Statistics and Probability Commons^™

Pre-Service Teachers’ Emerging Views On Educational Equity, Melody Wilson

Master's Theses and Doctoral Dissertations

An equity-based Statistics course for pre-service mathematics teachers could play a role in the development of pre-service teachers’ equity literacy, encouraging conversations about equity in education and illuminating structural factors that contribute to the educational opportunity gap in the U.S. In the Winter 2019 semester, a faculty team at the author’s university piloted such a course. The course included data explorations dealing with structural inequities by race – one of the most difficult topics to address productively in a teacher preparation course. For the present study, a survey of pre-service teachers’ views on educational equity was administered in a required …

The Impacts Of Race, Residence, And Prenatal Care On Infant Mortality, Mary Christine Dorley

Walden Dissertations and Doctoral Studies

Tennessee ranks high for infant mortality (IM) in the United States. Despite public health efforts, the IM rate for Blacks is twice that of Whites mimicking what is observed nationally. Several risk factors for IM have been identified; however, it was still unclear how places of residence and prenatal care (PNC) affect IM for Tennesseans. The purpose of this study was to assess the relationship between places of residence (conceptualized by rurality and racial concentration), PNC, and IM among racial groups across Tennessee and to determine if race modified these associations. This was a cross-sectional study using data from the …

Applying Machine Learning Algorithms For The Analysis Of Biological Sequences And Medical Records, Shaopeng Gu

Electronic Theses and Dissertations

The modern sequencing technology revolutionizes the genomic research and triggers explosive growth of DNA, RNA, and protein sequences. How to infer the structure and function from biological sequences is a fundamentally important task in genomics and proteomics fields. With the development of statistical and machine learning methods, an integrated and user-friendly tool containing the state-of-the-art data mining methods are needed. Here, we propose SeqFea-Learn, a comprehensive Python pipeline that integrating multiple steps: feature extraction, dimensionality reduction, feature selection, predicting model constructions based on machine learning and deep learning approaches to analyze sequences. We used enhancers, RNA N6- methyladenosine sites and …

Lack Of Vaccination Risks, Abigail Sebunia

Williams Honors College, Honors Research Projects

This paper is a study regarding how vaccination rates are related to the number of measles cases that occur in a particular state. First, I will review the history of vaccines and the motivations for the refusals of this medical procedure. In addition, I will examine the various regulations regarding vaccinations and which states allow non-medical exemptions for religious or personal reasons. Within my analysis, I will provide examples of recent outbreaks to represent the extent of this current dilemma. Furthermore, I will offer potential solutions to mitigate measles outbreaks and the science regarding Herd Immunity Thresholds (HIT) to limit …

Effects On Perception And Accuracy Of Live Putting When Leaving The Flagstick In The Hole, Danielle Nicholson

Williams Honors College, Honors Research Projects

The purpose of this study was to investigate the preferences of golfers and how those preferences affected their putting. This study involved participants who were golfers with a four-handicap or better taking a survey of their preferences of having the flagstick in and out and then assessing their putting from various distances with the flagstick in and out. The researcher measured the distance of the golf ball away from the hole after it had finished rolling. Then, t-tests were used to compare the data with the flagstick in and flagstick out to determine if there is a significant difference. The …

Microarray Data Analysis And Classification Of Cancers, Grant Gates

Williams Honors College, Honors Research Projects

When it comes to cancer, there is no standardized approach for identifying new cancer classes nor is there a standardized approach for assigning cancer tumors to existing classes. These two ideas are known as class discovery and class prediction. For a cancer patient to receive proper treatment, it is important that the type of cancer be accurately identified. For my Senior Honors Project, I would like to use this opportunity to research a topic in bioinformatics. Bioinformatics incorporates a few different subjects into one including biology, computer science and statistics. An intricate method for class discovery and class prediction is …

A Comparative Study Of Kendall-Theil Sen, Siegel Vs Quantile Regression With Outliers, Ahmad Farooqi

Wayne State University Dissertations

Researchers in social and behavioral sciences usually interested in study the relationship between a response variable Y_i and one or more independent predictors〖 X〗_i either for the purpose of explanation or prediction. Ordinary Least Square Regression is a parametric approach used to study this kind of relationship. One of the disadvantages of Ordinary Least Square is it does not fit well in the presence of outliers in the response variable Y_i or both in the response variable Y_i and the predictor variable〖 X〗_i, also if the data were sampled from a non-normal distribution. Quantile Regression, Theil-Sen regression, and the modified …

