University of Wyoming Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

University of Wyoming Case Solution

1. Variables that is likely to be related 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 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 occasions. It is determined that the college of Wyoming lacked significant assets to contribute for home-based matches. In the last year, the percentage was high because of the way that it made out of more home-based occasions with a significant number of assets to contribute.

Another variable, which had influenced 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 would put resources into the new groups for further advancement. Hence, these two variables would be considered as having a remarkable effect on the business operations.

2. Linear regressions to develop two models:

The regression model could help to analyze the impact of these two variables and relationship with each other regarding the impact of one variable to another. Many relationships between both variables can change according to the situation.

Furthermore, the case implements with two models, one is the expected revenue and the other is the tickets sold. So far, the relationship between both is also analyzed along with the impact of both with the change of any one variable. Both variables are interdependent on each other, therefore it would help to determine the expected change in the outcome over the time. Two models have been generated from the linear regression, where one is predicting revenue concession, and another is predicting tickets sales. Both models have been analyzed, and interpreted.

Model 1: Predicting Ticket Sales:

In this model, it is considered that the changing variable consists of the tickets sold. Thus,according to the interpretation of the model, it shows that the adjusted R-square represents the expected occurrence of one variable over another. From the result, it is identified that 60% change of one variable would incur if another variable performs at 100% of occurrence.

Another factor to be considered under the model is the standard error generated from the relationship between the variables. This shows that a minimum amount of deviation would incur if the revenues would likely incur with the adjusted tickets being offered. Therefore, it is concluded that with less variation in the process, there is less risk associated with the predicted cards instead of focusing more on the revenues.

Model 2: Predicting Revenue Concession:

In this model, predicting revenue concession would allow the business to recognize the expected sources of income based on the limited number of tickets where it is also analyzed that the independent variable under this case is the actual tickets sold, consequently according to the real figure, the athletic planning committee would be able to identify the changing trends of concession revenue overtime. Moreover, from the results, it can be seen that there is a same R-square value created in the model as compared to model 1 because R-square signifies the cross-relationship between the variables.................

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