Regression Analysis: Predicting for Detroit Tigers Game
Regression Analysis: Predicting for Detroit Tigers Game
Managerial Economics
BSNS 6130
December 13, 2012
By:
Morgan Thomas
Chad Goodrich
Jake Dodson
Austin Burris
Brittany Lutz
Abstract
As there are many who invest in athletic events, the ability to better predict attendance to such events, such as the Detroit Tigers games, could benefit many. The benefits include being able to better stock concessions stands, allocate advertising budgets, and staff security. Therefore, the aim of our study was better explain the variation in attendance to the Detroit Tigers home games. The variables we used included: * Opponent * Detroit Tigers’ Winning Percentage * Opponent Winning Percentage * Ace Pitcher * Temperature * Promotional Days
We predicted each variable would have a positive relationship with attendance. However, our data proved that to be otherwise as you can see in our regression model:
Attendance = 29697.22 + 3524.993 (O) – 6723.86 (DTW%) + 691.2616 (OW%) + 936.4279 (AP) + 121.5826 (T) + 1810.263 (PD)
All variables except the opponents winning percentage had a positive relationship with attendance to each game. The R Squared also demonstrated that the variables used only explained 26.4% of the variation in attendance. Furthermore, P-Value of the F-Stat proved our model 99.9% level of confidence, meaning it is significant. Overall, three of our variables including temperature, opponent, and promotional days, were significant at the 90% level of confidence and three of them were not including opponent winning percentage, ace pitcher, and winning percentage.
Introduction
Businesses are continuously looking at ways to maximize their advertising dollars. Major League Baseball teams are no different, with franchises being valued in the billions and players salaries in the hundreds of millions, ownership must find ways to maximize their return on investment.
The purpose of this study is to determine the effect on
Managerial Economics
BSNS 6130
December 13, 2012
By:
Morgan Thomas
Chad Goodrich
Jake Dodson
Austin Burris
Brittany Lutz
Abstract
As there are many who invest in athletic events, the ability to better predict attendance to such events, such as the Detroit Tigers games, could benefit many. The benefits include being able to better stock concessions stands, allocate advertising budgets, and staff security. Therefore, the aim of our study was better explain the variation in attendance to the Detroit Tigers home games. The variables we used included: * Opponent * Detroit Tigers’ Winning Percentage * Opponent Winning Percentage * Ace Pitcher * Temperature * Promotional Days
We predicted each variable would have a positive relationship with attendance. However, our data proved that to be otherwise as you can see in our regression model:
Attendance = 29697.22 + 3524.993 (O) – 6723.86 (DTW%) + 691.2616 (OW%) + 936.4279 (AP) + 121.5826 (T) + 1810.263 (PD)
All variables except the opponents winning percentage had a positive relationship with attendance to each game. The R Squared also demonstrated that the variables used only explained 26.4% of the variation in attendance. Furthermore, P-Value of the F-Stat proved our model 99.9% level of confidence, meaning it is significant. Overall, three of our variables including temperature, opponent, and promotional days, were significant at the 90% level of confidence and three of them were not including opponent winning percentage, ace pitcher, and winning percentage.
Introduction
Businesses are continuously looking at ways to maximize their advertising dollars. Major League Baseball teams are no different, with franchises being valued in the billions and players salaries in the hundreds of millions, ownership must find ways to maximize their return on investment.
The purpose of this study is to determine the effect on
Cited: Nitecki, Noel, Libby Manship, and Mike Manship. Marketing Investments: Predicting Attendance for Detroit Tiger 's Games. Rep. N.p.: Anderson University, 2004. Print.