Matching Items (12)
Filtering by
- All Subjects: Regression
- Creators: School of Mathematical and Statistical Sciences
Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The NFL is one of largest and most influential industries in the world. In America there are few companies that have a stronger hold on the American culture and create such a phenomena from year to year. In this project aimed to develop a strategy that helps an NFL team be as successful as possible by defining which positions are most important to a team's success. Data from fifteen years of NFL games was collected and information on every player in the league was analyzed. First there needed to be a benchmark which describes a team as being average and then every player in the NFL must be compared to that average. Based on properties of linear regression using ordinary least squares this project aims to define such a model that shows each position's importance. Finally, once such a model had been established then the focus turned to the NFL draft in which the goal was to find a strategy of where each position needs to be drafted so that it is most likely to give the best payoff based on the results of the regression in part one.
ContributorsBalzer, Kevin Ryan (Author) / Goegan, Brian (Thesis director) / Dassanayake, Maduranga (Committee member) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Exchange traded funds (ETFs) in many ways are similar to more traditional closed-end mutual
funds, although thee differ in a crucial way. ETFs rely on a creation and redemption feature to
achieve their functionality and this mechanism is designed to minimize the deviations that occur
between the ETF’s listed price and the net asset value of the ETF’s underlying assets. However
while this does cause ETF deviations to be generally lower than their mutual fund counterparts,
as our paper explores this process does not eliminate these deviations completely. This article
builds off an earlier paper by Engle and Sarkar (2006) that investigates these properties of
premiums (discounts) of ETFs from their fair market value. And looks to see if these premia
have changed in the last 10 years. Our paper then diverges from the original and takes a deeper
look into the standard deviations of these premia specifically.
Our findings show that over 70% of an ETFs standard deviation of premia can be
explained through a linear combination consisting of two variables: a categorical (Domestic[US],
Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds
that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market
indicators such as the economic freedom index and investment freedom index are insignificant
predictors of an ETFs standard deviation of premia. These findings differ somewhat from
existing literature which indicate that these factors should have a significant impact on the
predictive ability of an ETFs standard deviation of premia.
funds, although thee differ in a crucial way. ETFs rely on a creation and redemption feature to
achieve their functionality and this mechanism is designed to minimize the deviations that occur
between the ETF’s listed price and the net asset value of the ETF’s underlying assets. However
while this does cause ETF deviations to be generally lower than their mutual fund counterparts,
as our paper explores this process does not eliminate these deviations completely. This article
builds off an earlier paper by Engle and Sarkar (2006) that investigates these properties of
premiums (discounts) of ETFs from their fair market value. And looks to see if these premia
have changed in the last 10 years. Our paper then diverges from the original and takes a deeper
look into the standard deviations of these premia specifically.
Our findings show that over 70% of an ETFs standard deviation of premia can be
explained through a linear combination consisting of two variables: a categorical (Domestic[US],
Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds
that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market
indicators such as the economic freedom index and investment freedom index are insignificant
predictors of an ETFs standard deviation of premia. These findings differ somewhat from
existing literature which indicate that these factors should have a significant impact on the
predictive ability of an ETFs standard deviation of premia.
ContributorsHenning, Thomas Louis (Co-author) / Zhang, Jingbo (Co-author) / Simonson, Mark (Thesis director) / Wendell, Licon (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In the last decade, the population of honey bees across the globe has declined sharply leaving scientists and bee keepers to wonder why? Amongst all nations, the United States has seen some of the greatest declines in the last 10 plus years. Without a definite explanation, Colony Collapse Disorder (CCD) was coined to explain the sudden and sharp decline of the honey bee colonies that beekeepers were experiencing. Colony collapses have been rising higher compared to expected averages over the years, and during the winter season losses are even more severe than what is normally acceptable. There are some possible explanations pointing towards meteorological variables, diseases, and even pesticide usage. Despite the cause of CCD being unknown, thousands of beekeepers have reported their losses, and even numbers of infected colonies and colonies under certain stressors in the most recent years. Using the data that was reported to The United States Department of Agriculture (USDA), as well as weather data collected by The National Centers for Environmental Information (NOAA) and the National Centers for Environmental Information (NCEI), regression analysis was used to investigate honey bee colonies to find relationships between stressors in honey bee colonies and meteorological variables, and colony collapses during the winter months. The regression analysis focused on the winter season, or quarter 4 of the year, which includes the months of October, November, and December. In the model, the response variables was the percentage of colonies lost in quarter 4. Through the model, it was concluded that certain weather thresholds and the percentage increase of colonies under certain stressors were related to colony loss.
