6. What is the conclusion if the regression coefficient of the variable diameter at breast height is tested? O A. Weight of tree is dependent on diameter at breast height. O B. Weight of tree is independent with diameter at breast height. O C. Weight of tree is linearly dependent on diameter at breast height. O D. Weight of tree is not linearly dependent on diameter at breast height.

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6. What is the conclusion if the regression coefficient of the variable diameter at breast height is tested?
O A. Weight of tree is dependent on diameter at breast height.
B. Weight of tree is independent with diameter at breast height.
O C. Weight of tree is linearly dependent on diameter at breast height.
O D. Weight of tree is not linearly dependent on diameter at breast height.
Transcribed Image Text:6. What is the conclusion if the regression coefficient of the variable diameter at breast height is tested? O A. Weight of tree is dependent on diameter at breast height. B. Weight of tree is independent with diameter at breast height. O C. Weight of tree is linearly dependent on diameter at breast height. O D. Weight of tree is not linearly dependent on diameter at breast height.
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)
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