Statistics and Probability Commons^™

Wavelet-Based Multiscale Analysis Of Cave Co₂ Concentration In Response To Short-Term High-Intensity Tourism Activities: Spatiotemporal Heterogeneity And Lag Characteristics, Mingda Cao, Wenwen Song, Yan Zhang, Jie Zhang, Zhiqiang Yao

International Journal of Speleology

High-intensity tourism activities can cause a significant increase in cave air CO₂ concentration, thereby affecting the cave micro-environment and secondary carbonate deposition. During the 2023 National Day Golden Week, high-frequency continuous monitoring of cave air CO₂ partial pressure (PCO_2(A)) and visitor numbers was conducted in Dawang Cave, Anhui Province. Wavelet transform and cross-correlation analyses were used to reveal the multi-scale response characteristics of CO₂ concentration to tourism activities. The results show that: (1) PCO_2(A) exhibited clear diurnal variations (higher during the day, lower at night) and decreased spatially with enhanced ventilation, controlled jointly by …

Drainage Proximity And Sinkhole Occurrence In Sivrihisar (Central Turkey): A Comparative Analysis Of Linear, Poisson, And Negative Binomial Regression Models, Bilge Bingül, Emrah Pekkan, Resul Çömert

International Journal of Speleology

This study investigates the relationship between sinkhole occurrence and distance to drainage in the Sivrihisar region (Central Turkey) and evaluates the suitability of different regression approaches for modeling clustered count data in karst terrains. A comprehensive inventory of 104 sinkholes developed within the Neogene lacustrine limestones of the Akpınar Formation was compiled using official records, remote sensing analyses, and detailed field surveys. Sinkhole occurrences were analyzed relative to a drainage network derived from a high-resolution Digital Surface Model and grouped by proximity to drainage lines. Linear Regression (LM), Poisson Regression (PR), and Negative Binomial Regression (NBR) models were comparatively applied …

Teaching Numeracy For Social Justice: Educational Equity, Esther Isabelle Wilder, Crystal C. Rodriguez, Caterina Shost, Eduardo Vianna, Frank Wang

Numeracy

The special collection, “Teaching Numeracy for Social Justice: Educational Equity,” focuses on how quantitative reasoning (QR) can function as a vehicle for equity, empowerment, and democratic participation. Building on a longstanding tradition that treats numeracy as inseparable from social justice, these contributions highlight how progressive pedagogies (e.g., active learning, authentic research, and equity-oriented assessment) have the potential to broaden access for students historically excluded from quantitative fields. The studies span a variety of conceptual frameworks, introductory and advanced quantitative instruction, and interdisciplinary applications, showcasing how inclusive QR practices can build confidence, agency, and real-world understanding. These articles demonstrate that when …

Statistical Challenges And Simulation Results For Pilot Clinical Trials, Weiliang Cen

USF Tampa Graduate Theses and Dissertations

Background: The effect size estimated from a pilot trial is often an inaccurate reflection of the true effect size observed in a large trial, leading to either underestimation or overestimation. Published data suggest that effect sizes from large trials are typically smaller than those reported in their corresponding pilot trials. To address this discrepancy, conservative or discount adjustment methods are widely recommended to modify pilot trial effect sizes when calculating sample sizes, thereby maintaining adequate statistical power. This study aims to assess effect sizes from both pilot and large trials and to evaluate the performance of existing adjustment methods.

Methods: …

Bayes In The Brain: A Review Of Everything Is Predictable: How Bayesian Statistics Explain Our World, (2024) By Tom Chivers., Michael T. Catalano

Numeracy

Tom Chivers’ Everything is Predictable: How Bayesian Statistics Explain Our World, is an interesting and wide-ranging narrative on Bayesian thinking, its history, and its applicability to both our everyday lives and the pursuit of scientific truth. Although appropriate for the non-expert, afficionados and teachers of quantitative literacy should find the plethora of examples, links to psychology as it applies to how people reason about probabilities, and even Chivers’ philosophical musings informative and thought-provoking.

