Regression Analysis

Regression Analysis

Team B

RES/342

July 26, 2012

Janet Smith

Regression Analysis

Baseball is one of the oldest and most venerable organized sports in the nation. Attendance to MLB games across the nation are determined by many different factors. The team’s record, the reputation of the team, the age of the stadium, the team’s lifespan, and event/concession pricing will all affect attendance. Team B will be performing a linear regression analysis on determining whether there is a correlation between Major League Baseball yearly attendance and the year stadiums were built.

The Decision Rule that is being put in to place is we will reject the null or fail to reject the null with a .05 alpha. “Sometimes a relationship cannot be modeled using a linear regression” (Doane & Seward, 2007). In the decision rule one will determine if the right data will be captured for the test by determining which path will be taken a linear or a nonlinear regression. Before running the test the proper rule must be set up to determine the alpha that will state whether to reject the null or fail to reject the null.

Research on a linear regression analysis hypothesis goes through the same five step procedure as the other types of hypothesis research. First step Team B will be using is to provide a hypothesis statement for the research. The hypothesis comes in two possible statements, the null hypothesis (H0), and the alternate hypothesis (H1). The null hypothesis for the linear regression is the MLB yearly attendance, which is the predictor (X); correlates with the year MLB stadiums were built (Y). The alternate hypothesis of the MLB yearly attendance does not correlate with the year MLB stadiums was built.

The second procedure of the hypothesis is stating the significance for the alpha. The team for this research will be using the .05 alpha as life and death has no importance in this research. Type I (rejecting the null) or Type II (failing to reject).

Choosing the...