Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the American League and the National League. MLB collects a wide variety of team and player statistics. Some of the statistics often used to evaluate pitching performance are as follows: ERA: The average number of earned runs given up by the pitcher per nine innings. An earned run is any run that the opponent scores off a particular pitcher except for runs scored as a result of errors. SO/IP: The average number of strikeouts per inning pitched. HR/IP: The average number of home runs per inning pitched. R/IP: The number of runs given up per inning pitched. The following data show values for these statistics for a random sample of 20 pitchers from the American League for a full season. a. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched. b. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of home runs per inning pitched. c. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched. d. A. J. Burnett, a pitcher for the New York Yankees, had an average number of strikeouts per inning pitched of .91 and an average number of home runs per inning of .16. Use the estimated regression equation developed in part (c) to predict the average number of runs given up per inning for A. J. Burnett. ( Note: The actual value for R/IP was .6.) e. Suppose a suggestion was made to also use the earned run average as another independent variable in part (c). What do you think of this suggestion?

Question

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Answer 1

Textbook Question

Chapter 15.2, Problem 10E

Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the American League and the National League. MLB collects a wide variety of team and player statistics. Some of the statistics often used to evaluate pitching performance are as follows:

ERA: The average number of earned runs given up by the pitcher per nine innings. An earned run is any run that the opponent scores off a particular pitcher except for runs scored as a result of errors.

SO/IP: The average number of strikeouts per inning pitched.

HR/IP: The average number of home runs per inning pitched.

R/IP: The number of runs given up per inning pitched.

The following data show values for these statistics for a random sample of 20 pitchers from the American League for a full season.

Chapter 15.2, Problem 10E, Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the

a. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched.
b. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of home runs per inning pitched.
c. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched.
d. A. J. Burnett, a pitcher for the New York Yankees, had an average number of strikeouts per inning pitched of .91 and an average number of home runs per inning of .16. Use the estimated regression equation developed in part (c) to predict the average number of runs given up per inning for A. J. Burnett. (Note: The actual value for R/IP was .6.)
e. Suppose a suggestion was made to also use the earned run average as another independent variable in part (c). What do you think of this suggestion?

a.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Explanation of Solution

Calculation:

The Major League Baseball (MLB) dataset consist of statistics regarding the average number of earned runs given up by the pitcher per nine innings (ERA), the average number of strikeouts per innings pitched (SO/IP), the average number of home runs per innings pitched (HR/IP) and the number of runs given up per innings pitched (R/IP).

Multiple linear regression model:

A multiple linear regression model is given as y^=b0+b1x1+...+bpxp where y^ is the predicted value of response or dependent variables, and x1,x2,...,xp are the p predictor variables. The quantities b1,b2,...,bp are the estimated slopes corresponding to x1,x2,...,xp respectively, and b0 is the estimated intercept of the line, from the sample data.

Regression:

Software procedure:

Step-by-step procedure to get the regression equation using EXCEL software:

Open an EXCEL sheet and enter the data on W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$D$21.
Click the box of Labels.
Under Output Range enter $I$1.
Click OK.

The output using EXCEL software is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 1

Thus, the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

b.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $E$1:$E$21.
Click the box of Labels.
Under Output Range enter $L$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 2

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

c.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$E$21.
Click the box of Labels.
Under Output Range enter $O$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 3

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

d.

Expert Solution

To determine

Predict the average number of runs given up per inning for Pitcher A using the regression equation of Part c.

Answer to Problem 10E

The predicted average number of runs given up per inning for Pitcher A is 0.4757.

Explanation of Solution

Calculation:

The average number of strikeouts and the average number of home runs per innings pitched for Pitcher A are 0.91 and 0.16, respectively.

From Part (c), it is found that estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP

Thus, using the given information, the predicted average number of runs given up per inning for Pitcher At is calculated below.

R/IP^=0.5365−0.2483(0.91)+1.032(0.16)=0.5365−0.225953+0.16512≈0.4757

Thus, the predicted average number of runs given up per inning for Pitcher A is 0.4757.

It is to be noted that the actual average number of runs given up per inning for Pitcher A was 0.6.

Thus, the predicted value is slightly less than the actual value. Hence, the residual is positive, which underestimates the model slightly.

e.

