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The 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 multicollinearity a potential problem? Won Runs Hits Earned Run Strike Average Outs Walks Won 1.000 Runs 0.785 1.000 Hits 0.446 0.686 1.000 Earned Run -0.682 -0.163 0.088 1.000 Average Strike Outs -0.237 -0.003 -0.416 0.190 1.000 Walks 0.533 0.675 0.206 -0.241 0.353 1.000 (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. Hits Earned Run Average c. Walks D. Runs E. Strike Outs 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 regression model. Let X₁ represent Runs, let X2 represent Hits, let X3 represent Earned Run Average, let X4 represent Strike Outs, and let X5 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 fill in the answer boxes within your choice. (Type integers or decimals rounded to three decimal places as needed.) A. Won 76.537 + (0.094) X 15.1613 -0.011) B. Won= + OC. Won = + × + ( ) × + ( ) ×+(x+(x

The 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 multicollinearity a potential problem? Won Runs Hits Earned Run Strike Average Outs Walks Won 1.000 Runs 0.785 1.000 Hits 0.446 0.686 1.000 Earned Run -0.682 -0.163 0.088 1.000 Average Strike Outs -0.237 -0.003 -0.416 0.190 1.000 Walks 0.533 0.675 0.206 -0.241 0.353 1.000 (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. Hits Earned Run Average c. Walks D. Runs E. Strike Outs 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 regression model. Let X₁ represent Runs, let X2 represent Hits, let X3 represent Earned Run Average, let X4 represent Strike Outs, and let X5 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 fill in the answer boxes within your choice. (Type integers or decimals rounded to three decimal places as needed.) A. Won 76.537 + (0.094) X 15.1613 -0.011) B. Won= + OC. Won = + × + ( ) × + ( ) ×+(x+(x

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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The 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 multicollinearity a potential problem? Won Runs Hits Earned Run Strike Average Outs Walks Won 1.000 Runs 0.785 1.000 Hits 0.446 0.686 1.000 Earned Run -0.682 -0.163 0.088 1.000 Average Strike Outs -0.237 -0.003 -0.416 0.190 1.000 Walks 0.533 0.675 0.206 -0.241 0.353 1.000 (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. Hits Earned Run Average c. Walks D. Runs E. Strike Outs 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 regression model. Let X₁ represent Runs, let X2 represent Hits, let X3 represent Earned Run Average, let X4 represent Strike Outs, and let X5 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 fill in the answer boxes within your choice. (Type integers or decimals rounded to three decimal places as needed.) A. Won 76.537 + (0.094) X 15.1613 -0.011) B. Won= + OC. Won = + × + ( ) × + ( ) ×+(x+(x
Transcribed Image Text:The 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 multicollinearity a potential problem? Won Runs Hits Earned Run Strike Average Outs Walks Won 1.000 Runs 0.785 1.000 Hits 0.446 0.686 1.000 Earned Run -0.682 -0.163 0.088 1.000 Average Strike Outs -0.237 -0.003 -0.416 0.190 1.000 Walks 0.533 0.675 0.206 -0.241 0.353 1.000 (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. Hits Earned Run Average c. Walks D. Runs E. Strike Outs 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 regression model. Let X₁ represent Runs, let X2 represent Hits, let X3 represent Earned Run Average, let X4 represent Strike Outs, and let X5 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 fill in the answer boxes within your choice. (Type integers or decimals rounded to three decimal places as needed.) A. Won 76.537 + (0.094) X 15.1613 -0.011) B. Won= + OC. Won = + × + ( ) × + ( ) ×+(x+(x
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