Physical Sciences and Mathematics Commons^™

Conceptualizing And Interpreting Mean And Median With Future Teachers, Eryn Stehr Maher, Ha Nguyen, Gregory Chamblee, Sharon Taylor

Department of Mathematical Sciences Faculty Publications

Mathematical Education of Teachers II (METII), echoed by the American Statistical Association publication, Statistical Education of Teachers, recommended teacher preparation programs support future teachers in developing deep understandings of mean and median, such that middle grades teachers may use them to “summarize, describe, and compare distributions” (Conference Board of Mathematical Sciences, 2012, p. 44; Franklin et al., 2015). Georgia Standards of Excellence require statistical reasoning from students beginning as early as 6-7 years old, including interpretation of measures of center and statistical reasoning about best measures of center (Georgia Department of Education, 2015). This level of understanding and interpretation of …

Investigating The Factors That Best Describe Student Experience And Performance In College, Abigale Wynn

Undergraduate Honors Thesis Collection

The National Survey of Student Engagement (NSSE) surveys students at four-year institutions around the United States in order to offer Universities accessible ways to evaluate their students' experiences and performance. The NSSE data is collected in the form of a Likert-scale survey geared towards first year and senior year students. It asks questions about how they spend their time throughout the academic year and how they rate their experience. This thesis looks at the NSSE survey data from Butler University in 2016 and attempts to apply classification techniques and predictive models to draw conclusions about student performance. Methods such as …

Estimation And Variable Selection In High-Dimensional Settings With Mismeasured Observations, Michael Byrd

Statistical Science Theses and Dissertations

Understanding high-dimensional data has become essential for practitioners across many disciplines. The general increase in ability to collect large amounts of data has prompted statistical methods to adapt for the rising number of possible relationships to be uncovered. The key to this adaptation has been the notion of sparse models, or, rather, models where most relationships between variables are assumed to be negligible at best. Driving these sparse models have been constraints on the solution set, yielding regularization penalties imposed on the optimization procedure. While these penalties have found great success, they are typically formulated with strong assumptions on the …

Toward Collaborative Open Data Science In Metabolomics Using Jupyter Notebooks And Cloud Computing, Kevin M. Mendez, Leighton Pritchard, Stacey N. Reinke, David I. Broadhurst

Research outputs 2014 to 2021

Background

A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results. As an omics science, which generates vast amounts of data and relies heavily on data science for deriving biological meaning, metabolomics is highly vulnerable to irreproducibility. The metabolomics community has made substantial efforts to align with FAIR data standards by promoting open data formats, data repositories, online spectral libraries, and metabolite databases. Open data analysis platforms also exist; however, they tend to be inflexible and rely on the user to adequately report their methods …

The Effect Of Using A Project-Based Learning (Pbl) Approach To Improve Engineering Students' Understanding Of Statistics, Fionnuala Farrell, Michael Carr

Articles

Over the last number of years we have gradually been introducing a project based learning approach to the teaching of engineering mathematics inDublin Institute of Technology. Several projects are now in existence for the teaching of both second-order differential equations and first order differential equations.We intend to incrementally extend this approach acrossmore of the engineering mathematics curriculum. As part of this ongoing process, practical realworld projects in statistics were incorporated into a second year ordinary degree mathematics module. This paper provides an overview of these projects and their implementation. As a means to measure the success of this initiative, we …

A Self-Contained Course In The Mathematical Theory Of Statistics For Scientists & Engineers With An Emphasis On Predictive Regression Modeling & Financial Applications., Tim Smith

Open Access Textbooks

Preface & Acknowledgments

This textbook is designed for a higher level undergraduate, perhaps even first year graduate, course for engineering or science students who are interested to gain knowledge of using data analysis to make predictive models. While there is no statistical perquisite knowledge required to read this book, due to the fact that the study is designed for the reader to truly understand the underlying theory rather than just learn how to read computer output, it would be best read with some familiarity of elementary statistics. The book is self-contained and the only true perquisite knowledge is a solid …

Conceptualizing And Interpreting Mean And Median With Future Teachers, Eryn M. Stehr, Ha Nguyen, Gregory Chamblee, Sharon Taylor

Proceedings of the Annual Meeting of the Georgia Association of Mathematics Teacher Educators