Expert Solution

To determine

Explain about the suggestion to use the earned run average as another independent variable in the regression model developed in Part c.

Explanation of Solution

Calculation:

It would be useful to use the earned run average or ERA as a third independent variable, only if it has a strong linear association with the response variable, R/IP. The correlation coefficient measures the strength of the linear relation between two variables.

Software procedure:

Step-by-step procedure to get correlation coefficient using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
In cell I1 enter the formula =CORREL(F1:F21,C1:C21).
Press Enter.

The EXCEL output is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 4

Thus, the correlation coefficient between R/IO and ERA is approximately 0.964, which is very close to 1, indicating a very strong positive linear relationship between the variables.

Due to the high correlation coefficient it can be said that if a pitcher gives up more runs per inning, then the pitcher’s run average also increases. Thus, automatically there will be high value of the coefficient of determination in the regression analysis, if ERA is used as a predictor variable.

Thus, it is reasonable to use the earned run average as another independent variable in the regression model developed in Part c.

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Answer 2

Textbook Question

Chapter 15.2, Problem 10E

Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the American League and the National League. MLB collects a wide variety of team and player statistics. Some of the statistics often used to evaluate pitching performance are as follows:

ERA: The average number of earned runs given up by the pitcher per nine innings. An earned run is any run that the opponent scores off a particular pitcher except for runs scored as a result of errors.

SO/IP: The average number of strikeouts per inning pitched.

HR/IP: The average number of home runs per inning pitched.

R/IP: The number of runs given up per inning pitched.

The following data show values for these statistics for a random sample of 20 pitchers from the American League for a full season.

Chapter 15.2, Problem 10E, Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the

a. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched.
b. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of home runs per inning pitched.
c. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched.
d. A. J. Burnett, a pitcher for the New York Yankees, had an average number of strikeouts per inning pitched of .91 and an average number of home runs per inning of .16. Use the estimated regression equation developed in part (c) to predict the average number of runs given up per inning for A. J. Burnett. (Note: The actual value for R/IP was .6.)
e. Suppose a suggestion was made to also use the earned run average as another independent variable in part (c). What do you think of this suggestion?

a.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Explanation of Solution

Calculation:

The Major League Baseball (MLB) dataset consist of statistics regarding the average number of earned runs given up by the pitcher per nine innings (ERA), the average number of strikeouts per innings pitched (SO/IP), the average number of home runs per innings pitched (HR/IP) and the number of runs given up per innings pitched (R/IP).

Multiple linear regression model:

A multiple linear regression model is given as y^=b0+b1x1+...+bpxp where y^ is the predicted value of response or dependent variables, and x1,x2,...,xp are the p predictor variables. The quantities b1,b2,...,bp are the estimated slopes corresponding to x1,x2,...,xp respectively, and b0 is the estimated intercept of the line, from the sample data.

Regression:

Software procedure:

Step-by-step procedure to get the regression equation using EXCEL software:

Open an EXCEL sheet and enter the data on W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$D$21.
Click the box of Labels.
Under Output Range enter $I$1.
Click OK.

The output using EXCEL software is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 1

Thus, the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

b.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $E$1:$E$21.
Click the box of Labels.
Under Output Range enter $L$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 2

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

c.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$E$21.
Click the box of Labels.
Under Output Range enter $O$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 3

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

d.

Expert Solution

To determine

Predict the average number of runs given up per inning for Pitcher A using the regression equation of Part c.

Answer to Problem 10E

The predicted average number of runs given up per inning for Pitcher A is 0.4757.

Explanation of Solution

Calculation:

The average number of strikeouts and the average number of home runs per innings pitched for Pitcher A are 0.91 and 0.16, respectively.

From Part (c), it is found that estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP

Thus, using the given information, the predicted average number of runs given up per inning for Pitcher At is calculated below.

R/IP^=0.5365−0.2483(0.91)+1.032(0.16)=0.5365−0.225953+0.16512≈0.4757

Thus, the predicted average number of runs given up per inning for Pitcher A is 0.4757.

It is to be noted that the actual average number of runs given up per inning for Pitcher A was 0.6.

Thus, the predicted value is slightly less than the actual value. Hence, the residual is positive, which underestimates the model slightly.

e.

