Matching Items (23)

133699-Thumbnail Image.png

Impact of R&D Expenditure on Pharmaceutical Drug Prices: A Cross-Country Comparison

Description

Over the past few decades, pharmaceutical spending has been increasing, due in large part to high prices of prescription drugs. In the United States, pharmaceutical manufacturers defend high prices by

Over the past few decades, pharmaceutical spending has been increasing, due in large part to high prices of prescription drugs. In the United States, pharmaceutical manufacturers defend high prices by citing the high costs of research and development, which they argue spurns innovation and makes up for the high prices paid by consumers. This study seeks to determine the validity of that claim and to fully understand the impact that R&D expenditures have on pharmaceutical drug prices. Employing a fixed effects regression, this study assesses the relationship between per capita R&D expenditure and per capita pharmaceutical spending (a stand-in variable for average drug price) for twelve OECD-member countries over a span of seven years. Holding country and year effects fixed, this regression shows a nearly one to one positive relationship between R&D expenditure and pharmaceutical spending, meaning a one-dollar increase in R&D expenditure increases pharmaceutical spending by around one-dollar as well. This impact, while statistically significant, is not that large, implying that R&D expenditures are not a strong driver of drug prices, contrary to what many pharmaceutical manufacturers argue.

Contributors

Agent

Created

Date Created
  • 2018-05

134415-Thumbnail Image.png

The Adaptive Lasso Procedure for Building a Traffic Forecasting Model

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

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.

Contributors

Agent

Created

Date Created
  • 2017-05

133035-Thumbnail Image.png

EFFECT OF A CASHLESS ECONOMY ON THE ROBBERY RATE

Description

A global trend towards cashlessness following the increase in technological advances in financial transactions lends way to a discussion of its various impacts on society. As part of this discussion,

A global trend towards cashlessness following the increase in technological advances in financial transactions lends way to a discussion of its various impacts on society. As part of this discussion, it is important to consider how this trend influences crime rates. The purpose of this project is to specifically investigate the relationship between a cashless society and the robbery rate. Using data collected from the World Bank’s Global Financial Inclusions Index and the United Nations Office of Drugs and Crime, we implemented a multilinear regression to observe this relationship across countries (n = 29). We aimed to do this by regressing the robbery rate on cashlessness and controlling for other related variables, such as gross domestic product and corruption. We found that as a country becomes more cashless, the robbery rate decreases (β = -677.8379, p = 0.071), thus providing an incentive for countries to join this global trend. We also conducted tests for heteroscedasticity and multicollinearity. Overall, our results indicate that a reduction in the amount of cash circulating within a country negatively impacts robbery rates.

Contributors

Agent

Created

Date Created
  • 2019-05

133036-Thumbnail Image.png

The Economic Impact of the Opioid Crisis in the United States

Description

This study examines the economic impact of the opioid crisis in the United States. Primarily testing the years 2007-2018, I gathered data from the Census Bureau, Centers for Disease Control,

This study examines the economic impact of the opioid crisis in the United States. Primarily testing the years 2007-2018, I gathered data from the Census Bureau, Centers for Disease Control, and Kaiser Family Foundation in order to examine the relative impact of a one dollar increase in GDP per Capita on the death rates caused by opioids. By implementing a fixed-effects panel data design, I regressed deaths on GDP per Capita while holding the following constant: population, U.S. retail opioid prescriptions per 100 people, annual average unemployment rate, percent of the population that is Caucasian, and percent of the population that is male. I found that GDP per Capita and opioid related deaths are negatively correlated, meaning that with every additional person dying from opioids, GDP per capita decreases. The finding of this research is important because opioid overdose is harmful to society, as U.S. life expectancy is consistently dropping as opioid death rates rise. Increasing awareness on this topic can help prevent misuse and the overall reduction in opioid related deaths.

