Matching Items (14)
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- All Subjects: Regression
- Creators: School of Mathematical and Statistical Sciences
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
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
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
This paper explores how marginalist economics defines and inevitably constrains Victorian sensation fiction's content and composition. I argue that economic intuition implies that sensationalist heroes and antagonists, writers and readers all pursued a fundamental, "rational" aim: the attainment of pleasure. So although "sensationalism" took on connotations of moral impropriety in the Victorian age, sensation fiction primarily involves experiences of pain on the page that excite the reader's pleasure. As such, sensationalism as a whole can be seen as a conformist product, one which mirrors the effects of all commodities on the market, rather than as a rebellious one. Indeed, contrary to modern and contemporary critics' assumptions, sensation fiction may not be as scandalous as it seems.
ContributorsFischer, Brett Andrew (Author) / Bivona, Daniel (Thesis director) / Looser, Devoney (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / School of Politics and Global Studies (Contributor) / Department of English (Contributor)
Created2014-12
DescriptionDiscusses the reading experience and writing strategies in relation to four prominent novels from the genre
ContributorsO'Malley, Erik Andrew (Author) / Cook, Paul (Thesis director) / Mallot, Edward (Committee member) / Broglio, Ronald (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of English (Contributor)
Created2013-05
ContributorsPanosian, N. Zari (Author) / Ison, Tara (Thesis director) / Fortunato, Joe (Committee member) / Talerico, Daniela (Committee member) / Barrett, The Honors College (Contributor) / Department of English (Contributor)
Created2013-05
Description
Historically, per capita water demand has tended to increase proportionately with population growth. However, the last two decades have exhibited a different trend; per capita water usage is declining despite a growing economy and population. Subsequently, city planners and water suppliers have been struggling to understand this new trend and whether it will continue over the coming years. This leads to inefficient water management practices as well as flawed water storage design, both of which have adverse impacts on the economy and environment. Water usage data, provided by the city of Santa Monica, was analyzed using a combination of hydro-climatic and demographic variables to dissect these trends and variation in usage. The data proved to be tremendously difficult to work with; several values were missing or erroneously reported, and additional variables had to be brought from external sources to help explain the variation. Upon completion of the data processing, several statistical techniques including regression and clustering models were built to identify potential correlations and understand the consumers’ behavior. The regression models highlighted temperature and precipitation as significant stimuli of water usage, while the cluster models emphasized high volume consumers and their respective demographic traits. However, the overall model accuracy and fit was very poor for the models due to the inadequate quality of data collection and management. The imprecise measurement process for recording water usage along with varying levels of granularity across the different variables prevented the models from revealing meaningful associations. Moving forward, smart meter technology needs to be considered as it accurately captures real-time water usage and transmits the information to data hubs which then implement predictive analytics to provide updated trends. This efficient system will allow cities across the nation to stay abreast of future water usage developments and conserve time, resources, and the environment.
ContributorsPendyala, Kiran Vinaysai (Author) / Garcia, Margaret (Thesis director) / Stufken, John (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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
Often when considering John Milton's greatest work, Paradise Lost, the general public operates under a number of assumptions which are patently false. One of these assumptions is that Milton was an orthodox Christian when writing Paradise Lost and Paradise Regained. This thesis sheds light on the issue by examining his personal beliefs about the trinity in De Doctrina Christiana, defending the use of the treatise in analyzing the poems, and explaining how Milton uses veiled language in order to hide his heterodox beliefs. I contend that deriving an antitrinitarian mode of thought from De Doctrina Christiana and reading the poem with this antitrinitarian belief, the cognitive frame of reference in which Milton was when writing both poems, in mind is a more consistent reading of Paradise Lost and Paradise Regained than reading both texts with a traditional, orthodox, Christian perspective. Examining a variety of selections from Paradise Lost and Paradise Regained, I demonstrate the powerful nature of the antitrintarian reading of Milton. Many passages, for example the invocation in the opening lines of Book III of Paradise Lost, become much clearer with an antitrinitarian reading. Reading the texts with an antitrinitarian view reduces ambiguity in the text and clarifies a number of passages and details which, from a Trinitarian view, are left completely unanswered in Paradise Lost. In addition to clarifying confusing passages, an antitrinitarian reading demonstrates Milton's masterful use of 17th century English Protestantism "buzz-words" to mask his true beliefs without compromising his personal religious convictions about the second member of the Christian Triune Godhead.
ContributorsGould, George Andrew (Author) / David, Hawkes (Thesis director) / Sturges, Robert (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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