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University Of Wyoming Basketball Case Solution & Answer

University Of Wyoming Basketball Case Solution 

Introduction

Indoor sports like Athlete and Basketball games are the primary source of income at Wyoming University and are primarily played by males. These sorts of games bring in a lot of money for the university’s athletic department. The men’s basketball team at Wyoming University hosts a seasonal game to increase sales and expand the market for buying and selling gaming tickets(Sorochuk, 2011).

The university’s director considers the following elements while determining how to maximize the university’s total earnings. These variables, if not adequately analyzed or managed by the university’s administration, will affect the university’s future sales. These are the following factors:

  1. The term “days” refers to both weekends and weekdays.
  2. Opponents, who may be conference or non-conference participants.
  3. Winning percentage across the four seasons offered.

Problem Statement

  • The issue arose when the department was required to anticipate income for the next basketball season, which is scheduled to begin at the end of 2009 and finish in early 2010.
  • Although the department sets ticket pricing, it has been causing the agency difficulty in projecting income. Nonetheless, throughout the years 2010–2011, the home game schedule was unknown.
  • A new issue has emerged because of the uncertainty and inaccurate projection of ticket sales and discounts.

Variables are related to Ticket and Concession Sales

After analyzing the previously collected data, it was determined that demand for tickets changes year to year, making it very impossible to anticipate the number of tickets and concession sales accurately. Additionally, it can be seen that the quantity of tickets and concession sales is based on the following three variables:

  • Weekdays in a week.
  • Opponent’s types.
  • The University of Wyoming team’s performances.

In this situation, the first factor is the college’s annual investment requirements. This variable implies that if more events are held, the groups will practice more, increasing income each year.

Most games were placed away from home grounds, or, in certain cases, other colleges’ venues completed a home-based event. The University of Wyoming was unable to offer major assets to the home-based series. Last year, the proportion was higher because it was comprised of more home-based events requiring many assets.

Another key factor affecting the revenue concession and ticket sales was the link with alternative groups. Thus, it was determined that several groups were competing against one another inside the groups, as opposed to expanding the opposition via the use of new groups, because such usages needs more expenditures, which would be invested in the new groups for further progress. As a result, these two factors would be deemed to have a significant impact on corporate operations.

Linear Regression to Develop Two Models:

Regression models are the statistical tools that helps in conducting the analysis of the connection between these two parameters, as well as the effect of one parameter on the other. Numerous interactions between the two variables are vary and based on the situation.

Additionally, the example employs two models: one based on estimated income and another on ticket sales. Thus far, we have examined the connection between the two as well as the variables’ impacts. Due to the interdependence of these two factors, it is possible to determine the expected evolution of the findings over time. Two models were created using linear regression, one with an income discount and one without. Both models have been examined and elaborated.

Model 1: Ticket Sales Forecast

The parameter that varies in this model is the number of tickets sold. As a result, the adjusted square of R represents the predicted frequency of one parameter compared to the other, as per the model’s explanation. According to the data, if another variable is run at 100% of the frequency, one dependent variable by 65%.

Additionally, the standard error introduced by the connection between the variables must be included in this model. This demonstrates that if income is anticipated to be produced when adjusted ticket sales are provided, there will be a small gap. As a result, it may be inferred that by making fewer adjustments to the process, the risk that comes with planned cards can get lower than focusing on income………………..

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