Textbook Content Analysis For Statistical Content Standards For 8th-Grade Math: Commercial Publishers, Curriculum Software Supplement, And An Open Educational Resource, Matthew M. O'Brien

USF Tampa Graduate Theses and Dissertations

This dissertation examined the vertical and horizontal content analysis of statistical content within current 8th-grade mathematics instructional materials to determine the extent to which students are provided with opportunity to learn high cognitive instances and the usage of the four phases of statistical problem-solving across two commercial publishers, one open educational resource, and one curriculum software supplement. All previous analyses of statistics education content textbooks have only examined textbooks from commercial publishers or those published by national governments. The horizontal content textbook analysis investigated the textbook's background information and overall structure. The vertical content textbook analysis examined the cognitive level …

Does A Name Make A Difference? Teaching Random Selection In The Classroom, Venessa Singhroy, Rommel Robertson, Kostas Stroumbakis

Numeracy

In Statistics education, it is crucial to emphasize the foundational significance of random selection, which underpins statistical methodologies and ensures unbiased representation of populations in samples. However, students often struggle to grasp the concept’s complexities, leading to challenges in applying random selection methods effectively. This paper examines the gap between students’ theoretical understanding of randomness and their practical application of this concept. Using an Explanatory Sequential Design, this study presents an instructional activity aimed at teaching the concept of randomness in the selection process and proposes modifications to enhance student comprehension. The activity, implemented in undergraduate Statistics and Psychology courses, …

Optimization Of Markov Chain Modeling In Predicting College Student Retention, Kien Nguyen

Journal of Global Education and Research

College student retention is one of the most important metrics in higher education. With institutions across the US facing decreasing enrollment, developing a reliable retention prediction method is crucial. In recent years, the use of the Markov chain model in forecasting student enrollment and progression has become more common, but there is little work on its application in student retention. One key factor in determining this model's effectiveness is what parameters should be used in the student population’s segmentation or grouping. This study presents a rigorous algorithm, coupled with a prediction model, capable of selecting parameters that provide the most …

Essays On Information Technology In Healthcare, Gleb Zavadskiy

USF Tampa Graduate Theses and Dissertations

Information technologies (IT) and information systems (IS) have profound significance across various sectors of society, including healthcare, business, education, government, and beyond. First, IT facilitates instant communication globally through email, messaging apps, video conferencing, and social media, revolutionizing how individuals and organizations interact, collaborate, and share information (Hacker et al. 2020; Tang and Hew 2020).

Secondly, the Internet and digital libraries provide worldwide access to vast amounts of information, what makes knowledge and education available to everyone, empowering individuals to learn and stay informed on diverse topics (Haleem et al. 2022). Another aspect of IT systems in various industries is …

Artificial Intelligence Modeling Of Alzheimer's Disease And Environmental Science, Mohamed Abu Sheha

USF Tampa Graduate Theses and Dissertations

A data-driven statistical model operates as a mathematical illustration of a tangible issuefaced in reality, enabling the creation of predictions or decisions based on data. The process of utilizing probability and statistical principles in statistical models is essential for deriving meaningful conclusions from the data. The research conducted in this dissertation utilizes artificial intelligence (AI) models to integrate findings across health and environmental sci- ence.

The first research study in this dissertation, Alzheimer’s disease is a mental health issue and a brain aging dilemma that makes it difficult for older people to complete daily tasks without assistance. The physician uses …

Mathematical Modeling Of Tumor Response Dynamics To Predict Progression-Free Survival In Patients With Recurrent High-Grade Glioma, Daniel James Glazar

USF Tampa Graduate Theses and Dissertations

In this dissertation, I aim to develop a mathematical model describing tumor volume response dynamics to perform individual dynamic predictions of progression-free survival (PFS) on patients with recurrent high-grade glioma (rHGG).

Patients with rHGG have a dismal prognosis with median overall survival (OS) of <12 months and median PFS of <7 months. However, there is a wide heterogeneity in treatment responses. Therefore, to aid clinicians with making decisions to alter therapeutic protocol, I would like to predict patient-specific PFS.

