Categorical Data Analysis Commons^™

Predicting Game Day Outcomes In National Football League Games, Josh Klein, Anna Frowein, Chris Irwin

SMU Data Science Review

In this paper, we present a model for predicting the game day outcomes of National Football League games. 3 of the most popular sources for game day predictions are analyzed for comparison. Player data and outcomes from previous games are used, but we also incorporate several weather factors into our models. Over 1,700 games were incorporated and 3 separate models are created using simple regression, principal component analysis, and a recursive model. We also discuss the ethicality of using data science techniques by individuals with the knowledge in order to gain an advantage over a population lacking this specialized training.

Examining Multimorbidities Using Association Rule Learning, Kaylee Dudley

Undergraduate Honors Theses

All insurance companies, regardless of the kind of insurance they offer, do their best to predict the future by comparing current to historical information. Any statistically significant correlation, regardless of expectations and hidden factors, can help to actuarially model future behavior. Using deidentified data from over 6 million health insurance policies over one year, we looked for any significant groupings of medical issues. The medical issues are defined based on the commercial “Episode Treatment Groups” (ETGs) classification, and our claims contain 347 different ETGs. We performed different kinds of analysis, including Bayesian posterior cluster analysis, k-means cluster analysis, and association …

Resistance To Peer Influence Moderates The Relationship Between Perceived (But Not Actual) Peer Norms And Binge Drinking In A College Student Social Network, Graham T. Diguiseppi, Matthew K. Meisel, Sara G. Balestrieri, Miles Q. Ott, Melissa J. Cox, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Introduction: Adolescent and young adult binge drinking is strongly associated with perceived social norms and the drinking behavior that occurs within peer networks. The extent to which an individual is influenced by the behavior of others may depend upon that individual’s resistance to peer influence (RPI).

Methods: Students in their first semester of college (N = 1323; 54.7% female, 57% White, 15.1% Hispanic) reported on their own binge drinking, and the perceived binge drinking of up to 10 important peers in the first-year class. Using network autocorrelation models, we investigated cross-sectional relationships between participant’s binge drinking frequency and the perceived …

Under The Influence, Leonardo Cavicchio

Honors Projects in Mathematics

The purpose of this Honors Capstone entitled Under the Influence is to assess the validity of claims concerning the possible influence of roommates on one another, concerning alcohol on college campuses. This will be done by examining data collected in a prior study conducted over a two-year period. This analysis will focus on how alcohol consumption changes in correlation with the personality factors of roommates over an extended period of time. This secondary analysis of de-identified data will focus on primary and secondary subquestions. The primary question that will be addressed with the data set collected from the University of …

A Convolutional Neural Network Model For Species Classification Of Camera Trap Images, Annie Casey

Mathematics Undergraduate Theses

The overall purpose of this study was to automate the manual process of tagging species found in camera trap images using machine learning. The basic design of this study was to implement a Convolutional Neural Network model in Python using the Keras and Tensorflow modules that learn to recognize patterns in images in order to classify what species is in a given image and to label it accordingly. Results of the analysis highlight the importance of a large sample size, the degree of accuracy according to various arguments in the model, effectiveness of multiple layers that include Max Pooling, and …

An Event- And Network-Level Analysis Of College Students’ Maximum Drinking Day, Matthew K. Meisel, Angelo M. Dibello, Sara G. Balestrieri, Miles Q. Ott, Graham T. Diguiseppi, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Background—Heavy episodic drinking is common among college students and remains a serious public health issue. Previous event-level research among college students has examined behaviors and individual-level characteristics that drive consumption and related consequences but often ignores the social network of people with whom these heavy drinking episodes occur. The main aim of the current study was to investigate the network of social connections between drinkers on their heaviest drinking occasions.

