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

Hey, since there are multiple subparts posted, we will answer the first three subparts. If you want any specific subpart to be answered then please submit that subpart only or specify the subpart in your message. Given: Dependent variable = Number games won(wins) 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) correlation matrix Procedure for correlation matrix in excel: Step 1: Enter the data. Step 2: Select the Data tab. Step 3: Select Data analysis. Step 4: Select the correlation. Step 5: Select by columns in the Grouped section. Step 6: Enter the range of data. The correlation matrix is given below BA ERA League HR Wins BA 1 ERA -0.4248 1 League 0.184303 0.24187 1 HR 0.332278 -0.33829 0.225937 1 Wins 0.66438 -0.87343 -0.03916 0.540622 1 i) Batting average has a stronger positive correlation (0.66438) with the dependent variable (wins). Earned run average has a strong negative correlation (-0.87343) with the dependent variable (wins). ii) The batting average and the Earned run average have multicollinearity as their correlation value is (-0.4248). b) Multiple linear regression model Procedure for multiple linear regression model in excel: Step 1: Enter the data. Step 2: Select the Data tab. Step 3: Select Data analysis. Step 4: Select the Regression. Step 5: Select by labels, residuals, standardized residuals. The output is given below Regression Statistics Multiple R 0.951732 R Square 0.905794 Adjusted R Square 0.890721 Standard Error 4.864992 Observations 30 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 22.06117 34.2046 0.644977 0.524817 -48.3845 92.50686 -48.3845 92.50685714 BA 494.1202 118.0685 4.18503 0.000308 250.9536 737.2869 250.9536 737.2868921 ERA -19.6055 2.2074 -8.88173 3.32E-09 -24.1518 -15.0593 -24.1518 -15.05930404 League 0.688646 2.022367 0.340515 0.736314 -3.4765 4.853788 -3.4765 4.853787928 HR 0.09311 0.031856 2.922852 0.007262 0.027502 0.158718 0.027502 0.158717998 i) Regression Equation Number games won= 22.06117+(494.1202) Batting average +(-19.6055)Earned run average+ (0.688646)League+(0.09311)number of home runs The coefficient in the regression equation implies how much the mean of the dependent variable varies for a one-unit change in the independent variable while the other factors are kept constant. The sign of the coefficient implies whether the independent and dependent are positively correlated or negatively correlated. If the sign is positive, they are positively correlated and if the sign is negative, they are negatively correlated. ii) R-square=0.905794 91% percentage of the model explains the variation in the dependent variable. c) Global test: Ho:β1=β2=β3=β4=0 df SS MS F Significance F Regression 4 5689.263 1422.316 60.09408 1.85E-12 Residual 25 591.7038 23.66815 Total 29 6280.967 since the significance value is approximately zero. the null hypothesis is rejected. It means that the independent variables explain the model well. The regression model with an independent variable fits the data better than the model without the independent variable.