasonal variations, and market shocks. Predictive modeling was conducted using Linear Regression, Decision Trees, and Neural Networks, with results showing that job security consistently …

Nba Player Types And Salaries: Assessing The Disparities In Pay, Nick Riccardi, Rodney J. Paul

Sport Management - All Scholarship

The purpose of this study was to identify player types that exist in the modern National Basketball Association (NBA), test whether player types are paid differently controlling for performance and other factors and construct successful rosters with cheaper payrolls.
We collected performance statistics and salary data for players and teams across five seasons (2018-19 to 2022-23). Cluster analysis is leveraged to group together player-seasons to identify the player types that exist in the NBA. Linear regression models are run to test for differences in pay by cluster membership while controlling for performance, age, and contractual details. Linear programming simulation models …

Unified Hybrid Censoring Samples From Power Pratibha Distribution And Its Applications, Mahmoud Mansour, Hebatalla H. Mohammad Dr, Khalaf S. Sultan Prof.

Basic Science Engineering

This paper suggests an extensive inferential method for the Power Pratibha Distribution (PPD) under Unified Hybrid Censoring Schemes (UHCSs), since there is a growing interest in flexible models in both reliability and service operations. This work studies the PPD model using standard Maximum Likelihood Estimation methods and modern Bayesian approaches too. Using a complex architecture, UHCS simulates tests more closely to what is done in practice than by using more basic censoring schemes. Using analysis, the probability and statistical ranges are carefully calculated for the parameters. Tests demonstrate that Bayesian estimation gives better results than many other methods for estimation, …

Decoding The Algorithm: The Mathematics Behind Tiktok’S Short-Form Content Success, Ashley N. Lynch

Honors Scholar Theses

Within the realm of social networks, TikTok has become the central hub for short-form video content. The network’s unique ability to capture individual preferences using predictive analytics has greatly contributed to its massive success, allowing the company to optimize its performance and content personalization. In an age where digital media have such a significant influence on society, it is essential that users develop an understanding of how social network algorithms function to make more informed online decisions. Although TikTok’s technological system is primarily undisclosed, the platform certifiably leverages several key mathematical principles within its algorithm to achieve its core goals …

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

Enhancing Animal Shelter Operations With Time Series And Machine Learning, Sakava L. Kiv, Donald L. Anderson, Shivam Negi, Jacquelyn Cheun

SMU Data Science Review

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and …

A Machine-Learning Tool-Supported Methodology For Nonprofit Donor Analysis, Corbin Weiss

Campus Research Month

We developed a machine-learning tool-supported methodology for modeling the nonprofit donor relationship. This approach was demonstrated in the case of a US-based nonprofit. Conclusions were drawn from this example and tool-support provided for use by other nonprofits.

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. …

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. …

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 …

Forecasting Equity Betas Using Option-Implied Moments, Ivan Kolesnikov

CMC Senior Theses

Traditional beta estimates are constructed from historical stock‑and‑market returns and therefore adjust only as fast as realized data accrue. This thesis investigates whether the forward‑looking information embedded in equity‑option prices can enhance beta forecasts. Using near‑end‑of‑day quotes for 236 S&P 500 firms between 2007 and 2024, I extract risk‑neutral variance and skewness, construct five alternative beta estimators (historical, option‑implied, and three hybrids), and evaluate them against realized betas over six‑, twelve‑, and twenty‑four‑month windows. Rolling‑OLS beta remains the most accurate benchmark at short horizons, yet option‑implied moments add economically and statistically significant value when systematic exposure is expected to change …

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Investigating Servant Leadership Measurement: A Mixed-Methods Study Integrating Content Analysis And Meta-Analysis, Kai Torsten Schramm

Electronic Theses and Dissertations

This study aimed to understand the similarities and differences among servant leadership measures and the variations in their effect sizes on job performance and job satisfaction. This paper explores how the items in servant leadership measures portrayed the servant leadership construct and how these relate to the outcomes. The researcher used an exploratory sequential mixed methods design. Which involved a qualitative content analysis of the measurement items and a meta-analysis of outcomes, considering the findings from the content analysis. Six key categories determined the three main themes: selfless generosity, inspiring influence, adaptive humility, integrity, empowering, and harmonious engagement. The three …