Contributors

Agent

Created

Date Created
  • 2019-05

147913-Thumbnail Image.png

Measuring the Relationship between Self-Efficacy and Mindset in ASU Freshman Engineering Students

Description

This study investigated how mindset intervention in freshman engineering courses influenced students’ implicit intelligence and self-efficacy beliefs. An intervention which bolsters students’ beliefs that they possess the cognitive tools to

This study investigated how mindset intervention in freshman engineering courses influenced students’ implicit intelligence and self-efficacy beliefs. An intervention which bolsters students’ beliefs that they possess the cognitive tools to perform well in their classes can be the deciding factor in their decision to continue in their engineering major. Treatment was administered across four sections of an introductory engineering course where two professors taught two sections. Across three survey points, one course of each professor received the intervention while the other remained neutral, but the second time point switched this condition, so all students received intervention. Robust efficacy and mindset scales quantitatively measured the strength of their beliefs in their abilities, general and engineering, and if they believed they could change their intelligence and abilities. Repeated measures ANOVA and linear regressions revealed that students who embody a growth mindset tended to have stronger and higher self-efficacy beliefs. With the introduction of intervention, the relationship between mindset and self-efficacy grew stronger and more positive over time.

Contributors

Agent

Created

Date Created
  • 2021-05

137647-Thumbnail Image.png

Early Career Performance Models: Regression-Based Forecasting Models for Predicting Future Major League Baseball Player Performance

Description

The widespread use of statistical analysis in sports-particularly Baseball- has made it increasingly necessary for small and mid-market teams to find ways to maintain their analytical advantages over large market

The widespread use of statistical analysis in sports-particularly Baseball- has made it increasingly necessary for small and mid-market teams to find ways to maintain their analytical advantages over large market clubs. In baseball, an opportunity for exists for teams with limited financial resources to sign players under team control to long-term contracts before other teams can bid for their services in free agency. If small and mid-market clubs can successfully identify talented players early, clubs can save money, achieve cost certainty and remain competitive for longer periods of time. These deals are also advantageous to players since they receive job security and greater financial dividends earlier in their career. The objective of this paper is to develop a regression-based predictive model that teams can use to forecast the performance of young baseball players with limited Major League experience. There were several tasks conducted to achieve this goal: (1) Data was obtained from Major League Baseball and Lahman's Baseball Database and sorted using Excel macros for easier analysis. (2) Players were separated into three positional groups depending on similar fielding requirements and offensive profiles: Group I was comprised of first and third basemen, Group II contains second basemen, shortstops, and center fielders and Group III contains left and right fielders. (3) Based on the context of baseball and the nature of offensive performance metrics, only players who achieve greater than 200 plate appearances within the first two years of their major league debut are included in this analysis. (4) The statistical software package JMP was used to create regression models of each group and analyze the residuals for any irregularities or normality violations. Once the models were developed, slight adjustments were made to improve the accuracy of the forecasts and identify opportunities for future work. It was discovered that Group I and Group III were the easiest player groupings to forecast while Group II required several attempts to improve the model.

Contributors

Agent

Created

Date Created
  • 2013-05

147964-Thumbnail Image.png

Accuracy of Error Correction Code and Regression Analysis within a Python Software

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

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.

Contributors

Agent

Created

Date Created
  • 2021-05

Modelling Megacities: An Approach to Modelling Dense Urban Area

Description

In 2010, for the first time in human history, more than half of the world's total population lived in cities; this number is expected to increase to 60% or more

In 2010, for the first time in human history, more than half of the world's total population lived in cities; this number is expected to increase to 60% or more by 2050. The goal of this research effort is to create a comprehensive model and modelling framework for megacities, middleweight cities, and urban agglomerations, collectively referred to as dense urban areas. The motivation for this project comes from the United States Army's desire for readiness in all operating environments including dense urban areas. Though there is valuable insight in research to support Army operational behaviors, megacities are of unique interest to nearly every societal sector imaginable. A novel application for determining both main effects and interactive effects between factors within a dense urban area is a Design of Experiments- providing insight on factor causations. Regression Modelling can also be employed for analysis of dense urban areas, providing wide ranging insights into correlations between factors and their interactions. Past studies involving megacities concern themselves with general trend of cities and their operation. This study is unique in its efforts to model a singular megacity to enable decision support for military operational planning, as well as potential decision support to city planners to increase the sustainability of these dense urban areas and megacities.

Contributors

Agent

Created

Date Created
  • 2016-05

133570-Thumbnail Image.png

Regression Analysis on Colony Collapse Disorder in the United States

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

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.

Contributors

Agent

Created

Date Created
  • 2018-05

Analytics in the National Hockey League (NHL): A Regression of Goals For, Goals Against and Wins from 2005-2017

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

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.

Contributors

Agent

Created

Date Created
  • 2018-05