Expert Solution

To determine

Explain about the suggestion to use the earned run average as another independent variable in the regression model developed in Part c.

Explanation of Solution

Calculation:

It would be useful to use the earned run average or ERA as a third independent variable, only if it has a strong linear association with the response variable, R/IP. The correlation coefficient measures the strength of the linear relation between two variables.

Software procedure:

Step-by-step procedure to get correlation coefficient using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
In cell I1 enter the formula =CORREL(F1:F21,C1:C21).
Press Enter.

The EXCEL output is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 4

Thus, the correlation coefficient between R/IO and ERA is approximately 0.964, which is very close to 1, indicating a very strong positive linear relationship between the variables.

Due to the high correlation coefficient it can be said that if a pitcher gives up more runs per inning, then the pitcher’s run average also increases. Thus, automatically there will be high value of the coefficient of determination in the regression analysis, if ERA is used as a predictor variable.

Thus, it is reasonable to use the earned run average as another independent variable in the regression model developed in Part c.

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Answer 3

Textbook Question

Chapter 15.2, Problem 10E

Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the American League and the National League. MLB collects a wide variety of team and player statistics. Some of the statistics often used to evaluate pitching performance are as follows:

ERA: The average number of earned runs given up by the pitcher per nine innings. An earned run is any run that the opponent scores off a particular pitcher except for runs scored as a result of errors.

SO/IP: The average number of strikeouts per inning pitched.

HR/IP: The average number of home runs per inning pitched.

R/IP: The number of runs given up per inning pitched.

The following data show values for these statistics for a random sample of 20 pitchers from the American League for a full season.

Chapter 15.2, Problem 10E, Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the

a. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched.
b. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of home runs per inning pitched.
c. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched.
d. A. J. Burnett, a pitcher for the New York Yankees, had an average number of strikeouts per inning pitched of .91 and an average number of home runs per inning of .16. Use the estimated regression equation developed in part (c) to predict the average number of runs given up per inning for A. J. Burnett. (Note: The actual value for R/IP was .6.)
e. Suppose a suggestion was made to also use the earned run average as another independent variable in part (c). What do you think of this suggestion?

a.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Explanation of Solution

Calculation:

The Major League Baseball (MLB) dataset consist of statistics regarding the average number of earned runs given up by the pitcher per nine innings (ERA), the average number of strikeouts per innings pitched (SO/IP), the average number of home runs per innings pitched (HR/IP) and the number of runs given up per innings pitched (R/IP).

Multiple linear regression model:

A multiple linear regression model is given as y^=b0+b1x1+...+bpxp where y^ is the predicted value of response or dependent variables, and x1,x2,...,xp are the p predictor variables. The quantities b1,b2,...,bp are the estimated slopes corresponding to x1,x2,...,xp respectively, and b0 is the estimated intercept of the line, from the sample data.

Regression:

Software procedure:

Step-by-step procedure to get the regression equation using EXCEL software:

Open an EXCEL sheet and enter the data on W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$D$21.
Click the box of Labels.
Under Output Range enter $I$1.
Click OK.

The output using EXCEL software is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 1

Thus, the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

b.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $E$1:$E$21.
Click the box of Labels.
Under Output Range enter $L$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 2

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

c.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$E$21.
Click the box of Labels.
Under Output Range enter $O$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 3

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

d.

Expert Solution

To determine

Predict the average number of runs given up per inning for Pitcher A using the regression equation of Part c.

Answer to Problem 10E

The predicted average number of runs given up per inning for Pitcher A is 0.4757.

Explanation of Solution

Calculation:

The average number of strikeouts and the average number of home runs per innings pitched for Pitcher A are 0.91 and 0.16, respectively.

From Part (c), it is found that estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP

Thus, using the given information, the predicted average number of runs given up per inning for Pitcher At is calculated below.

R/IP^=0.5365−0.2483(0.91)+1.032(0.16)=0.5365−0.225953+0.16512≈0.4757

Thus, the predicted average number of runs given up per inning for Pitcher A is 0.4757.

It is to be noted that the actual average number of runs given up per inning for Pitcher A was 0.6.

Thus, the predicted value is slightly less than the actual value. Hence, the residual is positive, which underestimates the model slightly.

e.