Reporting Number Needed To Treat In Clinical Trials Published In Physical Therapy Specific Literature 1989 - 2018, Susan Ann Talley

Wayne State University Dissertations

Evidence-based practice requires physical therapists to make clinical decisions about the best intervention to use when providing services to patients/clients. Although null hypothesis significance testing (NHST) is frequently used to interpret the outcome of a clinical trial investigating the comparative effectiveness of an intervention, statistical significance does not directly translate into clinical importance. Number needed to treat (NNT) is a measure of effect size (ES) that may be particularly useful when translating the results from clinical trials to PT clinical practice. The purpose of this study was to conduct a bibliometric content analysis of the methods of reporting research results …

Global Warming Statistical Analysis, Jared Skinner

Williams Honors College, Honors Research Projects

This paper will investigate global warming and its effects on natural disasters. I will review the historic movements of climate change and activism, as well as the current discussions surrounding global warming. Secondly, I will examine various datasets, paying attention to the severity and frequency of specific natural disasters. I will then touch briefly on the topic of catastrophe modeling as it relates to the increased risk and losses associated with the discussed natural disasters and how those put the problem of global warming in a framework which financial and government institutions can grasp. I will also be analyzing economic …

Automatic 13C Chemical Shift Reference Correction Of Protein Nmr Spectral Data Using Data Mining And Bayesian Statistical Modeling, Xi Chen

Theses and Dissertations--Molecular and Cellular Biochemistry

Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially of biomacromolecules such as proteins. However, due to the intrinsic properties of NMR experiments, results from the NMR instruments require a refencing step before the down-the-line analysis. Poor chemical shift referencing, especially for ¹³C in protein Nuclear Magnetic Resonance (NMR) experiments, fundamentally limits and even prevents effective study of biomacromolecules via NMR. There is no available method that can rereference carbon chemical shifts from protein NMR without secondary experimental information such as structure or resonance assignment.

To solve this problem, we …

Bayesian Analysis For The Intraclass Model And For The Quantile Semiparametric Mixed-Effects Double Regression Models, Duo Zhang

Dissertations, Master's Theses and Master's Reports

This dissertation consists of three distinct but related research projects. The first two projects focus on objective Bayesian hypothesis testing and estimation for the intraclass correlation coefficient in linear models. The third project deals with Bayesian quantile inference for the semiparametric mixed-effects double regression models. In the first project, we derive the Bayes factors based on the divergence-based priors for testing the intraclass correlation coefficient (ICC). The hypothesis testing of the ICC is used to test the uncorrelatedness in multilevel modeling, and it has not well been studied from an objective Bayesian perspective. Simulation results show that the two sorts …

Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall

Numeracy

We discuss the connection between the numeracy and social justice movements both in historical context and in its modern incarnation. The intersection between numeracy and social justice encompasses a wide variety of disciplines and quantitative topics, but within that variety there are important commonalities. We examine the importance of sound quantitative measures for understanding social issues and the necessity of interdisciplinary collaboration in this work. Particular reference is made to the papers in the first part of the Numeracy special collection on social justice, which appear in this issue.

An Overview And Evaluation Of Synthetc: A Statistical Model For Extra-Tropical Cyclones, Rafael Uryayev

Dissertations and Theses

Extratropical cyclones (ETCs) are the most common weather phenomena affecting the United States, Canada, and Europe. They can pose serious hazards over large swaths of area. In this thesis, a statistical model of ETCs, called SynthETC, is discussed. The model accounts for the for genesis, track path, termination, and intensity of statistically generated ETCs. Genesis is modeled as a Poisson process, whose mean is determined by climate and historical information. Tracks are modeled as a regression-mean determined by climate and historical information plus a stochastic component. Lysis is modeled using logistic regression, with climate states as covariates. Intensity is modeled …

Hydroclimate Drivers And Atmospheric Dynamics Of Floods, Nasser Najibi

Dissertations and Theses

Our preliminary survey showed that most of the recent flood-related studies did not formally explain the physical mechanisms of long-duration and large-peak flood events that can evoke substantial damages to properties and infrastructure systems. These studies also fell short of fully assessing the interactions of coupled ocean-atmosphere and land dynamics which are capable of forcing substantial changes to the flood attributes by governing the exceeding surface flow regimes and moisture source-sink relationships at the spatiotemporal scales important for risk management. This dissertation advances the understanding of the variability in flood duration, peak, volume, and timing at the regional to the …

