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Regression equation and forecasting

For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the National League teams in the year 2000. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted (Figure not included) scatter plot as is the least-squares regression line. The equation for this line is ^y= 5.91+0.34x.

Player payroll,x Mean attendance, y
Arizona 103.5 39.51
Atlanta 93.8 32.10
Chicago Cubs 75.0 33.21
Cincinatti 46.3 22.96
Colorado 56.6 33.83
Florida 40.8 10.00
Houston 65.4 31.11
Los Angeles 101.5 38.64
Milwaukee 49.3 24.32
Montreal 37.9 10.00
New York Mets 94.4 34.57
Philadelphia 59.6 20.00
Pittsburg 46.1 21.98
San Diego 41.8 27.41
San Fransisco 78.4 40.12
St Louis 76.2 37.16

For these data, mean attendance values that are greater than the mean of the mean attendance values tend to be paired with player payroll values that are ? the mean of the player payroll values.

According to the regression equation, for an increase of one million dollars in player payroll, there is a corresponding ? of 0.34 thousand fans in mean attendance.

From the regression equation, what is the predicted mean attendance(in thousands of fans) when the player payroll is 40.8 million dollars?

What was the observed mean attendance (in thousands of fans) when the player payroll was 40.8 million dollars?

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Solution:

For these data, mean attendance values that are greater than the mean of the mean attendance values tend to be paired with player payroll values that are grater than the mean of the player payroll ...

Solution Summary

Solution predicts the mean attendence for a given payroll.

$2.19