Expert Solution

To determine

Explain about the suggestion to use the earned run average as another independent variable in the regression model developed in Part c.

Explanation of Solution

Calculation:

It would be useful to use the earned run average or ERA as a third independent variable, only if it has a strong linear association with the response variable, R/IP. The correlation coefficient measures the strength of the linear relation between two variables.

Software procedure:

Step-by-step procedure to get correlation coefficient using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
In cell I1 enter the formula =CORREL(F1:F21,C1:C21).
Press Enter.

The EXCEL output is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 4

Thus, the correlation coefficient between R/IO and ERA is approximately 0.964, which is very close to 1, indicating a very strong positive linear relationship between the variables.

Due to the high correlation coefficient it can be said that if a pitcher gives up more runs per inning, then the pitcher’s run average also increases. Thus, automatically there will be high value of the coefficient of determination in the regression analysis, if ERA is used as a predictor variable.

Thus, it is reasonable to use the earned run average as another independent variable in the regression model developed in Part c.

Want to see more full solutions like this?

Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

Answer 4

Textbook Question

Chapter 15.2, Problem 10E

Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the American League and the National League. MLB collects a wide variety of team and player statistics. Some of the statistics often used to evaluate pitching performance are as follows:

ERA: The average number of earned runs given up by the pitcher per nine innings. An earned run is any run that the opponent scores off a particular pitcher except for runs scored as a result of errors.

SO/IP: The average number of strikeouts per inning pitched.

HR/IP: The average number of home runs per inning pitched.

R/IP: The number of runs given up per inning pitched.

The following data show values for these statistics for a random sample of 20 pitchers from the American League for a full season.

Chapter 15.2, Problem 10E, Baseball Pitcher Performance. Major League Baseball (MLB) consists of teams that play in the

a. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched.
b. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of home runs per inning pitched.
c. Develop an estimated regression equation that can be used to predict the average number of runs given up per inning given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched.
d. A. J. Burnett, a pitcher for the New York Yankees, had an average number of strikeouts per inning pitched of .91 and an average number of home runs per inning of .16. Use the estimated regression equation developed in part (c) to predict the average number of runs given up per inning for A. J. Burnett. (Note: The actual value for R/IP was .6.)
e. Suppose a suggestion was made to also use the earned run average as another independent variable in part (c). What do you think of this suggestion?

a.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Explanation of Solution

Calculation:

The Major League Baseball (MLB) dataset consist of statistics regarding the average number of earned runs given up by the pitcher per nine innings (ERA), the average number of strikeouts per innings pitched (SO/IP), the average number of home runs per innings pitched (HR/IP) and the number of runs given up per innings pitched (R/IP).

Multiple linear regression model:

A multiple linear regression model is given as y^=b0+b1x1+...+bpxp where y^ is the predicted value of response or dependent variables, and x1,x2,...,xp are the p predictor variables. The quantities b1,b2,...,bp are the estimated slopes corresponding to x1,x2,...,xp respectively, and b0 is the estimated intercept of the line, from the sample data.

Regression:

Software procedure:

Step-by-step procedure to get the regression equation using EXCEL software:

Open an EXCEL sheet and enter the data on W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$D$21.
Click the box of Labels.
Under Output Range enter $I$1.
Click OK.

The output using EXCEL software is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 1

Thus, the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

b.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $E$1:$E$21.
Click the box of Labels.
Under Output Range enter $L$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 2

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

c.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$E$21.
Click the box of Labels.
Under Output Range enter $O$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 3

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

d.

Expert Solution

To determine

Predict the average number of runs given up per inning for Pitcher A using the regression equation of Part c.

Answer to Problem 10E

The predicted average number of runs given up per inning for Pitcher A is 0.4757.

Explanation of Solution

Calculation:

The average number of strikeouts and the average number of home runs per innings pitched for Pitcher A are 0.91 and 0.16, respectively.

From Part (c), it is found that estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP

Thus, using the given information, the predicted average number of runs given up per inning for Pitcher At is calculated below.

R/IP^=0.5365−0.2483(0.91)+1.032(0.16)=0.5365−0.225953+0.16512≈0.4757

Thus, the predicted average number of runs given up per inning for Pitcher A is 0.4757.