ContributorsVasquez, Henry Antony (Author) / Zheng, Yi (Thesis director) / Saffell, Erinanne (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The focus of this project is developing a business plan for faith-based counseling for college students. Renewed Living Counseling Center (RLCC) is a faith-based counseling center in the Tempe area serving Arizona State University students. RLCC strives to bring healing and wholeness to each student who comes through the doors, to empower them to realize and live out their potential, by providing them with the skills to accomplish their dreams and live full lives, through counseling, motivation, education, and treating studentʼs behaviors to become whole and successful. Research indicates that the proposed center, Renewed Living Counseling Center (RLCC), has great potential for success because:
1. Spirituality and faith are increasingly recognized as important aspects in a personʼs life. National research shows that 66% of people feel counseling should include spirituality. Research with ASU students found that students reflect this statistic, as they feel spirituality is an important part of counseling. Students also feel spirituality is appropriate to include as part of counseling services offered by centers referred to by ASU.
2. There is a need for counseling at ASU. Nationally,approximately1,100 college students commit suicide each year. At ASU, almost one-third of students reported feeling so depressed that it is difficult to function, and 0.9% report having attempted suicide within the past year.
3. Surveys of ASU students indicate that students who describe themselves as being religious are more desirous that counseling include a spiritual dimension. Surveys of campus pastors indicate that over 80% believe there is a need for faith-based counseling and would refer students to a local center.
4. Price is an issue. Indeed, a survey of campus pastors indicated that they believed cost of counseling to be one of the primary deterrents to students seeking help. One way to control costs is to use a mixture of residents and licensed counselors. As in medicine, students must complete coursework along with a period of residency or internship to obtain licensing. Both religious and secular masters programs in counseling exist in the greater Phoenix area. Thus, there is a potential supply of students who could work as residents, permitting RLCC to offer counseling services at reasonable prices.
1. Spirituality and faith are increasingly recognized as important aspects in a personʼs life. National research shows that 66% of people feel counseling should include spirituality. Research with ASU students found that students reflect this statistic, as they feel spirituality is an important part of counseling. Students also feel spirituality is appropriate to include as part of counseling services offered by centers referred to by ASU.
2. There is a need for counseling at ASU. Nationally,approximately1,100 college students commit suicide each year. At ASU, almost one-third of students reported feeling so depressed that it is difficult to function, and 0.9% report having attempted suicide within the past year.
3. Surveys of ASU students indicate that students who describe themselves as being religious are more desirous that counseling include a spiritual dimension. Surveys of campus pastors indicate that over 80% believe there is a need for faith-based counseling and would refer students to a local center.
4. Price is an issue. Indeed, a survey of campus pastors indicated that they believed cost of counseling to be one of the primary deterrents to students seeking help. One way to control costs is to use a mixture of residents and licensed counselors. As in medicine, students must complete coursework along with a period of residency or internship to obtain licensing. Both religious and secular masters programs in counseling exist in the greater Phoenix area. Thus, there is a potential supply of students who could work as residents, permitting RLCC to offer counseling services at reasonable prices.
ContributorsMatthews, Rachel Leigh (Author) / Steinbart, Paul (Thesis director) / Chung, Sally (Committee member) / Sanders, Ben (Committee member) / Barrett, The Honors College (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor)
Created2014-05
Description
This paper will begin by initially discussing the potential uses and challenges of efficient and accurate traffic forecasting. The data we used includes traffic volume from seven locations on a busy Athens street in April and May of 2000. This data was used as part of a traffic forecasting competition. Our initial observation, was that due to the volatility and oscillating nature of daily traffic volume, simple linear regression models will not perform well in predicting the time-series data. For this we present the Harmonic Time Series model. Such model (assuming all predictors are significant) will include a sinusoidal term for each time index within a period of data. Our assumption is that traffic volumes have a period of one week (which is evidenced by the graphs reproduced in our paper). This leads to a model that has 6,720 sine and cosine terms. This is clearly too many coefficients, so in an effort to avoid over-fitting and having an efficient model, we apply the sub-setting algorithm known as Adaptive Lass.
ContributorsMora, Juan (Author) / Kamarianakis, Ioannis (Thesis director) / Yu, Wanchunzi (Committee member) / W. P. Carey School of Business (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This paper attempts to introduce analytics and regression techniques into the National Hockey League. Hockey as a sport has been a slow adapter of analytics, and this can be attributed to poor data collection methods. Using data collected for hockeyreference.com, and R statistical software, the number of wins a team experiences will be predicted using Goals For and Goals Against statistics from 2005-2017. The model showed statistical significance and strong normality throughout the data. The number of wins each team was expected to experience in 2016-2017 was predicted using the model and then compared to the actual number of games each team won. To further analyze the validity of the model, the expected playoff outcome for 2016-2017 was compared to the observed playoff outcome. The discussion focused on team's that did not fit the model or traditional analytics and expected forecasts. The possible discrepancies were analyzed using the Las Vegas Golden Knights as a case study. Possible next steps for data analysis are presented and the role of future technology and innovation in hockey analytics is discussed and predicted.
