Refer to the Baseball 2018 data given below, which report information on the 30 Major League Baseball teams for the 2018 season. Let the number of games won be the dependent variable and the following variables be independent variables: team batting average, team earned run average (ERA), number of home runs and whether the team plays in the American or National league (American League is 1 and National League is 0).a. Develop a correlation matrix. (i) Which independent variables have strong or weak correlations with the dependent variable. (ii) Do you see any problems with multicollinearity? Explain your answer.b. Use Excel to determine the multiple regression equation. (i) Write out the regression equation and determine its practical application (i.e., interpret the equation). (ii) Report and interpret the R-square.c. Conduct a global test on the set of independent variables. Interpret.d. Conduct a test of hypothesis on each of the independent variables. Would you consider deleting any of the variables? (i) If so, which ones?(ii) If so, what is your new equation?e. Develop a histogram of the residuals from the final regression equation developed in part (d-ii). Is it reasonable to conclude that the normality assumption has been met? Why or Why not?f. Plot the residuals against the fitted values from the final regression equation developed in part (d-ii). Plot the residuals on the vertical axis and the fitted values on the horizontal axis. What regression assumption is supported? Why is it supported? Certainly! The table presents data on Major League Baseball teams for the year 2018. Here's the transcription and explanation:**Table Description:**- **Columns:** - **Team:** Name of the baseball team. - **League:** Indicates whether the team is in the National or American league. - **Team Salary ($ mil):** Total salary expenditure for the team in millions of dollars. - **HR (Home Runs):** Total number of home runs scored by the team. - **BA (Batting Average):** The batting average, a statistical measure of a batter's performance. - **Wins:** Total number of games won by the team. - **ERA (Earned Run Average):** The average number of earned runs given up by a pitcher per nine innings pitched. - **Year Stadium Opened:** The year the team’s home stadium was opened. - **Attendance (mil):** Total number of spectators attending the games, measured in millions. - **Net Worth ($ bil):** The estimated value of the team in billions of dollars.**Teams:**1. Arizona Diamondbacks: National, $143.32 mil, HR 176, BA 0.235, Wins 82, ERA 3.72, Stadium 1998, Attendance 2.242695 mil, Net Worth $1.21 bil.2. Atlanta Braves: National, $130.6 mil, HR 175, BA 0.257, Wins 90, ERA 3.75, Stadium 2017, Attendance 2.555781 mil, Net Worth $1.625 bil.3. Baltimore Orioles: American, $127.63 mil, HR 188, BA 0.239, Wins 47, ERA 5.18, Stadium 1992, Attendance 1.564912 mil, Net Worth $1.2 bil.4. Boston Red Sox: American, $227.4 mil, HR 208, BA 0.268, Wins 108, ERA 3.75, Stadium 1912, Attendance 2.895575 mil, Net Worth $2.8 bil.5. Chicago Cubs: National, $194.26 mil, HR 167, BA 0.258, Wins 95, ERA 3.65, Stadium 1914, Attendance 3.181089 mil, Net Worth $2.9 bil.6

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Refer to the Baseball 2018 data given below, which report information on the 30 Major League Baseball teams for the 2018 season. Let the number of games won be the dependent variable and the following variables be independent variables: team batting average, team earned run average (ERA), number of home runs and whether the team plays in the American or National league (American League is 1 and National League is 0). a. Develop a correlation matrix. (i) Which independent variables have strong or weak correlations with the dependent variable. (ii) Do you see any problems with multicollinearity? Explain your answer. b. Use Excel to determine the multiple regression equation. (i) Write out the regression equation and determine its practical application (i.e., interpret the equation). (ii) Report and interpret the R-square. c. Conduct a global test on the set of independent variables. Interpret. d. Conduct a test of hypothesis on each of the independent variables. Would you consider deleting any of the variables? (i) If so, which ones? (ii) If so, what is your new equation? e. Develop a histogram of the residuals from the final regression equation developed in part (d-ii). Is it reasonable to conclude that the normality assumption has been met? Why or Why not? f. Plot the residuals against the fitted values from the final regression equation developed in part (d-ii). Plot the residuals on the vertical axis and the fitted values on the horizontal axis. What regression assumption is supported? Why is it supported?

MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

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Refer to the Baseball 2018 data given below, which report information on the 30 Major League Baseball teams for the 2018 season. Let the number of games won be the dependent variable and the following variables be independent variables: team batting average, team earned run average (ERA), number of home runs and whether the team plays in the American or National league (American League is 1 and National League is 0).

a. Develop a correlation matrix.

(i) Which independent variables have strong or weak correlations with the dependent variable.

(ii) Do you see any problems with multicollinearity? Explain your answer.

b. Use Excel to determine the multiple regression equation.

(i) Write out the regression equation and determine its practical application (i.e., interpret the equation).

(ii) Report and interpret the R-square.

c. Conduct a global test on the set of independent variables. Interpret.

d. Conduct a test of hypothesis on each of the independent variables. Would you consider deleting any of the variables?

(i) If so, which ones?

(ii) If so, what is your new equation?

e. Develop a histogram of the residuals from the final regression equation developed in part (d-ii). Is it reasonable to conclude that the normality assumption has been met? Why or Why not?

f. Plot the residuals against the fitted values from the final regression equation developed in part (d-ii). Plot the residuals on the vertical axis and the fitted values on the horizontal axis. What regression assumption is supported? Why is it supported?

Certainly! The table presents data on Major League Baseball teams for the year 2018. Here's the transcription and explanation:

**Table Description:**

- **Columns:**

- **Team:** Name of the baseball team.
- **League:** Indicates whether the team is in the National or American league.
- **Team Salary ($ mil):** Total salary expenditure for the team in millions of dollars.
- **HR (Home Runs):** Total number of home runs scored by the team.
- **BA (Batting Average):** The batting average, a statistical measure of a batter's performance.
- **Wins:** Total number of games won by the team.
- **ERA (Earned Run Average):** The average number of earned runs given up by a pitcher per nine innings pitched.
- **Year Stadium Opened:** The year the team’s home stadium was opened.
- **Attendance (mil):** Total number of spectators attending the games, measured in millions.
- **Net Worth ($ bil):** The estimated value of the team in billions of dollars.

**Teams:**

1. Arizona Diamondbacks: National, $143.32 mil, HR 176, BA 0.235, Wins 82, ERA 3.72, Stadium 1998, Attendance 2.242695 mil, Net Worth $1.21 bil.
2. Atlanta Braves: National, $130.6 mil, HR 175, BA 0.257, Wins 90, ERA 3.75, Stadium 2017, Attendance 2.555781 mil, Net Worth $1.625 bil.
3. Baltimore Orioles: American, $127.63 mil, HR 188, BA 0.239, Wins 47, ERA 5.18, Stadium 1992, Attendance 1.564912 mil, Net Worth $1.2 bil.
4. Boston Red Sox: American, $227.4 mil, HR 208, BA 0.268, Wins 108, ERA 3.75, Stadium 1912, Attendance 2.895575 mil, Net Worth $2.8 bil.
5. Chicago Cubs: National, $194.26 mil, HR 167, BA 0.258, Wins 95, ERA 3.65, Stadium 1914, Attendance 3.181089 mil, Net Worth $2.9 bil.
6

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