It is to be noted that the actual average number of runs given up per inning for Pitcher A was 0.6.

Thus, the predicted value is slightly less than the actual value. Hence, the residual is positive, which underestimates the model slightly.

e.

Expert Solution

To determine

Explain about the suggestion to use the earned run average as another independent variable in the regression model developed in Part c.

Explanation of Solution

Calculation:

It would be useful to use the earned run average or ERA as a third independent variable, only if it has a strong linear association with the response variable, R/IP. The correlation coefficient measures the strength of the linear relation between two variables.

Software procedure:

Step-by-step procedure to get correlation coefficient using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
In cell I1 enter the formula =CORREL(F1:F21,C1:C21).
Press Enter.

The EXCEL output is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 4

Thus, the correlation coefficient between R/IO and ERA is approximately 0.964, which is very close to 1, indicating a very strong positive linear relationship between the variables.

Due to the high correlation coefficient it can be said that if a pitcher gives up more runs per inning, then the pitcher’s run average also increases. Thus, automatically there will be high value of the coefficient of determination in the regression analysis, if ERA is used as a predictor variable.

Thus, it is reasonable to use the earned run average as another independent variable in the regression model developed in Part c.

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Answer 5

Textbook Question

Answer 6

Textbook Question

Answer 7

a.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Explanation of Solution

Calculation:

The Major League Baseball (MLB) dataset consist of statistics regarding the average number of earned runs given up by the pitcher per nine innings (ERA), the average number of strikeouts per innings pitched (SO/IP), the average number of home runs per innings pitched (HR/IP) and the number of runs given up per innings pitched (R/IP).

Multiple linear regression model:

A multiple linear regression model is given as y^=b0+b1x1+...+bpxp where y^ is the predicted value of response or dependent variables, and x1,x2,...,xp are the p predictor variables. The quantities b1,b2,...,bp are the estimated slopes corresponding to x1,x2,...,xp respectively, and b0 is the estimated intercept of the line, from the sample data.

Regression:

Software procedure:

Step-by-step procedure to get the regression equation using EXCEL software:

Open an EXCEL sheet and enter the data on W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$D$21.
Click the box of Labels.
Under Output Range enter $I$1.
Click OK.

The output using EXCEL software is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 1

Thus, the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

b.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $E$1:$E$21.
Click the box of Labels.
Under Output Range enter $L$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 2

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

c.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$E$21.
Click the box of Labels.
Under Output Range enter $O$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 3

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

d.

Expert Solution

To determine

Predict the average number of runs given up per inning for Pitcher A using the regression equation of Part c.

Answer to Problem 10E

The predicted average number of runs given up per inning for Pitcher A is 0.4757.

Explanation of Solution

Calculation:

The average number of strikeouts and the average number of home runs per innings pitched for Pitcher A are 0.91 and 0.16, respectively.

From Part (c), it is found that estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP

Thus, using the given information, the predicted average number of runs given up per inning for Pitcher At is calculated below.

R/IP^=0.5365−0.2483(0.91)+1.032(0.16)=0.5365−0.225953+0.16512≈0.4757

Thus, the predicted average number of runs given up per inning for Pitcher A is 0.4757.

It is to be noted that the actual average number of runs given up per inning for Pitcher A was 0.6.

Thus, the predicted value is slightly less than the actual value. Hence, the residual is positive, which underestimates the model slightly.

e.

Expert Solution

To determine

Explain about the suggestion to use the earned run average as another independent variable in the regression model developed in Part c.

Explanation of Solution

Calculation:

It would be useful to use the earned run average or ERA as a third independent variable, only if it has a strong linear association with the response variable, R/IP. The correlation coefficient measures the strength of the linear relation between two variables.

Software procedure:

Step-by-step procedure to get correlation coefficient using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
In cell I1 enter the formula =CORREL(F1:F21,C1:C21).
Press Enter.

The EXCEL output is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 4

Thus, the correlation coefficient between R/IO and ERA is approximately 0.964, which is very close to 1, indicating a very strong positive linear relationship between the variables.

Due to the high correlation coefficient it can be said that if a pitcher gives up more runs per inning, then the pitcher’s run average also increases. Thus, automatically there will be high value of the coefficient of determination in the regression analysis, if ERA is used as a predictor variable.

