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Pga Tour- Sports Management

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Executive Summary

After looking the PGA golf data, yes it is possible to determine the performance measures of the players average score. In this problem the PGA Tour is trying to figure out whether any of these variables (Drive Distance, Drive Accuracy, GIR, Sand saves, PPR, and scrambling) are significant in relation to the original regression equation. Since the P-value of drive accuracy (.96737), drive distance (.05879), and sand saves is greater than the alpha P-value of .05, these are not significant to the original regression equation. None of these variables have an effect on determining the scoring average for these golfers. The other variables (GIR, PPR, and scrambling) are significant in predicting the average score. After taking out drive distance, drive accuracy and sand saves we run the regression analysis again. With only the new variables being GIR, PPR, and scrambling the new R^2 is .99992. This shows that these variables give you a better and closer prediction as to what the golfer shoots. With the overall f-value of 86.58115 it is highly residual when compared to regression. There are 4 outliers when looking at the standard residuals that are below negative two and above positive two. Since there were only 4 outliers that were closely off, this is not considered to be a big threat when the data is out of 125 golfers.

Managerial Report

1. Introduction A. Based on the PGA Tour Statistics, being a player making the top 125 will make you exempt for the following season. It is important to these golfers to be “exempt” because it qualifies them to be a full-time member of the PGA tour the next season. In terms of success, the scoring average is the most important term on the tour. Taking 125 of the highest paid players in 2008 we see what data sets correspond with the earnings of these athletes.

2. Analysis B. With

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