To perform individual dynamic predictions, I employ the Claret tumor growth inhibition (TGI) model. I further develop this model by coupling it with two different survival models. Inter-patient heterogeneity is also taken into account through mixed effects, including covariate effects. Model PFS predictions were evaluated using receiver operating characteristic (ROC) curve analysis as well as Brier …

Stochastic Analytical Predictive Models For Life Sciences And Crop Production Process, Erasmus Tetteh-Bator

USF Tampa Graduate Theses and Dissertations

Analytical predictive modeling uses algorithm-based mathematical, probabilistic and statistical methods to anticipate future events by identifying patterns in past data. It is a technique for predicting outcomes and a key application of statistical analysis in real-world scenarios. Real data-driven predictive models in various fields, such as life sciences, economics,or production industry, help individuals and institutions make data-driven informed decisions, which is essential for business success, providing companies with a competitive edge.

One of every five adult deaths is caused by heart disease and one person dies every 33 from cardiovascular disease in the United States according to the Center for …

Comparative Analysis Of Time Series Models On U.S. Stock And Exchange Rates: Bayesian Estimation Of Time Series Error Term Model Versus Machine Learning Approaches, Young Keun Yang

USF Tampa Graduate Theses and Dissertations

This study presents a comparative analysis of contemporary applications of time series models, focusing on the Bayesian approach. In contrast to many nonparametric studies, the Bayesian approach circumvents the common issue of bandwidth selection by offering systematic estimation and avoiding ad hoc methods. Specifically, we delve into the Bayesian approach for estimating the autocovariance function of a time series model’s error term. Traditional time series models often make the unrealistic assumption of a constant error term. Furthermore, models such as autoregressive conditional heteroskedasticity (ARCH) and general autoregressive conditional heteroskedasticity (GARCH) address the limitation of constant variance by assuming an autoregressive …

Advancing Text Summarization And Classification: Deep Insights From Transformer-Based Statistical Learning, Kun Bu

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) is a part of human's daily life nowadays. Machine Learning (ML) as one aspect from AI has been rapidly developing during the past two decades, especially from the statistical learning approaches, which emphasized the use of probability and statistics to model data, such as Support Vector Machines (SVMs) for classification and regression tasks to the ensemble learning techniques, such as Random Forest, Gradient Boosting Machine (GBM), and stacking. Ensemble learning has evolved into a pivotal concept in contemporary machine learning, empowering practitioners to amalgamate multiple models to enhance generalization, accuracy, and robustness. As the field of machine …

Utilizing Machine Learning Techniques For Accurate Diagnosis Of Breast Cancer And Comprehensive Statistical Analysis Of Clinical Data, Myat Ei Ei Phyo

USF Tampa Graduate Theses and Dissertations

Breast cancer represents a formidable malignancy, presenting a substantial threat to global health and individual well-being. Conventionally, it is widely held that the prognosis for breast cancer patients hinges predominantly upon the timing of diagnosis and the extent of cancer progression, typically delineated by its stage. However, emerging evidence from robust regression and machine learning analyses challenges this prevailing notion. The results indicate that survival months cannot be solely attributed to diagnosis and socio-economic factors. Instead, additional variables such as existing diseases and treatment complexities may contribute to the intricate landscape of breast cancer outcomes.

This research aims to delve …

Using Social Network Analysis To Measure And Visualize Student Clustering Within Middle And High Schools, Geoffrey David West

USF Tampa Graduate Theses and Dissertations

The dominant philosophy of American public schools has been to group students together based on similar characteristics. Known as tracking, high achieving students would take courses on the “college track” while others would take “career track” courses. It was not long until advocates noticed that this process unfairly advantaged affluent and White student over poor and minoritized groups. A new process called “ability grouping” took over where tracking left off, but to the same effect. It is difficult to measure the degree students are grouped together by a certain characteristic, and while a few research papers aim to do so, …

Cybersecurity: Stochastic Intensity Function And Monitoring Indicators, Hackers Demographics, Statistical Analysis And Treatment Of Ovarian Cancer, Ranju Karki

USF Tampa Graduate Theses and Dissertations

One of the major tasks in the present day-era is securing computer systems against unauthorized access. Every year we lost millions of dollars because of cyber attacks and thousands of people suffer economically and psychologically. Rapid development in the field of information technology increases the challenges to the Information technology personnel working on protecting against cyber attacks. In cyber security, vulnerabilities and hackers play a major role. Researchers are putting in enormous efforts to develop methods and models to control vulnerabilities and psychological behavior and motivation of hackers. Our study defines two important aspects of the computer operating system concerning …

