Unformatted text preview: ast year, and implementing the above stated
multiple regression model, the team statistician obtained the following least squares multiple
regression equation:
The multiple regression compute output also indicated the following:
Interpret the estimated model coefficient b2. 11340 Chapter 01  An Introduction to Business Statistics 93. The management of a professional baseball team is in the process of determining the
budget for next year. A major component of future revenue is attendance at the home games.
In order to predict attendance at home games the team statistician has used a multiple
regression model with dummy variables. The model is of the form: y = β0 + β1x1 + β2x2 +
β3x3 + ε where:
Y = attendance at a home game
x1 = current power rating of the team on a scale from 0 to 100 before the game.
x2 and x3 are dummy variables, and they are defined below.
x2 = 1, if weekend
x2= 0, otherwise
x3= 1, if weather is favorable
x3= 0, otherwise
After collecting the data based on 30 games from last year, and implementing the above stated
multiple regression model, the team statistician obtained the following least squares multiple
regression equation:
The multiple regression compute output also indicated the following:
Interpret the estimated model coefficient b3. 11341 Chapter 01  An Introduction to Business Statistics 94. The management of a professional baseball team is in the process of determining the
budget for next year. A major component of future revenue is attendance at the home games.
In order to predict attendance at home games the team statistician has used a multiple
regression model with dummy variables. The model is of the form: y = β0 + β1x1 + β2x2 +
β3x3 + ε where:
Y = attendance at a home game
x1 = current power rating of the team on a scale from 0 to 100 before the game.
x2 and x3 are dummy variables, and they are defined below.
x2 = 1, if weekend
x2= 0, otherwise
x3= 1, if weather is favorable
x3= 0, otherwise
After collecting the data based on 30 games from last year, and implementing the above stated
multiple regression model, the team statistician obtained the following least squares multiple
regression equation:
The multiple regression compute output also indicated the following:
Assume that the overall model is useful in predicting the game attendance and the team
statistician wants to know if the mean attendance is higher on the weekends as compared to
the weekdays. State the appropriate null and alternative hypotheses. 11342 Chapter 01  An Introduction to Business Statistics 95. The management of a professional baseball team is in the process of determining the
budget for next year. A major component of future revenue is attendance at the home games.
In order to predict attendance at home games the team statistician has used a multiple
regression model with dummy variables. The model is of the form: y = β0 + β1x1 + β2x2 +
β3x3 + ε where:
Y = attendance at a home game
x1 = current power rating of the team on a scale from 0 to 100 before the game.
x2 and x3 are dummy variables, and they are defined below.
x2 = 1, if weekend
x2= 0, otherwise
x3= 1, if weather is favorable
x3= 0, otherwise
After collecting the data based on 30 games from last year, and implementing the above stated
multiple regression model, the team statistician obtained the following least squares multiple
regression equation:
The multiple regression compute output also indicated the following:
Assume that the overall model is useful in predicting the game attendance and the team
statistician wants to know if the mean attendance is higher on the weekends as compared to
the weekdays. At α = .05, test to determine if the attendance is higher on weekend home
games. 11343 Chapter 01  An Introduction to Business Statistics 96. The management of a professional baseball team is in the process of determining the
budget for next year. A major component of future revenue is attendance at the home games.
In order to predict attendance at home games the team statistician has used a multiple
regression model with dummy variables. The model is of the form: y = β0 + β1x1 + β2x2 +
β3x3 + ε where:
Y = attendance at a home game
x1 = current power rating of the team on a scale from 0 to 100 before the game.
x2 and x3 are dummy variables, and they are defined below.
x2 = 1, if weekend
x2= 0, otherwise
x3= 1, if weather is favorable
x3= 0, otherwise
After collecting the data based on 30 games from last year, and implementing the above stated
multiple regression model, the team statistician obtained the following least squares multiple
regression equation:
The multiple regression compute output also indicated the following:
Assume that the overall model is useful in predicting the game attendance. Assume today is
Wednesday morning and the weather forecast indicates sunny, excellent weather conditions
for the rest of the day. Later today, there is a home baseball game for this team. Assume that
the current power rating of the team i...
View
Full
Document
 Winter '14

Click to edit the document details