Mathematical Education of Teachers II (METII), echoed by the American Statistical Association publication, Statistical Education of Teachers, recommended teacher preparation programs support future teachers in developing deep understandings of mean and median, such that middle grades teachers may use them to “summarize, describe, and compare distributions” (Conference Board of Mathematical Sciences, 2012, p. 44; Franklin et al., 2015). Georgia Standards of Excellence require statistical reasoning from students beginning as early as 6-7 years old, including interpretation of measures of center and statistical reasoning about best measures of center (Georgia Department of Education, 2015). This level of understanding and interpretation of …

Optical Vortex And Poincaré Analysis For Biophysical Dynamics, Anindya Majumdar

Dissertations, Master's Theses and Master's Reports

Coherent light - such as that from a laser - on interaction with biological tissues, undergoes scattering. This scattered light undergoes interference and the resultant field has randomly added phases and amplitudes. This random interference pattern is known as speckles, and has been the subject of multiple applications, including imaging techniques. These speckle fields inherently contain optical vortices, or phase singularities. These are locations where the intensity (or amplitude) of the interference pattern is zero, and the phase is undefined.

In the research presented in this dissertation, dynamic speckle patterns were obtained through computer simulations as well as laboratory setups …

Modeling Stochastically Intransitive Relationships In Paired Comparison Data, Ryan Patrick Alexander Mcshane

Statistical Science Theses and Dissertations

If the Warriors beat the Rockets and the Rockets beat the Spurs, does that mean that the Warriors are better than the Spurs? Sophisticated fans would argue that the Warriors are better by the transitive property, but could Spurs fans make a legitimate argument that their team is better despite this chain of evidence?

We first explore the nature of intransitive (rock-scissors-paper) relationships with a graph theoretic approach to the method of paired comparisons framework popularized by Kendall and Smith (1940). Then, we focus on the setting where all pairs of items, teams, players, or objects have been compared to …

Basketball Charts, Kevin Lewis

Williams Honors College, Honors Research Projects

The purpose of this project was to develop an interactive web application with access to a self-updating database of basketball statistics. This data would then be used to allow users to generate informative visuals about specific sets of players. Obtaining statistics from the National Basketball Association (NBA) for the 2018-19 season was the original target goal. By utilizing an open source and community driven API, this goal was successfully achieved. With the data in place, the development of the chart building tool that was intended to be the primary functionality of the web application could begin. Highcharts was used as …

Automated Trading Systems Statistical And Machine Learning Methods And Hardware Implementation: A Survey, Boming Huang, Yuziang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou

Information Technology & Decision Sciences Faculty Publications

Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and …

Cramer Type Moderate Deviations For Random Fields And Mutual Information Estimation For Mixed-Pair Random Variables, Aleksandr Beknazaryan

Electronic Theses and Dissertations

In this dissertation we first study Cramer type moderate deviation for partial sums of random fields by applying the conjugate method. In 1938 Cramer published his results on large deviations of sums of i.i.d. random variables after which a lot of research has been done on establishing Cramer type moderate and large deviation theorems for different types of random variables and for various statistics. In particular results have been obtained for independent non-identically distributed random variables for the sum of independent random to estimate the mutual information between two random variables. The estimates enjoy a central limit theorem under some …

Utilizing Multi-Level Classification Techniques To Predict Adverse Drug Effects And Reactions, Victoria Puhl

Undergraduate Honors Thesis Collection

Multi-class classification models are used to predict categorical response variables with more than two possible outcomes. A collection of multi-class classification techniques such as Multinomial Logistic Regression, Na\"{i}ve Bayes, and Support Vector Machine is used in predicting patients’ drug reactions and adverse drug effects based on patients’ demographic and drug administration. The newly released 2018 data on drug reactions and adverse drug effects from U.S. Food and Drug Administration are tested with the models. The applicability of model evaluation measures such as sensitivity, specificity and prediction accuracy in multi-class settings, are also discussed.

Bayesian Hierarchical Meta-Analysis Of Asymptomatic Ebola Seroprevalence, Peter Brody-Moore

CMC Senior Theses

The continued study of asymptomatic Ebolavirus infection is necessary to develop a more complete understanding of Ebola transmission dynamics. This paper conducts a meta-analysis of eight studies that measure seroprevalence (the number of subjects that test positive for anti-Ebolavirus antibodies in their blood) in subjects with household exposure or known case-contact with Ebola, but that have shown no symptoms. In our two random effects Bayesian hierarchical models, we find estimated seroprevalences of 8.76% and 9.72%, significantly higher than the 3.3% found by a previous meta-analysis of these eight studies. We also produce a variation of this meta-analysis where we exclude …