Thus, it is reasonable to use the earned run average as another independent variable in the regression model developed in Part c.

Answer 8

a.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Explanation of Solution

Calculation:

The Major League Baseball (MLB) dataset consist of statistics regarding the average number of earned runs given up by the pitcher per nine innings (ERA), the average number of strikeouts per innings pitched (SO/IP), the average number of home runs per innings pitched (HR/IP) and the number of runs given up per innings pitched (R/IP).

Multiple linear regression model:

A multiple linear regression model is given as y^=b0+b1x1+...+bpxp where y^ is the predicted value of response or dependent variables, and x1,x2,...,xp are the p predictor variables. The quantities b1,b2,...,bp are the estimated slopes corresponding to x1,x2,...,xp respectively, and b0 is the estimated intercept of the line, from the sample data.

Regression:

Software procedure:

Step-by-step procedure to get the regression equation using EXCEL software:

Open an EXCEL sheet and enter the data on W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$D$21.
Click the box of Labels.
Under Output Range enter $I$1.
Click OK.

The output using EXCEL software is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 1

Thus, the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Answer 9

a.

Expert Solution

Answer 10

a.

Expert Solution

Answer 11

Expert Solution

Answer 12

To determine

Find the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given.

Answer 13

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Answer 14

Explanation of Solution

Calculation:

The Major League Baseball (MLB) dataset consist of statistics regarding the average number of earned runs given up by the pitcher per nine innings (ERA), the average number of strikeouts per innings pitched (SO/IP), the average number of home runs per innings pitched (HR/IP) and the number of runs given up per innings pitched (R/IP).

Multiple linear regression model:

A multiple linear regression model is given as y^=b0+b1x1+...+bpxp where y^ is the predicted value of response or dependent variables, and x1,x2,...,xp are the p predictor variables. The quantities b1,b2,...,bp are the estimated slopes corresponding to x1,x2,...,xp respectively, and b0 is the estimated intercept of the line, from the sample data.

Regression:

Software procedure:

Step-by-step procedure to get the regression equation using EXCEL software:

Open an EXCEL sheet and enter the data on W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$D$21.
Click the box of Labels.
Under Output Range enter $I$1.
Click OK.

The output using EXCEL software is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 1

Thus, the estimated regression equation that could be used to predict the average number of runs given up per inning, when the average number of strikeouts per innings pitched is given is R/IP^=0.6758−0.2838 SO/IP.

Answer 15

b.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $E$1:$E$21.
Click the box of Labels.
Under Output Range enter $L$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 2

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Answer 16

b.

Expert Solution

Answer 17

b.

Expert Solution

Answer 18

Expert Solution

Answer 19

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched.

Answer 20

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Answer 21

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $E$1:$E$21.
Click the box of Labels.
Under Output Range enter $L$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 2

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of home runs per innings pitched is R/IP^=0.3081+1.347 HR/IP.

Answer 22

c.

Expert Solution

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched.

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$E$21.
Click the box of Labels.
Under Output Range enter $O$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 3

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Answer 23

c.

Expert Solution

Answer 24

c.

Expert Solution

Answer 25

Expert Solution

Answer 26

To determine

Find the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched.

Answer 27

Answer to Problem 10E

The estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Answer 28

Explanation of Solution

Calculation:

The regression equation can be obtained using EXCEL software.

Software procedure:

Step-by-step procedure to get regression equation using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
Select Data > Data Analysis > Regression.
Click OK.
Under Input Y Range enter $F$1:$F$21.
Under Input X Range enter $D$1:$E$21.
Click the box of Labels.
Under Output Range enter $O$1.
Click OK.

The output using EXCEL software is given as,

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 3

Thus, the estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP.

Answer 29

d.

Expert Solution

To determine

Predict the average number of runs given up per inning for Pitcher A using the regression equation of Part c.

Answer to Problem 10E

The predicted average number of runs given up per inning for Pitcher A is 0.4757.

Explanation of Solution

Calculation:

The average number of strikeouts and the average number of home runs per innings pitched for Pitcher A are 0.91 and 0.16, respectively.

