Matching Items (10)
Filtering by
- All Subjects: Regression
Description
Company X has developed RealSenseTM technology, a depth sensing camera that provides machines the ability to capture three-dimensional spaces along with motion within these spaces. The goal of RealSense was to give machines human-like senses, such as knowing how far away objects are and perceiving the surrounding environment. The key issue for Company X is how to commercialize RealSense's depth recognition capabilities. This thesis addresses the problem by examining which markets to address and how to monetize this technology. The first part of the analysis identified potential markets for RealSense. This was achieved by evaluating current markets that could benefit from the camera's gesture recognition, 3D scanning, and depth sensing abilities. After identifying seven industries where RealSense could add value, a model of the available, addressable, and obtainable market sizes was developed for each segment. Key competitors and market dynamics were used to estimate the portion of the market that Company X could capture. These models provided a forecast of the discounted gross profits that could be earned over the next five years. These forecasted gross profits, combined with an examination of the competitive landscape and synergistic opportunities, resulted in the selection of the three segments thought to be most profitable to Company X. These segments are smart home, consumer drones, and automotive. The final part of the analysis investigated entrance strategies. Company X's competitive advantages in each space were found by examining the competition, both for the RealSense camera in general and other technologies specific to each industry. Finally, ideas about ways to monetize RealSense were developed by exploring various revenue models and channels.
ContributorsDunn, Nicole (Co-author) / Boudreau, Thomas (Co-author) / Kinzy, Chris (Co-author) / Radigan, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / WPC Graduate Programs (Contributor) / Department of Psychology (Contributor) / Department of Finance (Contributor) / School of Accountancy (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Science (Contributor) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The current Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation of the major tasks and decisions within the DoD acquisition system, identifies several what-if intervention strategies to improve program completion time. However, processes that contribute to the program acquisition completion time were not explicitly identified in the simulation study. This research seeks to determine the acquisition processes that contribute significantly to total simulated program time in the acquisition system for all programs reaching Milestone C. Specifically, this research examines the effect of increased scope management, technology maturity, and decreased variation and mean process times in post-Design Readiness Review contractor activities by performing additional simulation analyses. Potential policies are formulated from the results to further improve program acquisition completion time.
ContributorsWorger, Danielle Marie (Author) / Wu, Teresa (Thesis director) / Shunk, Dan (Committee member) / Wirthlin, J. Robert (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2013-05
DescriptionA look at current 3D printing capabilities, and exploring the potential for additive manufacturing to transform the economy in the future.
ContributorsBennewitz, Chase (Co-author) / Paul, John (Co-author) / Parker, Kerry (Co-author) / Maltz, Arnold (Thesis director) / McDowell, John (Committee member) / Fujinami, Chris (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor) / Department of Supply Chain Management (Contributor) / W. P. Carey School of Business (Contributor)
Created2013-05
Description
The purpose of this research project is to develop a recommendation for Company X on the strategies it should use to enter a new market. This was done through the compilation and interpretation of data from the company and the construction of a financial model capable of analyzing our different proposed strategies. Company X is a leading producer of silicon chips which seeks to remain one of the leading forces in new technologies. Currently Company X wants to assess the value and risks associated with introducing a new packaging technology (FO-WLP) into their products either by developing the technology in-house or outsourcing production. The first portion of the research consisted mostly of gathering the necessary business acumen to be able to to fully understand our research findings. Market research was conducted to discover what competitors exist and what inputs should be included for the model with help from employees at Company X. The research then proceeded with the identification of three possible strategies and construction of financial models to analyze these options. Using the results from our analysis we were able to develop our recommendation for Company X and lay out the next steps which the Company needs to take before investing in the new technology.
ContributorsRubenzer, Jack (Co-author) / Galaviz, Roberto (Co-author) / Mariani, Stephanie (Co-author) / Mecinas, Freddy (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Department of Supply Chain Management (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
In this study, the implementation of educational technology and its effect on learning and user experience is measured. A demographic survey, pretest/posttest, and educational experience survey was used to collect data on the control and experimental groups. The experimental group was subjected to different learning material than the control group with the use of the Elements 4D mobile application by Daqri to learn basic chemical elements and compounds. The control group learning material provided all the exact information as the application, but in the 2D form of a printed packet. It was expected the experimental group would outperform the control group and have a more enjoyable experience and higher performance. After data analysis, it was concluded that the control group outperformed the experimental group on performance and both groups has similar experiences in contradiction to the hypothesis. Once the factors that contribute to the limitations of different study duration, learning the application beforehand, and only-memorization questions are addressed, the study can be conducted again. Application improvements may also alter the future results of the study and hopefully lead to full implementation into a curriculum.
ContributorsApplegate, Garrett Charles (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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, 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.
ContributorsChoksi, Aashini S (Co-author) / Elliott, Keeley (Co-author) / Goegan, Brian (Thesis director) / McDaniel, Cara (Committee member) / School of International Letters and Cultures (Contributor) / Department of Economics (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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, 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.
ContributorsRavi, Ritika Lisa (Author) / Goegan, Brian (Thesis director) / Hill, John (Committee member) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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 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.
ContributorsJack, Nathan Scott (Author) / Shunk, Dan (Thesis director) / Montgomery, Douglas (Committee member) / Borror, Connie (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2013-05
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 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.
ContributorsMathesen, Logan Michael (Author) / Zenzen, Frances (Thesis director) / Jennings, Cheryl (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper aims to get a snapshot of charter school and public school performance in the state of California, specifically looking at high schools. Based off of data gathered on specific variables of interest and carefully constructed regression models, we are testing whether charter schools perform differently from public schools. This paper attempts to analyze results from standard OLS regression models and random effects GLS models, both with and without
interaction effects between charter schools and ethnicity and geographic area. While discussing results, this paper will also acknowledge limitations while drawing the line between correlation and causality. Our variable of interest throughout the paper is charter school, controlling for other factors that might impact API scores such as geographic area, demographics, and school
characteristics.
interaction effects between charter schools and ethnicity and geographic area. While discussing results, this paper will also acknowledge limitations while drawing the line between correlation and causality. Our variable of interest throughout the paper is charter school, controlling for other factors that might impact API scores such as geographic area, demographics, and school
characteristics.
ContributorsValdez, Logan Taylor (Author) / Goegan, Brian (Thesis director) / Murphy, Alvin (Committee member) / Department of Information Systems (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05