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# Regression Analysis: Predictive Equations

In 2012, the total payroll for the New York Yankess was almost \$200 million, while the total payroll for the Oakland Athletics (a team known for using baseball analytics or sabermetrics) was about \$55 million, less than one-third of the Yankees payroll. In the table attached to the Excel file, you will see the payrolls (in millions) and the total number of victories for the baseball teams in the American League in the 2012 season. Develop a regression model to predict the total number of victories based on the payroll. Use the model to predict the number of victories for a team with a team with a payroll of \$79 million. Based on the results of the computer output, discuss the relationship between payroll and victories.

Complete the following:
State the linear equation.
Explain the overall statistical significance of the model.
Explain the statistical significance for each independent variable in the model
Interpret the Adjusted R2.
Is this a good predictive equation(s)? Which variables should be excluded (if any) and why? Explain.
Use Excel's regression option to perform the regression. Use one Excel spreadsheet file for the calculations and explanations, with one worksheet per problem. Use the problem number for each worksheet name. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell).

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