Real Data–Driven Analytical Predictive Modeling For Financial Systems: Stochastic Intensity Function And Monitoring Indicator, Jayanta Kumar Pokharel

USF Tampa Graduate Theses and Dissertations

Data contain important information and data driven decision making process is the most for any type of businesses to succeed. It is the fact that businesses which follow data driven decision making process will have competitive edge over their counterparts which contributes in value creation for any firm or individual. Statistical procedure and methodology is the heart of data science which helps to extract crucial information from the data scientifically. It is common technique to use algorithm on historical data to see the outcome for the future which we defined as “prediction”, and predictive modeling is one of major statistical …

Exploring Time-Varying Extraneous Variables Effects In Single-Case Studies, Ke Cheng

USF Tampa Graduate Theses and Dissertations

The effect of time-varying extraneous variables has been studied in other statistical analyses such as using Kaplan–Meier or Cox regression analysis in survival analyses. Nonetheless, the effect of modeling versus not modeling individual specific time varying extraneous variables has not been explored in multiple-baseline single case designs through Monte Carlo simulation studies. Therefore, in my dissertation, I used simulation methods to explore for a variety of conditions (varying in the number of participants, number of observations per participant, type of extraneous variable effect, size of the true intervention effect) the impact of extraneous variables on bias and standard error of …

Statistical Analysis Of Ribonucleotide Incorporation In Human Cells, Tejasvi Channagiri

USF Tampa Graduate Theses and Dissertations

During the DNA replication process, ribonucleotides, the building blocks of RNA, may be occasionally incorporated in the newly synthesized DNA. DNA is primarily composed of deoxyribonucleotides and there exist cellular mechanisms for removing ribonucleotides from DNA, which may point towards ribonucleotide incorporation being a replication error. Further, an excess of these ribonucleotides in the genome has been known to lead to genomic instability and has been implicated in human diseases. However, there are also hypotheses that suggest that ribonucleotides may be beneficial in certain circumstances. In this study we examine ribonucleotide incorporation in the human genome in several human cell …

Fuzzy Kc Clustering Imputation For Missing Not At Random Data, Markku A. Malmi Jr.

USF Tampa Graduate Theses and Dissertations

Research has a variety of difficulties, especially when involving human subjects, and one of the most prevalent is the issue of missing data. Missing data will always be present in research due to the fact there is no perfect method for collecting data and protecting against human error or mechanical failure. This requires researchers to be able to mitigate the problems that come along with missing data; reduction in power of an analysis and bias introduced by the missing pattern. This research investigated a non-parametric method using a nested approach of fuzzy K-Modes and fuzzy C-Means clustering to impute missing …

Beyond Statistical Significance: A Holistic View Of What Makes A Research Finding "Important", Jane E. Miller

Numeracy

Students often believe that statistical significance is the only determinant of whether a quantitative result is “important.” In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction, causality, generalizability, and changeability of the independent variable. I illustrate these issues with examples from an empirical study of the association between how much time teenagers spent playing video games and time spent reading. I describe how study design and context determine each of those aspects of “importance,” and close by summarizing how to provide a …

Establishing The Validity And Reliability Of The Locus Assessments, Tim Jacobbe, Bob Delmas, Brad Hartlaub, Jeff Haberstroh, Catherine Case, Steven Foti, Douglas Whitaker

Numeracy

The development of assessments as part of the funded LOCUS project is described. The assessments measure students’ conceptual understanding of statistics as outlined in the GAISE PreK–12 Framework. Results are reported from a large-scale administration to 3,430 students in grades 6 through 12 in the United States. Items were designed to assess levels of understanding as well as components of the statistical problem solving process as articulated in the GAISE framework. We discuss details of how the model used to develop the LOCUS assessments guided the gathering of evidence for validity and reliability arguments. Three types of validity evidence are …

Statistical Methods For Reliability Test Planning And Data Analysis, Oluwaseun Elizabeth Otunuga

USF Tampa Graduate Theses and Dissertations

This dissertation develops several statistical methods to advance the techniques and applications in the fields of reliability test planning and data analysis as well as statistical modeling and analysis in survival analysis.