Methods—Sociocentric network methods were used to collect information from individuals in the first-year class (N=1342) at one university. Past-month drinkers (N=972) reported on the characteristics of their heaviest drinking occasion …

Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems

Computer Science and Engineering Theses and Dissertations

Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …

Satellite Communications In The V And W Band: Tropospheric Effects, Bertus A. Shelters

Theses and Dissertations

An investigation into the use of Weather Cubes compiled by the atmospheric characterization package, Laser Environmental Effects Definition and Reference (LEEDR), to develop accurate, long-term attenuation statistics for link-budget analysis is presented. A Weather Cube is a three-dimensional mesh of numerical weather prediction (NWP) data plus LEEDR calculations that allows for the quantification of rain, cloud, aerosol, and molecular effects at any UV to RF wavelength on any path contained within the cube. The development of this methodology is motivated by the potential use of V (40-75 GHz) and W (75-110 GHz) band frequencies for the satellite communication application, as …

Default Priors For The Intercept Parameter In Logistic Regressions, Philip S. Boonstra, Ryan P. Barbaro, Ananda Sen

The University of Michigan Department of Biostatistics Working Paper Series

In logistic regression, separation refers to the situation in which a linear combination of predictors perfectly discriminates the binary outcome. Because finite-valued maximum likelihood parameter estimates do not exist under separation, Bayesian regressions with informative shrinkage of the regression coefficients offer a suitable alternative. Little focus has been given on whether and how to shrink the intercept parameter. Based upon classical studies of separation, we argue that efficiency in estimating regression coefficients may vary with the intercept prior. We adapt alternative prior distributions for the intercept that downweight implausibly extreme regions of the parameter space rendering less sensitivity to separation. …

Building A Better Risk Prevention Model, Steven Hornyak

National Youth Advocacy & Resilience Conference

This presentation chronicles the work of Houston County Schools in developing a risk prevention model built on more than ten years of longitudinal student data. In its second year of implementation, Houston At-Risk Profiles (HARP), has proven effective in identifying those students most in need of support and linking them to interventions and supports that lead to improved outcomes and significantly reduces the risk of failure.

Comparing Various Machine Learning Statistical Methods Using Variable Differentials To Predict College Basketball, Nicholas Bennett

Williams Honors College, Honors Research Projects

The purpose of this Senior Honors Project is to research, study, and demonstrate newfound knowledge of various machine learning statistical techniques that are not covered in the University of Akron’s statistics major curriculum. This report will be an overview of three machine-learning methods that were used to predict NCAA Basketball results, specifically, the March Madness tournament. The variables used for these methods, models, and tests will include numerous variables kept throughout the season for each team, along with a couple variables that are used by the selection committee when tournament teams are being picked. The end goal is to find …

Campus Climate Sexual Assault Survey (2015) Analysis, Felicia Rosin

Williams Honors College, Honors Research Projects

The issue of sexual assault has garnered widespread attention in recent years, as is evident by the growing number of high-profile cases and mainstream social movements. With this increasingly bright spotlight, it is no surprise that The University of Akron has interest in improving the sexual violence education programs offered to students. In 2015, the university conducted a survey to gather information on the campus climate surrounding sexual assault. This analysis dives into a deeper analysis of the data gathered in an attempt to pinpoint areas that require the university’s attention. The analysis covers topics identified by Dean of Students …

Penalized Mixed-Effects Ordinal Response Models For High-Dimensional Genomic Data In Twins And Families, Amanda E. Gentry

Theses and Dissertations

The Brisbane Longitudinal Twin Study (BLTS) was being conducted in Australia and was funded by the US National Institute on Drug Abuse (NIDA). Adolescent twins were sampled as a part of this study and surveyed about their substance use as part of the Pathways to Cannabis Use, Abuse and Dependence project. The methods developed in this dissertation were designed for the purpose of analyzing a subset of the Pathways data that includes demographics, cannabis use metrics, personality measures, and imputed genotypes (SNPs) for 493 complete twin pairs (986 subjects.) The primary goal was to determine what combination of SNPs and …

Examining The Issue Of Compliance With Personal Protective Equipment Among Wastewater Workers Across The Southeast Region Of The United States, Tamara L. Wright