Comparison Of Value At Risk Using Historical And Monte Carlo Methods On Pt Xyz Stock Portofolio, Eka Fitriani, Yulial Hikmah, Ira Rosianal Hikmah

Jurnal Administrasi Bisnis Terapan

One way to achieve profits in a company is through investment activities. However, everything has risks. Investing can also be risky. Therefore, the relationship between risk and investment is important because it will influence the determination of investment selection. The problem faced by investors is choosing an efficient portfolio, or a portfolio that provides the smallest risk. This risk can be done by measuring risk, one of which is using the Value at Risk (VaR) measure. Measurement using Value at Risk has several methods that are quite popular, namely the Historical Method, Variance-Covariance, and Monte Carlo. In this research, the …

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Automatic Appraisals Of Houses, Sloan Scroggin

Graduate Theses and Dissertations

Multiple hedonic models and an automatic appraiser model were used to create a residential house’s estimated sales price. The goal is to use the limited data available to a REALTOR® to estimate the future sales price of a residential home without the aid of pictures of the property or viewing the physical property. The first model automates some of the actions of an appraiser by finding comparable sales based on proximity, based both on distance between houses and characteristics of the houses, and then calculating a weighted average price for an estimated sales price of future sales. If the model …

Forecasting Stock Prices Using Arima Models And Technical Analysis, Muath I. Almaiman

Theses and Dissertations

This thesis explores the integration of Autoregressive Integrated Moving Average (ARIMA) models and technical analysis to forecast stock prices, with a focus on Coca-Cola's (KO) and Netflix’s (NFLX) stocks. It examines the effectiveness of combining ARIMA models, known for their predictive accuracy in time-series analysis, with technical indicators, particularly moving averages. The study evaluates whether this integrated approach can enhance the predictive capability of stocks prices beyond traditional methods. The predictive capability is evaluated using error metrics from the ARIMA models, as well as by assessing the return earned using simple rules based on the technical indicators. Utilizing data spanning …

An Analysis Of Corporate Social Responsibility And Real Earnings Management, Rachel Brassine

Theses, Dissertations and Capstones

Real earnings management (REM) is costly in the form of intense loan restrictions, increased interest expense, and public scrutiny. Nevertheless, companies still practice REM. Based on agency and stakeholder theories, this research predicts that as a company’s CSR score increases, REM will decrease, and this association will become more negative when a critical mass of females on the board of directors exists and when a board-level CSR committee is present. This study also predicts that when a company offers an executive incentive plan based on CSR metrics, REM will decrease, and the relationship will become more negative with a critical …

Optimizing Sports Outcome Prediction Through Feature Engineering And Machine Learning, Vitor S. Freitas

Graduate Theses/Dissertations

The challenge of predicting the outcome of a team game lies in the high complexity and dynamics of the sports data. This thesis focuses on the aspect of using feature engineering and the genetic algorithm to predict the winner and the score of various sports events. Generally, it deals with how machine learning algorithms are combined with state-of-the-art feature engineering techniques in sports datasets derived from various sports disciplines. In this thesis, five different machine learning models have been applied, classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine …

Self-Exciting Point Processes In Real Estate, Ian Fraser

Theses and Dissertations (Comprehensive)

This thesis introduces a novel approach to analyzing residential property sales through the lens of stochastic processes by employing point processes. Herein, property sales are treated as point patterns, using self-exciting point process models and a variety of statistical tools to uncover underlying patterns in the data. Key findings include the identification and explanation of clustering in both space and time, and the efficacy of a temporal Hawkes process with a sinusoidal background in predicting home sale occurrences. The temporal analysis starts by employing the state of art techniques for time series data like regression, autoregressive, and autoregressive integrated moving …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …