Matching Items (17)
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- All Subjects: Regression
- Creators: School of Mathematical and Statistical Sciences
Description
This study estimates the capitalization effect of golf courses in Maricopa County using the hedonic pricing method. It draws upon a dataset of 574,989 residential transactions from 2000 to 2006 to examine how the aesthetic, non-golf benefits of golf courses capitalize across a gradient of proximity measures. The measures for amenity value extend beyond home adjacency and include considerations for homes within a range of discrete walkability buffers of golf courses. The models also distinguish between public and private golf courses as a proxy for the level of golf course access perceived by non-golfers. Unobserved spatial characteristics of the neighborhoods around golf courses are controlled for by increasing the extent of spatial fixed effects from city, to census tract, and finally to 2000 meter golf course ‘neighborhoods.’ The estimation results support two primary conclusions. First, golf course proximity is found to be highly valued for adjacent homes and homes up to 50 meters way from a course, still evident but minimal between 50 and 150 meters, and insignificant at all other distance ranges. Second, private golf courses do not command a higher proximity premia compared to public courses with the exception of homes within 25 to 50 meters of a course, indicating that the non-golf benefits of courses capitalize similarly, regardless of course type. The results of this study motivate further investigation into golf course features that signal access or add value to homes in the range of capitalization, particularly for near-adjacent homes between 50 and 150 meters thought previously not to capitalize.
ContributorsJoiner, Emily (Author) / Abbott, Joshua (Thesis director) / Smith, Kerry (Committee member) / Economics Program in CLAS (Contributor) / School of Sustainability (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
Mathematics is an increasingly critical subject and the achievement of students in mathematics has been the focus of many recent reports and studies. However, few studies exist that both observe and discuss the specific teaching and assessment techniques employed in the classrooms across multiple countries. The focus of this study is to look at classrooms and educators across six high achieving countries to identify and compare teaching strategies being used. In Finland, Hong Kong, Japan, New Zealand, Singapore, and Switzerland, twenty educators were interviewed and fourteen educators were observed teaching. Themes were first identified by comparing individual teacher responses within each country. These themes were then grouped together across countries and eight emerging patterns were identified. These strategies include students active involvement in the classroom, students given written feedback on assessments, students involvement in thoughtful discussion about mathematical concepts, students solving and explaining mathematics problems at the board, students exploring mathematical concepts either before or after being taught the material, students engagement in practical applications, students making connections between concepts, and students having confidence in their ability to understand mathematics. The strategies identified across these six high achieving countries can inform educators in their efforts of increasing student understanding of mathematical concepts and lead to an improvement in mathematics performance.
ContributorsAnglin, Julia Mae (Author) / Middleton, James (Thesis director) / Vicich, James (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-12
Description
According to the Tax Policy Center, a joint project of the Brookings Institution and Urban Institute, the Earned Income Tax Credit (EITC) will provide 26 million households with 60 billion dollars of reduced taxes and refunds in 2015 \u2014 resources that serve to lift millions of families above the federal poverty line. Responding to the popularity of EITC programs and recent discussion of its expansion for childless adults, I select three comparative case studies of state-level EITC reform from 2005 to 2013. Each state represents a different kind of policy reform: the creation of a supplemental credit in Connecticut, credit reduction in New Jersey, and finally credit expansion for childless adults in Maryland. For each case study, I use Current Population Survey panel data from the March Supplement to complete a differences-in-differences (DD) analysis of EITC policy changes. Specifically, I analyze effects of policy reform on total earned income, employment and usual hours worked. For comparison groups, I construct unique counterfactual populations of northeastern U.S. states, using people of color with less than a college degree as my treatment group for their increased sensitivity to EITC policy reform. I find no statistically significant effects of policy creation in Connecticut, significant decreases in employment and hours worked in New Jersey, and finally, significant increases in earnings and hours worked in Maryland. My work supports the findings of other empirical work, suggesting that awareness of new supplemental EITC programs is critical to their effectiveness while demonstrating that these types of programs can affect the labor supply and outcomes of eligible groups.
ContributorsRichard, Katherine Rose (Author) / Dillon, Eleanor Wiske (Thesis director) / Silverman, Daniel (Committee member) / Herbst, Chris (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (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
Did the amount of media attention to the H1N1 flu or the information that the Centers for Disease Control (CDC) disseminates about the H1N1 flu, influence individuals' decisions to avoid public locations during the 2009-2010 H1N1 Influenza pandemic? I investigate this question using weekly-confirmed H1N1 cases from the CDC, the American Time Use Survey (ATUS), and the Google Trends weekly search volume index for certain key terms. I found that individuals did exhibit some avoidance behaviour during the flu pandemic in response to the CDC data, but not the measures of media attention. However, the magnitudes of these adjustments are small in comparison to other measures of avoidance behaviour, such as reduced time in public during extreme weather events.
ContributorsGunn, Quentin Lee (Author) / Kuminoff, Nicolai (Thesis director) / Abbott, Joshua (Committee member) / Fenichel, Eli (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor)
Created2013-12
Description
In a season that spans 162 games over the course of six months, MLB teams that travel more face additional fatigue and jetlag from travel. This factor could negatively impact them on the field. To explore this issue, I tested the significance of different variables by creating four models, which compared travel with a team's ability to win games as well as its ability to hit home runs. Based on these models, it appears as though changing time zones does not affect the outcome of games. However, these results did indicate that visiting teams with a greater time zone advantage over their opponent are less likely to hit a home run in a game.
ContributorsAronson, Sean Matthew (Author) / MacFie, Brian (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor) / WPC Graduate Programs (Contributor) / Department of Finance (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / W. P. Carey School of Business (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