**The University of Wyoming Menâ€™s Basketball Team Case Solution**

Therefore, in order to analyze the accuracy of flow, a deep analysis would be required to assess the factors that would contribute towards the performance of the proposed budget implemented by Bill Sparks. Therefore, it is identified that the root cause of the problem would be the inability to utilize all the factors related to the expected outcome.

**Analysis of expected revenue concession and ticket sold**

**Variables likely to be related to the revenues concession and ticket soldÂ **

In order to determine a deep analysis of utilizing the expected outcome generated from the proposed budget, first of all, it should be taken into consideration that those variables should be identified that would contribute towards the changing performance and results of revenue concession and the ticket sold. The first variable under the case is the universityâ€™s investment criteria each year, therefore it means that more events would determine more practices of the teams and more ability to increase the winning rates per year.

In exhibit 1, it can be seen that the most tickets sold were between 2007-2008 but the winning rates were not generating the expected results because under this case, most of the matches were set outside the home campus or in other words, other universityâ€™s campuses executed the home based events. It clearly shows that university of Wyoming lacked the necessary funds to invest for home-based matches. Later on in the same year, the ratio was high due to the fact that it composed of more home-based events with high number of funds to invest.

Another variable, which had an impact on the revenue concession and sold tickets, was the affiliation with the other teams, therefore it has been analyzed that there were only selected rival teams within a campus instead of utilizing the new teams to increase the competition because such utilization required additional funds which would be invested in the new teams for further development. Thus, these two variables are considered to have a major effect over the business operations.

**Using linear regression model to generate expected revenues**

In order to determine the expected revenues concessions as well as tickets to sell in the upcoming event,the use of linear regression model would help to analyze the impact of these two variables and relationship with each other regarding the impact of one variable over the other if the situation would change. Thus, such relationship might be directly or inversely linked with each other.

However, the case suggested two linear regression models; one for the expected revenue concession and the other for expected ticket to be sold. The relationship between them would also analyze the effect of using one variable with the changing variations of another variable. This would help to determine the expected change in the outcome if either of these two variables changes overtime. The following linear regression models have been analyzed and interpreted and are given below:

**Model 1 (Predicting ticket sales)**

Under this model, it is analyzed that the changing variable consists of the tickets been sold. Therefore, 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 perform at 100% of occurrence.

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

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