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- Creators: Department of Information Systems
Description
This paper is intended to identify a correlation between the winning percentage of sports teams in the four major professional sports leagues in the United States and the GDP per capita of their respective cities. We initially compiled fifteen years of franchise performance along with economic data from the Federal Reserve Bank of St. Louis to analyze this relationship. After converting the data into a language recognized by Stata, the regression tool we used, we ran multiple regressions to find relevant correlations based off of our inputs. This paper will show the value of the economic impact of strong or weak performance throughout various economic cycles through data analysis and conclusions drawn from the results of the regression analysis.
ContributorsAndl, Tyler (Co-author) / Shirk, Brandon (Co-author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
We were driven by the question: what is happening to the popularity of Major League Baseball? In order to answer this question we compared the league structure of Major League Baseball with that of the National Football League. We were able to speak with five former or current members of the respective leagues in order to gain some insight into how the two leagues operate. The main focus of our research was around the payroll structures of the two leagues as well as their revenue sharing policies. In the end, we discovered that Major League Baseball is becoming highly regionalized. The sport is still growing in popularity in terms of revenue and fan involvement, but it is becoming less popular on a national stage. The league is benefitting greatly from factors like the increasing importance of "TiVo proof programming" and a lack of competition. Each league is very different in its own right. While the NFL promotes a perception of competitive balance, Major League Baseball can be plagued by the negative perception it creates surrounding some of its smaller market teams.
ContributorsHeath, Cameron (Co-author) / Linamen, John (Co-author) / Eaton, John (Thesis director) / Mokwa, Michael (Committee member) / Barrett, The Honors College (Contributor) / WPC Graduate Programs (Contributor) / Department of Marketing (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor)
Created2015-05
Description
Customer lifetime value has been a popular topic within the marketing field with which many researchers and marketing managers have been dwelling. The topic plays an important role in customer segmentation and has been studied and applied in a variety of business areas. The main objective of customer lifetime value and customer segmentation is to classify the importance level of each client to a company and compare it to other clients. Questions, such as which marketing strategies should be implemented for which customers and how much should be invested in a certain group of customers can all be answered by customer lifetime value and customer segmentation. However, the related literature is missing comparative research on assessing the amount of time from the initial point of acquisition that a client needs to be with the company in order to accurately predict its customer segment. This paper intends to provide a clarification to the problem. Purpose: Analyze customer profitability with clustering analysis and identify how many years does a customer need to be with a company to accurately predict its customer segment. By determining this number, managers can understand their clients better and establish which clients will most likely yield a greater profit at an early stage of the relationship. Methodology: Using data mining to clean and prepare our financial services dataset, we selected young clients who were less than ten years old. Linear regression and K-means clustering analyses then returned five clusters of clients. Next, we predicted the accuracy levels of customers with two to seven data points against the "correct" segment. Lastly, we validated our overall prediction accuracy levels using the chance probability and the desired classification accuracy, calculated from a discriminant analysis. Findings: We found that using five data points or more to cluster returned percentage accuracies greater than the desired classification accuracy. However, this desired classification accuracy and the percentage accuracies were fairly low and not sufficient to use as a base for business decisions and other managerial purposes.
ContributorsOng, Kaitlyn (Co-author) / Tsen, Canh (Co-author) / Hollmann, Thomas (Thesis director) / Han, Sang Pil (Committee member) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
This project examines the effectiveness and key performance indicators of state prisons across the country in order to establish how Arizona’s prisons compare. Variables such as the daily inmate cost, annual budget, and the percentage of the budget spent on healthcare are all examined for a measurable impact on recidivism rate, which is the rate at which individuals released from prison re-offend and return to prison. The findings and next steps for the correctional industry are slightly controversial but will prove effective in improving the industry.
ContributorsScroggins, Ethan (Author) / Darcy, David (Thesis director) / Pirtle, Danny (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05