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The Demographics of Polling Places

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Elections in the United States are highly decentralized with vast powers given to the states to control laws surrounding voter registration, primary procedures, and polling places even in elections of federal officials. There are many individual factors that predict a

Elections in the United States are highly decentralized with vast powers given to the states to control laws surrounding voter registration, primary procedures, and polling places even in elections of federal officials. There are many individual factors that predict a person's likelihood of voting including race, education, and age. Historically disenfranchised groups are still disproportionately affected by restrictive voter registration and ID laws which can suppress their turnout. Less understood is how election-day polling place accessibility affects turnout. Absentee and early voting increase accessibility for all voters, but 47 states still rely on election-day polling places. I study how the geographic allocation of polling places and the number of voters assigned to each (polling place load) in Maricopa County, Arizona has affected turnout in primary and general elections between 2006 and 2016 while controlling for the demographics of voting precincts. This represents a significant data problem; voting precincts changed three times during the time studied and polling places themselves can change every election. To aid in analysis, I created a visualization that allows for the exploration of polling place load, precinct demographics, and polling place accessibility metrics in a map view of the county. I find through a spatial regression model that increasing the load on a polling place can decrease the election-day turnout and prohibitively large distances to the polling place have a similar effect. The effect is more pronounced during general elections and is present at varying levels during each of the 12 elections studied. Finally, I discuss how early voting options appear to have little positive effect on overall turnout and may in fact decrease it.

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2017-12

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Linear Modeling for Insurance Ratemaking/Reserving: Modeling Loss Development Factors for Catastrophe Claims

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Catastrophe events occur rather infrequently, but upon their occurrence, can lead to colossal losses for insurance companies. Due to their size and volatility, catastrophe losses are often treated separately from other insurance losses. In fact, many property and casualty insurance

Catastrophe events occur rather infrequently, but upon their occurrence, can lead to colossal losses for insurance companies. Due to their size and volatility, catastrophe losses are often treated separately from other insurance losses. In fact, many property and casualty insurance companies feature a department or team which focuses solely on modeling catastrophes. Setting reserves for catastrophe losses is difficult due to their unpredictable and often long-tailed nature. Determining loss development factors (LDFs) to estimate the ultimate loss amounts for catastrophe events is one method for setting reserves. In an attempt to aid Company XYZ set more accurate reserves, the research conducted focuses on estimating LDFs for catastrophes which have already occurred and have been settled. Furthermore, the research describes the process used to build a linear model in R to estimate LDFs for Company XYZ's closed catastrophe claims from 2001 \u2014 2016. This linear model was used to predict a catastrophe's LDFs based on the age in weeks of the catastrophe during the first year. Back testing was also performed, as was the comparison between the estimated ultimate losses and actual losses. Future research consideration was proposed.

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2018-05

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Assessing the Health Insurance Needs of the Low-Income Hispanic/Latino Population in Phoenix, Arizona

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The growing Hispanic population in Phoenix, Arizona frequently lacks financial resources which may limit their access to health care. The goal of this study was to identify the ideal factors in a health insurance plan for the Hispanic/Latino population in

The growing Hispanic population in Phoenix, Arizona frequently lacks financial resources which may limit their access to health care. The goal of this study was to identify the ideal factors in a health insurance plan for the Hispanic/Latino population in Phoenix, AZ. A survey was designed to gather information regarding demographics, health insurance, preferences, and affordability. The survey was completed by 260 participants. Several multivariate regressions were run using SAS Statistical Software. The final model generated explained 4.48% of the variation in the data set. It showed that an individual who identified as Hispanic/Latino was 8.2% less likely to have health insurance. In addition, an individual who identified as a US Citizen was 23% more likely to have health insurance. To improve access and enrollment among the Hispanic/Latino population, further investigation is needed to identify relevant communication techniques that increase enrollment among this high-risk community.

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2018-05

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Homeward Bound: An Overview of Continuing Care at Home

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AARP estimates that 90% of seniors wish to remain in their homes during retirement. Seniors need assistance as they age, historically they have received assistance from either family members, nursing homes, or Continuing Care Retirement Communities. For seniors not wanting

AARP estimates that 90% of seniors wish to remain in their homes during retirement. Seniors need assistance as they age, historically they have received assistance from either family members, nursing homes, or Continuing Care Retirement Communities. For seniors not wanting any of these options, there has been very few alternatives. Now, the emergence of the continuing care at home program is providing hope for a different method of elder care moving forward. CCaH programs offer services such as: skilled nursing care, care coordination, emergency response systems, aid with personal and health care, and transportation. Such services allow seniors to continue to live in their own home with assistance as their health deteriorates over time. Currently, only 30 CCaH programs exist. With the growth of the elderly population in the coming years, this model seems poised for growth.

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2019-05

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A Global Climate Crisis: Why is Arizona Behind The Renewable Energy Curve?

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Greenhouse gas emissions (GHG) continue to contribute heavily to global warming. It is estimated that the international community has only until 2050 to eliminate total carbon emissions or risk irreversible climate change. Arizona, despite its vast solar energy resources, is

Greenhouse gas emissions (GHG) continue to contribute heavily to global warming. It is estimated that the international community has only until 2050 to eliminate total carbon emissions or risk irreversible climate change. Arizona, despite its vast solar energy resources, is particularly behind in the global transition to carbon-free energy. This paper looks to explore issues that may be preventing Arizona from an efficient transition to carbon-free generation technologies. Identifiable factors include outdated state energy generation standards, lack of oversight and accountability of Arizona’s electricity industry regulatory body, and the ability for regulated utilities to take advantage of “dark money” campaign contributions. Various recommendations for mitigating the factors preventing Arizona from a carbon-free future are presented. Possibilities such as modernizing state energy generation standards, increasing oversight and accountability of Arizona’s electricity industry regulatory body, and potential market restructuring which would do away with the traditional regulated utility framework are explored. The goal is to inform readers of the issues plaguing the Arizona energy industry and recommend potential solutions moving forward.

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2020-12

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Looking at COVID-19 as a Factor in Insurance Loss Reserving Models

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A factor accounting for the COVID-19 pandemic was added to a generalized linear model to more accurately predict unpaid claims. COVID-19 has affected not just healthcare, but all sectors of the economy. Because of this, whether or not an automobile

A factor accounting for the COVID-19 pandemic was added to a generalized linear model to more accurately predict unpaid claims. COVID-19 has affected not just healthcare, but all sectors of the economy. Because of this, whether or not an automobile insurance claim is filed during the pandemic needs to be taken into account while estimating unpaid claims. Reserve-estimating functions such as glmReserve from the “ChainLadder” package in the statistical software R were experimented with to produce their own results. Because of their insufficiency, a manual approach to building the model turned out to be the most proficient method. Utilizing the GLM function, a model was built that emulated linear regression with a factor for COVID-19. The effects of such a model are analyzed based on effectiveness and interpretablility. A model such as this would prove useful for future calculations, especially as society is now returning to a “normal” state.

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2022-05