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For items 1 to 6: It is beneficial to be able to estimate the weight of a tree based on measurements of the tree taken before its harvest. Measurements on two variables were taken from a random sample of trees, which were subsequently harvested, and the actual weight (in kg) of the products were determined. The variables measured are trunk diameter at breast height or DBH (measured about 4 feet from ground level; in inches), height of tree (in feet), and embryo type (monocot or dicot). Suppose that the assumptions of linear regression were satisfied. A multiple linear regression model for predicting the weight of tree using diameter at breast height, height of tree, and embryo type was fitted. The software output is summarized below. Use alpha=5%. Intercept DBH height Estimate -1015.442 0.983 164.008 9.183 type_monocot -161.278 Multiple R-squared Std. Error 141.622 39.626 5.486 88.177 Adjusted R-squared 0.9703 tc -7.170 4.139 1.674 -1.829 p-value 0.0005383 0.0020 0.0144 0.1695 0.1414 p-value (model)

For items 1 to 6: It is beneficial to be able to estimate the weight of a tree based on measurements of the tree taken before its harvest. Measurements on two variables were taken from a random sample of trees, which were subsequently harvested, and the actual weight (in kg) of the products were determined. The variables measured are trunk diameter at breast height or DBH (measured about 4 feet from ground level; in inches), height of tree (in feet), and embryo type (monocot or dicot). Suppose that the assumptions of linear regression were satisfied. A multiple linear regression model for predicting the weight of tree using diameter at breast height, height of tree, and embryo type was fitted. The software output is summarized below. Use alpha=5%. Intercept DBH height Estimate -1015.442 0.983 164.008 9.183 type_monocot -161.278 Multiple R-squared Std. Error 141.622 39.626 5.486 88.177 Adjusted R-squared 0.9703 tc -7.170 4.139 1.674 -1.829 p-value 0.0005383 0.0020 0.0144 0.1695 0.1414 p-value (model)

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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For items 1 to 6: It is beneficial to be able to estimate the weight of a tree based on measurements of the tree taken before its harvest. Measurements on two variables were taken from a random sample of trees, which were subsequently harvested, and the actual weight (in kg) of the products were determined. The variables measured are trunk diameter at breast height or DBH (measured about 4 feet from ground level; in inches), height of tree (in feet), and embryo type (monocot or dicot). Suppose that the assumptions of linear regression were satisfied. A multiple linear regression model for predicting the weight of tree using diameter at breast height, height of tree, and embryo type was fitted. The software output is summarized below. Use alpha=5%. Intercept DBH height Estimate -1015.442 0.983 164.008 9.183 type_monocot -161.278 Multiple R-squared Std. Error 141.622 39.626 5.486 88.177 Adjusted R-squared 0.9703 tc -7.170 4.139 1.674 -1.829 p-value 0.0005383 0.0020 0.0144 0.1695 0.1414 p-value (model)
Transcribed Image Text:For items 1 to 6: It is beneficial to be able to estimate the weight of a tree based on measurements of the tree taken before its harvest. Measurements on two variables were taken from a random sample of trees, which were subsequently harvested, and the actual weight (in kg) of the products were determined. The variables measured are trunk diameter at breast height or DBH (measured about 4 feet from ground level; in inches), height of tree (in feet), and embryo type (monocot or dicot). Suppose that the assumptions of linear regression were satisfied. A multiple linear regression model for predicting the weight of tree using diameter at breast height, height of tree, and embryo type was fitted. The software output is summarized below. Use alpha=5%. Intercept DBH height Estimate -1015.442 0.983 164.008 9.183 type_monocot -161.278 Multiple R-squared Std. Error 141.622 39.626 5.486 88.177 Adjusted R-squared 0.9703 tc -7.170 4.139 1.674 -1.829 p-value 0.0005383 0.0020 0.0144 0.1695 0.1414 p-value (model)
5. Which of the following is(are) TRUE? 1. There is no need to test the significance of the individual regression coefficients. II. The variation in the weight that is explained by diameter at breast height, height of tree, and embryo type is 98.3% while the remaining percent is explained by other factors not considered in the model. O A. I only O B. II only O C. Both I and II O D. Neither I nor II
Transcribed Image Text:5. Which of the following is(are) TRUE? 1. There is no need to test the significance of the individual regression coefficients. II. The variation in the weight that is explained by diameter at breast height, height of tree, and embryo type is 98.3% while the remaining percent is explained by other factors not considered in the model. O A. I only O B. II only O C. Both I and II O D. Neither I nor II
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