College of Graduate Studies: Theses & Dissertations

Wastewater workers are exposed to different occupational hazards such as chemicals, gases, viruses, and bacteria. Personal Protective Equipment (PPE) is a significant factor that can reduce or increase the probability of an accident from hazardous exposures to chemicals and microbial contaminants. The purpose of this study was to identify wastewater worker’s beliefs and practices on wearing PPE and protections offered by PPE through the integration of the Health Belief Model (HBM). Participants were workers in the wastewater industry, which included wastewater operators, laboratory analysts, maintenance workers, wastewater collection workers, equipment operators, managers, and supervisors (n=272). The instrument was a self-administered …

Using Data Analytics For Discovering Library Resource Insights: Case From Singapore Management University, Ning Lu, Rui Song, Dina Li Gwek Heng, Swapna Gottipati, Aaron Tay

Research Collection School Of Computing and Information Systems

Library resources are critical in supporting teaching, research and learning processes. Several universities have employed online platforms and infrastructure for enabling the online services to students, faculty and staff. To provide efficient services by understanding and predicting user needs libraries are looking into the area of data analytics. Library analytics in Singapore Management University is the project committed to provide an interface for data-intensive project collaboration, while supporting one of the library’s key pillars on its commitment to collaborate on initiatives with SMU Communities and external groups. In this paper, we study the transaction logs for user behavior analysis that …

Data Envelopment Analysis Using Glpkapi In R, Konrad Miziolek, Jordan Beary, Shreyas Vasanth, Surekha Chanamolu, Rudraxi Mitra

Engineering and Technology Management Student Projects

The work done here is primarily a wrapper function written to separate some of the more difficult-to-use glpkAPI functionality from the end-user. The user, when prompted, selects the appropriate configuration of the .mod file to the task (for example, output-oriented CRS), and the data file, as a .dat. The function then loads the required glpkAPI library, and carries forward the model. It allocates the problem and workspace, reads the model file and data file the user selects, builds the problem, and solves it. The function returns primal values, and, if dual = TRUE is selected, also returns dual weights.

How Singapore Investors Can Profit From Unstructured Data, Clarence Goh

Research Collection School Of Accountancy

Data that is collected in the business environment can be structured or unstructured. In general, structured data refers to information which is highly organised and which can easily be stored in rows and columns within database systems. On the other hand, unstructured data does not have a strict data structure, and is also not organised in a pre-defined manner.

Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel

The Summer Undergraduate Research Fellowship (SURF) Symposium

Urbanization increases runoff by changing land use types from less impervious to impervious covers. Improving the accuracy of a runoff assessment model, the Long-Term Hydrologic Impact Assessment (L-THIA) Model, can help us to better evaluate the potential uses of Low Impact Development (LID) practices aimed at reducing runoff, as well as to identify appropriate runoff and water quality mitigation methods. Several versions of the model have been built over time, and inconsistencies have been introduced between the models. To improve the accuracy and consistency of the model, the equations and parameters (primarily curve numbers in the case of this model) …

Integrating Apache Spark And R For Big Data Analytics On Solving Geographic Problems, Mengqi Zhang, Tin Seong Kam

Research Collection School Of Computing and Information Systems

With the advent ofdigital technology and smart devices, a flood of digital data is beinggenerated every day. This huge amount of data not only records the historyactivities but also provides future valuable information for organizations andbusinesses. However, the true values of these data will not be fullyappreciated until they have been processed, analyzed and the analysis resultsbeen communicated to decision makers in a business friendly manner.In view of thisneed, big data has been one of the major research focus in the academicresearch community especially in the field of computer science and the softwarevendor as well as the big data service …

Visualizing Lab And Phenotype Associations Using Phewas And Electronic Health Records, Brenda Emerson, Miriam Goldman, Sahiti Kolli

Honors Projects

As the digitization of patient health records is becoming more common, we are given a great opportunity to analyze these records and hopefully make discoveries about diseases or medicines. Being given large datasets of Electronic Health Records, I and two other students decided to look for novel phenotype associations with mean lab values, look to see whether the presence of a lab had associations with a phenotype, and create an interactive application to visual the associations between labs and phenotypes.