University Of Wyoming Men’s Basketball Team Case Study Help


At the University of Wyoming, house games (indoor games)are played by a men’s basketball team that generates huge revenue for the university’s sports department. The problemhas arisen when the department forecastedthe revenues for the next basketball season, which is expected to take place at the end of the year 2009and the beginning of the year 2010. Though the department has set the prices of tickets, it has been creating trouble for the department in estimating the revenues, nonetheless, for the year between 2010 and 2011; the schedule of home games is not known. An additional problem arises due to the uncertainty and unsure estimation of ticket sales and discounts.

Variables areRelated to Ticket and Concession Sales:

The first variable under this case is the college’s investment criteria every year. In this variable, it suggests that if more events would be conducted, then the groups would tend to practice more, as a result, it would be able to increase its revenue every year.

The majority of the matches were set outside the home grounds or as it were, other colleges’ grounds completed a home-based occasion. It is determined that the college of Wyoming lacked significant assets to contribute to home-based matches. In the previous year; the percentage was high because of the way it was made out of more home-based occasions with a significant number of assets to contribute.

Another variable, which had great influence over the income concession and sold tickets was the association with alternate groups, therefore it was identified that there were numerous groups which were competing against each other in the groups as compared to using the new groups to expand the opposition on the grounds as such usage required extra finances, which tended to put resources into the new groups for further advancement. Hence, these two variables were considered as having a remarkable effect on business operations.

Linear Regression to Develop Two Models:

Regression models can help inanalyzing the relationship between these two variables and with each of them regarding the impact of the variables on one another. Many relationships between the two variables can vary depending on the situation.

In addition, the case is implemented using two models, one is expected income and the other is ticket sales. So far, we have analyzed the relationship between the two and the effects of any variables. These two variables are interdependent and therefore allow the expected evolution of the results to be determined over time. Two models were generated with linear regression, one offering an income discount and the other offering ticket sales. Both models were analyzed and explained.

Model 1: Ticket Sales Forecast

In this model, the variable that changes consists of the tickets sold. Consequently, according to the interpretation of the model, this shows that the adjusted square of R indicates the expected occurrence of one variable relative to the other. Based on the results, it can be determined that if another variable is executed at 100% of the occurrence, then one variable changes by 65%.

Another factor to consider in this model is the standard error caused by the relationship between the variables. This shows that if revenue is likely to be generated when providing adjusted ticket sales, there will be a minimal gap. Therefore, it can be concluded that with fewer changes in the process, the risks associated with the planned cards are lower rather than focusing on income.

The statistical tool i.e. regression is used in order to predict ticket sales and concession sales, it is being analyzed that the 27 percent is the coefficient of ticket sales which indicates that the value of ticket sales which is independent variable tends to increase because the coefficient of predicting ticket sales is positive. If ticket sales increases by one unit, there would be a 27% change in revenue. On the other hand, the multiple R-value for the ticket sales is comparatively lower than concession sales i.e. 65 percent which means that the data is 65% fitted to the regression line………………………………..


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