ctual statistical calculations. It shows the calculations of how each statistical variable affected winning percentage individually and how in combinations the same statistical variables affected winning percentage. The data we gathered from our analysis are presented in a formal way on the following page. Regression Steals Turnovers CommittedMade Field GoalsMade Free ThrowsStatisticsPer GamePer GamePer GamePer GameMultiple R0.2285470.4298360.5102070.384329R Square0.0522340.1847590.2603110.147709Adjusted R Square0.0492340.1821790.2579700.145011Standard Error0.1779280.1650200.1571880.168728Observations318318318318Regression Made 3 PointersBlocked ShotsPersonal FoulsAll Variables StatisticsPer GamePer GamePer GamePer GameMultiple R0.1470940.3669300.2767490.800440R Square0.0216370.1346370.0765900.640704Adjusted R Square0.0185410.1318990.0736680.632591Standard Error0.1807780.1700170.1756270.110607Observations318318318318The findings of listed in these tables show us how a jump in each independent variable considered individually would affect the dependent variable. The last statistical category listed show the relationship observed when all independent variable are considered together. Each independent variable considered separately had very low multiple R values which means that they could not significantly be used to predict the dependent variable of winning percentage.As you can see this last study gives us a better Multiple R value and a better R Squared value as well. We ran the multiple regression analysis eliminating variables that had lower R values but found no improvement from our original findings.Conclusions and RecommendationsWe were trying to determine what statistic or group of statistics could be used the help predict a teams winning percentage. Our study does not accomplish what we started out wanting to find, however, it does provide us with some direction with which to formulate future studies.The study does ...