ContributorsVermeer, Brandon Elliot (Author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
College and university students are heavily influenced by their exposure to opportunities, individuals, and belief-systems during their time in school. More specifically, countless students are impacted by campus Christian ministries. There are 67 registered religious clubs and organizations across Arizona State University's four campuses, and 46 of them identify as Christian. Similar to most faith-based organizations, Christian campus ministries seek to impact the lives of students. This study will take a look at the influence of these ministries at ASU by researching their intersection with another key component of university life: wellness.
The primary research question is, “How does involvement in Christian ministries at ASU relate to the wellness of students?” The study will examine multiple dimensions of wellness: occupational, physical, social, intellectual, spiritual, and emotional. Each component is essential to understanding the health and well-being of an individual, which is why this study will measure wellness levels in each dimension among samples of students at ASU.
The methodology chosen was a short, anonymous survey that 148 ASU students participated in—73 involved in Christian ministries at ASU and 75 not involved. The quantitative component included a wellness assessment using questions from The National Wellness Institute. These wellness scale questions were broken up into 5 randomized sections, each with one question per dimension, for 30 questions total. Each question response was assigned a rating on a 1 to 5 scale, 1 associated with low wellness and 5 high wellness. The qualitative component, comprised of short answer questions, only applied to students who were involved in a Christian ministry. This portion allowed respondents to explain if and how the ministry impacts each dimension of wellness uniquely.
The quantitative results showed some evident differences between students involved in Christian ministries and students not involved. The social and spiritual dimensions concluded much higher levels of wellness for involved students, both statistically significant with p-values of 0.028 and 0.004. Although some of the wellness differences between involved and not involved participants were not statistically significant, there is also notable variation among questions within each dimension. For the qualitative data, most students in Christian ministries said they believe their involvement increases their wellness in all six dimensions. For each dimension, over 75% of participants said that the ministry impacted their well-being. For the social, spiritual, and emotional dimensions, at least 97% of respondents said their ministry involvement impacted their wellness.
In examining the conclusions of the study, one recommendations is to strengthen the partnership between the greater ASU community and Christian ministries by collaborating and combining resources for programming that relates to their common goals and shared values. Additionally, other faith-based organizations at ASU may benefit from replicating this study to observe their unique wellness impact.
The primary research question is, “How does involvement in Christian ministries at ASU relate to the wellness of students?” The study will examine multiple dimensions of wellness: occupational, physical, social, intellectual, spiritual, and emotional. Each component is essential to understanding the health and well-being of an individual, which is why this study will measure wellness levels in each dimension among samples of students at ASU.
The methodology chosen was a short, anonymous survey that 148 ASU students participated in—73 involved in Christian ministries at ASU and 75 not involved. The quantitative component included a wellness assessment using questions from The National Wellness Institute. These wellness scale questions were broken up into 5 randomized sections, each with one question per dimension, for 30 questions total. Each question response was assigned a rating on a 1 to 5 scale, 1 associated with low wellness and 5 high wellness. The qualitative component, comprised of short answer questions, only applied to students who were involved in a Christian ministry. This portion allowed respondents to explain if and how the ministry impacts each dimension of wellness uniquely.
The quantitative results showed some evident differences between students involved in Christian ministries and students not involved. The social and spiritual dimensions concluded much higher levels of wellness for involved students, both statistically significant with p-values of 0.028 and 0.004. Although some of the wellness differences between involved and not involved participants were not statistically significant, there is also notable variation among questions within each dimension. For the qualitative data, most students in Christian ministries said they believe their involvement increases their wellness in all six dimensions. For each dimension, over 75% of participants said that the ministry impacted their well-being. For the social, spiritual, and emotional dimensions, at least 97% of respondents said their ministry involvement impacted their wellness.
In examining the conclusions of the study, one recommendations is to strengthen the partnership between the greater ASU community and Christian ministries by collaborating and combining resources for programming that relates to their common goals and shared values. Additionally, other faith-based organizations at ASU may benefit from replicating this study to observe their unique wellness impact.
ContributorsSouza, Ann Christina (Author) / Golden, Amy (Thesis director) / Valiente, Carlos (Committee member) / Department of English (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In collaboration with Moog Broad Reach and Arizona State University, a<br/>team of five undergraduate students designed a hardware design solution for<br/>protecting flash memory data in a spaced-based radioactive environment. Team<br/>Aegis have been working on the research, design, and implementation of a<br/>Verilog- and Python-based error correction code using a Reed-Solomon method<br/>to identify bit changes of error code. For an additional senior design project, a<br/>Python code was implemented that runs statistical analysis to identify whether<br/>the error correction code is more effective than a triple-redundancy check as well<br/>as determining if the presence of errors can be modeled by a regression model.
ContributorsSalls, Demetra Helen (Author) / Kozicki, Michael (Thesis director) / Hodge, Chris (Committee member) / Electrical Engineering Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This project uses SAS (Statistical Analysis Software) to create a regression model that provides a prediction for which NFL playoff team will win the Super Bowl in a given year.
ContributorsOleksyn, Alexander (Author) / Schneider, Laurence (Thesis director) / Hansen, Whitney (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2023-05