\fThe accompanying Major League Baseball data provide data for one season. Use the data to build a multiple regression model that predicts the number of wins. Complete parts a through c. :Click the icon to view the Major League Baseball data. a. Construct and examine the correlation matrix. Is multicallinearity a potential problem? Won Runs Hita Earned Run | Strike Average Outs Walks Won Rung 0.785 Hits 0.446 0.684 Earned Run 0.682 - 0.163 0.088 Average Strike Outs - 0.232 0.001 - 0.417 0.187 Walks 0.533 0.678 0.207 -0.241 0.358 (Type integers or decimals rounded to three decimal places as needed.) Is multicollinearity a potential problem? No, because none of the pairs of independent variables have correlation coefficients greater than 0.7. b. Suggest an appropriate set of independent variables that predict the number of wins by examining the correlation matrix. Use a critical value of 0.361 for the correlation coefficient. Select all that apply. A. Earned Run Average Y B. Hits C. Strike Outs LYD. Runs YE Walks C. Find the best multiple regression model for predicting the number of wins having only significant independent variables. How good is your model? Does it use the same variables you thought were appropriate in part b? Use a level of significance of 0.05. Determine the best multiple regr sion model. Let X, represent Runs, let X2 represent Hits, let X3 represent Earned Run Average, let X, represent Strike Outs, and let X, represent Walks. Enter the terms of the equation so that the X,-values are in ascending numeral order by base. Select the correct choice below and fil in the answer boxes within your choice. (Type integers or decimals rounded to three decimal places as needed.) OD. Won = +(DX