Categorical Data Analysis Commons^™

Kadafrica: Survey Analysis To Support Research For Smallholder Farmers, Gregory Asamoah, Robert Gill, Frank Sclafani, Bivin Sadler

SMU Data Science Review

In this paper, we present an analysis of survey data with the goal of determining if the KadAfrica training program, a social organization in Uganda, has a significant effect on the lives of the girls who participate in the program. This is done through an observational study of girl’s responses to several pre-program and post-program questions. These questions include topics such as the girl’s access to hygiene materials and their personal views on family finances. In addition to providing an analysis of historical data, we established a data platform in which future data can be stored and analyzed in an …

Machine Learning Vs Conventional Analysis Techniques For The Earth’S Magnetic Field Study, Sheri Loftin, Sarah J. Fite, Laura V. Bishop, Stavros Kotsiaros

SMU Data Science Review

Abstract. Current techniques for calculating and generating models used for analyzing the Earth’s magnetic field are laborious and time-consuming. We assert that machine learning can have a significant impact on building magnetic field models more quickly and on various levels of complexity, specifically as it pertains to data cleansing and sorting. Our approach to this problem uses a reverse iterative multi-phase process for data cleansing, in which, initially, the CHAOS-6 model data is examined to determine if machine learning can be used to differentiate between useful data components for spherical harmonics, versus data noise. During this phase, six different machine …

The Housing Bubble, Shelby Brown

Mathematics Senior Capstone Papers

The housing market is constantly changing. Fluctuating housing prices and a flooded market have home buyers hesitant to commit and sellers on edge. What if the prospective buyer or seller could take this financial step already knowing the state of the market? The purpose of this project is to attempt to predict the next housing bubble. A multivariable regression analysis is conducted using relevant data including variables such as average property prices, number of foreclosures, etc. in the United States beginning in the year 2009. The trends, patterns, and models created from the regression analysis are compared against data models …

Angry Birds Fly High Again With Data Analytics, Singapore Management University

Perspectives@SMU

User feedback has transformed Rovio’s culture and game design

The Evolution Of Data Science: A New Mode Of Knowledge Production, Jennifer Lewis Priestley, Robert J. Mcgrath

Faculty Articles

Is data science a new field of study or simply an extension or specialization of a discipline that already exists, such as statistics, computer science, or mathematics? This article explores the evolution of data science as a potentially new academic discipline, which has evolved as a function of new problem sets that established disciplines have been ill-prepared to address. The authors find that this newly-evolved discipline can be viewed through the lens of a new mode of knowledge production and is characterized by transdisciplinarity collaboration with the private sector and increased accountability. Lessons from this evolution can inform knowledge production …

Newsworthy Migrants: Sentiment And Text Analysis Of Dutch Newspapers, Nicholas J. Schmitz

Independent Study Project (ISP) Collection

Understanding how a migrant population is viewed and displayed by the host country has been a struggle for a long period of time for anyone studying migration. Traditional methods of collecting information were tedious, time intensive and expensive. However, Big data has been providing unique solutions to gaps in information in many different fields across the world. Media plays an important part in developing and assessing the public opinion on a topic. With the availability of a large number of online articles from historical time periods, it is possible to use quantitative analysis, such as text analytics, to can see …

Enhancing Smes’ Data Analytics Capability Through University Tie-Ups, Gary Pan, Poh Sun Seow, Benjamin Huan Zhou Lee

Research Collection School Of Accountancy

Harnessing the power of data analytics, SMEs can now generate visualisations of the company's historical data to date, and predictions for the future - something which is nearly impossible before the era of big data.

Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson

UNO Student Research and Creative Activity Fair

Coxsackievirus B3 (CVB3) is a cardiovirulent enterovirus from the family Picornaviridae. The RNA genome houses an internal ribosome entry site (IRES) in the 5’ untranslated region (5’UTR) that enables cap-independent translation. Ample evidence suggests that the structure of the 5’UTR is a critical element for virulence. We probe RNA structure in solution using base-specific modifying agents such as dimethyl sulfate as well as backbone targeting agents such as N-methylisatoic anhydride used in Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension (SHAPE). We have developed a pipeline that merges and evaluates base-specific and SHAPE data together with statistical analyses that provides confidence …

Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.

SDSU Data Science Symposium

Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component …

An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine

SMU Data Science Review

In this paper, we present an evaluation of training size impact on validation accuracy for an optimized Convolutional Neural Network (CNN). CNNs are currently the state-of-the-art architecture for object classification tasks. We used Amazon’s machine learning ecosystem to train and test 648 models to find the optimal hyperparameters with which to apply a CNN towards the Fashion-MNIST (Mixed National Institute of Standards and Technology) dataset. We were able to realize a validation accuracy of 90% by using only 40% of the original data. We found that hidden layers appear to have had zero impact on validation accuracy, whereas the neural …

Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran

SMU Data Science Review

In this paper, we present a performance comparison of machine learning algorithms executed on traditional and quantum computers. Quantum computing has potential of achieving incredible results for certain types of problems, and we explore if it can be applied to machine learning. First, we identified quantum machine learning algorithms with reproducible code and had classical machine learning counterparts. Then, we found relevant data sets with which we tested the comparable quantum and classical machine learning algorithm's performance. We evaluated performance with algorithm execution time and accuracy. We found that quantum variational support vector machines in some cases had higher accuracy …

Comparative Analysis Of Students’ Performance Between Online And On Campus In An Introductory Statistics Course, Kendal Mcdonald

The Corinthian

In this research, we compare students’ performance in an online and on-campus introductory statistics and probability course at Georgia College. MyStatLab is the learning management system used in both the online and on-campus courses for homework and quizzes. The online data is produced by five summer courses between Summer 2014 to Summer 2017 and the on-campus data is produced from nine on-campus courses from Spring 2014, Spring 2016, and Spring 2017. For homework, the research compares the scores made between online and on-campus. For quizzes, we test if there is a difference between the scores and the number of attempts …

Using Neural Networks To Classify Discrete Circular Probability Distributions, Madelyn Gaumer

HMC Senior Theses

Given the rise in the application of neural networks to all sorts of interesting problems, it seems natural to apply them to statistical tests. This senior thesis studies whether neural networks built to classify discrete circular probability distributions can outperform a class of well-known statistical tests for uniformity for discrete circular data that includes the Rayleigh Test¹, the Watson Test², and the Ajne Test³. Each neural network used is relatively small with no more than 3 layers: an input layer taking in discrete data sets on a circle, a hidden layer, and an output …

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: …

Time Series Forecasting And Analysis: A Study Of American Clothing Retail Sales Data, Weijun Huang

Honors Undergraduate Theses

This paper serves to address the effect of time on the sales of clothing retail, from 2010 to May 2019. The data was retrieved from the US Census, where N=113 observations were used, which were plotted to observe their trends. Once outliers and transformations were performed, the best model was fit, and diagnostic review occurred. Inspections for seasonality and forecasting was also conducted. The final model came out to be an ARIMA (2,0,1). Slight seasonality was present, but not enough to drastically influence the trends. Our results serve to highlight the economic growth of clothing retail sales for the past …

Fixed Choice Design And Augmented Fixed Choice Design For Network Data With Missing Observations, Miles Q. Ott, Matthew T. Harrison, Krista J. Gile, Nancy P. Barnett, Joseph W. Hogan

Statistical and Data Sciences: Faculty Publications

The statistical analysis of social networks is increasingly used to understand social processes and patterns. The association between social relationships and individual behaviors is of particular interest to sociologists, psychologists, and public health researchers. Several recent network studies make use of the fixed choice design (FCD), which induces missing edges in the network data. Because of the complex dependence structure inherent in networks, missing data can pose very difficult problems for valid statistical inference. In this article, we introduce novel methods for accounting for the FCD censoring and introduce a new survey design, which we call the augmented fixed choice …

Biodiversity And Distribution Of Benthic Foraminifera In Harrington Sound, Bermuda: The Effects Of Physical And Geochemical Factors On Dominant Taxa, Nam Le

Honors Theses

Harrington Sound, Bermuda, is a nearly enclosed lagoon acting as a subtropical/tropical, carbonate-rich basin in which carbonate sediments, reef patches, and carbonate-producing organisms accumulate. Here, one of the most important calcareous groups is the Foraminifera. Analyses of common benthic orders, including miliolids (Quinqueloculina and Triloculina spp.) and rotaliids (Homotrema rubrum, Elphidium spp., and Ammonia beccarii), are essential in understanding past and present environmental conditions affecting the island's coastal environment. These taxa have been studied previously; however, factors explaining their individual patterns of abundance in the Sound are not well detailed. The goal of this study is …

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 …

Improving Access To Clean Water In Rural Ecuador: The Connection Between Willingness To Pay And Population Health, Micalea Leaska

Capstone Collection

Climate change is affecting social and environmental determinants of health through access to safe drinking water, safely managed sanitation systems, and access to health care services and the ability for individuals to break free from unsuitable circumstances. Ecological disturbances such as those caused by climate change can cause a shift in host vectors or a change in habitat that results in a greater likelihood of the pathogen coming in contact with humans. Water, sanitation, and hygiene (WASH) services and their accessibility to populations can directly impact a community’s vulnerability to diseases and limiting factors to increase economic growth. If rural …

A New Independence Measure And Its Applications In High Dimensional Data Analysis, Chenlu Ke

Theses and Dissertations--Statistics

This dissertation has three consecutive topics. First, we propose a novel class of independence measures for testing independence between two random vectors based on the discrepancy between the conditional and the marginal characteristic functions. If one of the variables is categorical, our asymmetric index extends the typical ANOVA to a kernel ANOVA that can test a more general hypothesis of equal distributions among groups. The index is also applicable when both variables are continuous. Second, we develop a sufficient variable selection procedure based on the new measure in a large p small n setting. Our approach incorporates marginal information between …