A study was done to compare tree height with trunk thickness. The following output was generated from the regression model. Simple linear regression results: Dependent Variable: Tree Height Independent Variable: Trunk Size Height = 39.041668 + 8.668677 Trunk Sample size: 25 R (correlation coefficient) = 0.4418 R-sq = 0.1952 Estimate of error standard deviation: 10.09977 Parameter estimates: Parameter Estimate Std. Err. Intercept 39.041668 15.1736 Trunk 8.668677 3.670058 Assuming the conditions are met test if trunk size is a good predictor of tree height
A study was done to compare tree height with trunk thickness. The following output was generated from the regression model. Simple linear regression results: Dependent Variable: Tree Height Independent Variable: Trunk Size Height = 39.041668 + 8.668677 Trunk Sample size: 25 R (correlation coefficient) = 0.4418 R-sq = 0.1952 Estimate of error standard deviation: 10.09977 Parameter estimates: Parameter Estimate Std. Err. Intercept 39.041668 15.1736 Trunk 8.668677 3.670058 Assuming the conditions are met test if trunk size is a good predictor of tree height
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 8E
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A study was done to compare tree height with trunk thickness. The following output was generated from the regression model.
Simple linear regression results:
Dependent Variable: Tree Height
Independent Variable: Trunk Size
Height = 39.041668 + 8.668677 Trunk
R (
R-sq = 0.1952
Estimate of error standard deviation: 10.09977
Parameter estimates:
Parameter | Estimate | Std. Err. |
Intercept | 39.041668 | 15.1736 |
Trunk | 8.668677 | 3.670058 |
Assuming the conditions are met test if trunk size is a good predictor of tree height
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