Snap Scholar: The User Experience Of Engaging With Academic Research Through A Tappable Stories Medium, Ieva Burk

CMC Senior Theses

With the shift to learn and consume information through our mobile devices, most academic research is still only presented in long-form text. The Stanford Scholar Initiative has explored the segment of content creation and consumption of academic research through video. However, there has been another popular shift in presenting information from various social media platforms and media outlets in the past few years. Snapchat and Instagram have introduced the concept of tappable “Stories” that have gained popularity in the realm of content consumption.

To accelerate the growth of the creation of these research talks, I propose an alternative to video: …

A Comparison Of Bayesian Estimation Techniques In A Multidimensional Two-Parameter Partial Credit Item Response Model, Peiyan Liu

Electronic Theses and Dissertations

Bayesian estimation methods have shown better performance than the traditional Marginal Maximum Likelihood (MML) estimation method for parameter estimation in relatively simple item response models. However, extant literature is lacking on the investigation of Bayesian parameter estimation approaches for a multidimensional two parameter partial credit (M2PPC) model, therefore this simulation study investigated the performance of two Bayesian Markov Chain Monte Carlo (MCMC) algorithms: Gibbs Sampler and Hamiltonian Monte Carlo-No-U-Turn-Sampler (HMC-NUTS) for M2PPC models' parameter estimation. It compared the estimation accuracy and computing speed in different combinations of situations, including prior choices, test lengths, and the relationships between dimensions.

The datasets …

Assessing The Performance And Merit Of The Random Survival Forest And Cox Models On A Pancreatic Cancer Data Set, Carl Edward Mueller

Graduate Research Theses & Dissertations

Random Survival Forest (RSF) is one of the most powerful and easily applied machine learning models for survival data. RSF sacrifices some of the interpretability of the decision trees used to grow the forest in order to significantly reduce the bias and variance of the basic classification and regression tree (CART) paradigm. The lessened interpretability and higher computational intensity of RSF means that it may not always be the preferred method, even in settings where black-box methods are readily used. By contrast, the Cox Proportional Hazards (PH) model is incredibly flexible, resistant to overfitting, and transparently estimable. The tradeoff for …

Bayesian Functional Data Analysis Over Dependent Regions And Its Application For Identification Of Differentially Methylated Regions, Suvo Chatterjee

Graduate Research Theses & Dissertations

Bayesian functional data analysis (BFDA) provides flexible statistical inferences under harsh circumstances such as a large volume of data, considerable measurement errors and missing observations. Considering a sequence of segments and functional data analysis on each segment, where neighboring segments can be dependent, demanding computation is indispensable and analysis is sometimes infeasible for large number of segments. We consider a utilization of BFDA to identify differentially methylated regions (DMRs). Out of numerous existing methodologies to detect DMRs, there still does not exist a standard approach to identify DMRs especially under the assumption of dependency among genomic regions. In this dissertation, …

Simulating And Modelling Opinion Dynamics, Jennifer Heermance

Graduate Research Theses & Dissertations

The foundation of social media is conversation. Social media allows people to share ideas and opinions, as well as discuss those opinions. A point of intrigue for many social scientists is how those opinions change through interaction with others. What influences someone’s opinion? When is a person willing to adapt their opinion, and when does it remain the same? Is it possible to measure these opinion dynamics? Our overall goal is to develop a more comprehensive model for opinion dynamics. The first step of this process is to simulate data that can then be analyzed and used to develop a …

Exploring A Bayesian Analysis Of Opinion Dynamics Using The Approximate Bayesian Computation Method, Jessica L. Bishop

Graduate Research Theses & Dissertations

Social media has created a whole new framework in the way we understand ones expression of opinion, and how ones' opinion can influence others. Models of opinion dynamics, such as a probabilistic modeling framework of opinion dynamics over time are given by Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, and Manuel Gomez Rodriguez in ``Learning and Forecasting Opinion Dynamics in Social Networks." In this paper, we will continue to explore their models, now coming from a Bayesian statistical standpoint, specifically looking at the Approximate Bayesian Computation (ABC) method for the computation of better estimations for the data. We will …