From Part (c), it is found that estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP

Thus, using the given information, the predicted average number of runs given up per inning for Pitcher At is calculated below.

R/IP^=0.5365−0.2483(0.91)+1.032(0.16)=0.5365−0.225953+0.16512≈0.4757

Thus, the predicted average number of runs given up per inning for Pitcher A is 0.4757.

It is to be noted that the actual average number of runs given up per inning for Pitcher A was 0.6.

Thus, the predicted value is slightly less than the actual value. Hence, the residual is positive, which underestimates the model slightly.

Answer 30

d.

Expert Solution

Answer 31

d.

Expert Solution

Answer 32

Expert Solution

Answer 33

To determine

Predict the average number of runs given up per inning for Pitcher A using the regression equation of Part c.

Answer 34

Answer to Problem 10E

The predicted average number of runs given up per inning for Pitcher A is 0.4757.

Answer 35

Explanation of Solution

Calculation:

The average number of strikeouts and the average number of home runs per innings pitched for Pitcher A are 0.91 and 0.16, respectively.

From Part (c), it is found that estimated regression equation that could be used to predict the average number of runs given the average number of strikeouts per innings pitched and the average number of home runs per innings pitched is R/IP^=0.5365−0.2483 SO/IP+1.032 HR/IP

Thus, using the given information, the predicted average number of runs given up per inning for Pitcher At is calculated below.

R/IP^=0.5365−0.2483(0.91)+1.032(0.16)=0.5365−0.225953+0.16512≈0.4757

Thus, the predicted average number of runs given up per inning for Pitcher A is 0.4757.

It is to be noted that the actual average number of runs given up per inning for Pitcher A was 0.6.

Thus, the predicted value is slightly less than the actual value. Hence, the residual is positive, which underestimates the model slightly.

Answer 36

e.

Expert Solution

To determine

Explain about the suggestion to use the earned run average as another independent variable in the regression model developed in Part c.

Explanation of Solution

Calculation:

It would be useful to use the earned run average or ERA as a third independent variable, only if it has a strong linear association with the response variable, R/IP. The correlation coefficient measures the strength of the linear relation between two variables.

Software procedure:

Step-by-step procedure to get correlation coefficient using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
In cell I1 enter the formula =CORREL(F1:F21,C1:C21).
Press Enter.

The EXCEL output is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 4

Thus, the correlation coefficient between R/IO and ERA is approximately 0.964, which is very close to 1, indicating a very strong positive linear relationship between the variables.

Due to the high correlation coefficient it can be said that if a pitcher gives up more runs per inning, then the pitcher’s run average also increases. Thus, automatically there will be high value of the coefficient of determination in the regression analysis, if ERA is used as a predictor variable.

Thus, it is reasonable to use the earned run average as another independent variable in the regression model developed in Part c.

Answer 37

e.

Expert Solution

Answer 38

e.

Expert Solution

Answer 39

Expert Solution

Answer 40

To determine

Explain about the suggestion to use the earned run average as another independent variable in the regression model developed in Part c.

Answer 41

Explanation of Solution

Calculation:

It would be useful to use the earned run average or ERA as a third independent variable, only if it has a strong linear association with the response variable, R/IP. The correlation coefficient measures the strength of the linear relation between two variables.

Software procedure:

Step-by-step procedure to get correlation coefficient using EXCEL software:

Open an EXCEL sheet and enter the data of W, L, ERA, SO/IP, HR/IP and R/IP corresponding to columns A, B, C, D, E and F, respectively.
In cell I1 enter the formula =CORREL(F1:F21,C1:C21).
Press Enter.

The EXCEL output is given as follows:

Essentials of Statistics for Business and Economics (with XLSTAT Printed Access Card), Chapter 15.2, Problem 10E , additional homework tip 4

Thus, the correlation coefficient between R/IO and ERA is approximately 0.964, which is very close to 1, indicating a very strong positive linear relationship between the variables.

Due to the high correlation coefficient it can be said that if a pitcher gives up more runs per inning, then the pitcher’s run average also increases. Thus, automatically there will be high value of the coefficient of determination in the regression analysis, if ERA is used as a predictor variable.

Thus, it is reasonable to use the earned run average as another independent variable in the regression model developed in Part c.