The first project focuses on developing new demonstration test plans for lifetime data based on considering multiple objectives. Reliability demonstration tests have been broadly used for assuring reliability performance at the desired confidence level. We consider lifetime data that follows a Weibull distribution which has been broadly used for modeling a variety of shapes of lifetime distributions. When planning a demonstration test, there are often multiple aspects to …

Exploring The Vulnerability Of A Neural Tangent Generalization Attack (Ntga) - Generated Unlearnable Cifar-10 Dataset, Gitte Ost

USF Tampa Graduate Theses and Dissertations

Nowadays, a massive amount of data is generated and stored on servers and cloudsfrom various applications daily. Preventing these data from unauthorized use often becomes necessary and critical in various real-world applications. Many researchers have studied this crucial problem and developed different methods for this purpose. Among them, Neural Tangent Generalization Attack (NTGA) is one of the most efficient methods to make a dataset unlearnable, which means that the dataset is not learnable by machine learning/deep learning methods. That is, the NTGA-generated dataset is protected against unauthorized use. In this thesis, we explore the vulnerability of an NTGA-generated unlearnable CIFAR-10 …

Association Between Use Of Remdesivir And Bradycardia, Gibret Umeukeje

USF Tampa Graduate Theses and Dissertations

Remdesivir received the first emergency use authorization from the FDA for the treatment of COVID-19. Multiple adverse drug reactions (ADR) have been reported since its approval in October 2020. Bradycardia, defined by a decrease in heart rate has been reported as an adverse event for patients receiving remdesivir for COVID-19 treatment. The purpose of the research is to systematically investigate the frequency of occurrence of bradycardia in adults receiving remdesivir using clinical data derived from the FDA Adverse Event Reporting System (FAERS) database. Patients receiving remdesivir were compared to those receiving Paxlovid, Regen-Cov, and Dexamethasone for COVID-19 treatment to see …

An Attempt To Develop A Measurement Tool For Interpretation Performance Of Tourist Guides, Gizem Capar, Dilek Atci

University of South Florida (USF) M3 Publishing

The search for different experiences in touristic visits brings the necessity of differentiating the tours for tour guides with. Interpretation lies at the heart of this differentiation. This research aims to examine the structure of interpretation performance of tour guides empirically within the framework of E.R.O.T/T.O.R.E model. For this purpose, in line with the literature firstly conceptual structure of interpretation performance and interpretative guiding was determined, then expert opinion was sought with the expression pool consisting of draft statements. After expertising process, the measurement tool was first applied on a sample of 191 participants. For preliminary analysis the performance of …

Joint Models For Repeated Measured, Non-Normally Distributed Multilevel Data, Weiwei He

USF Tampa Graduate Theses and Dissertations

Clinical trials have tended to collect both survival information and longitudinal biomarkers, as well as other covariates. In order to better assess the severity of diverse diseases, we need to collect various longitudinal outcomes. Furthermore, longitudinal data could consist of a number of different measurements of varying types. The multilevel item response theory (MLIRT) model has been widely used in several fields such as public health and health sciences for multivariate longitudinal outcomes. Joint models combining the longitudinal and survival processes, as well as the relation between them, have been developed to minimize bias and improve the efficiency of estimates. …

Talking About Statistical Significance In Numeracy, Nathan D. Grawe, Gizem Karaali

Numeracy

In recent years, much debate has surrounded the potential for audiences to be mislead by several common practices when reporting statistical significance tests. Two editors of Numeracy share the journals perspectives on these questions. As an interdisciplinary journal, we recognize and honor the genre differences represented by our authors and audience members. As a consequence, the journal is open to many practices. Still, we acknowledge the concerns raised by the American Statistical Association and others and encourage authors to write with care and clarity, however results may be represented.

Data-Driven Analytical Predictive Modeling For Pancreatic Cancer, Financial & Social Systems, Aditya Chakraborty

USF Tampa Graduate Theses and Dissertations

Pancreatic cancer is one of the most deathly disease and becoming an increasingly commoncause of cancer mortality. It continues giving rise to massive challenges to clinicians and cancer researchers. The combined five-year survival rate for pancreatic cancer is extremely low, about 5 to 10 percent, owing to the fact that a large number of the patients are diagnosed at stage IV when the disease has metastasized. Our study investigates if there exists any statistical significant difference between the median survival times and also the survival probabilities of male and female pancreatic cancer patients at different cancer stages, and irrespective of …