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uring a season in order to restock the lake with the appropriate number of young fish. The ommission could get a fairly accurate assessment of the seasonal catch by extensive “netting veeps" of the lake before and after a season, but this technique is much too expensive to be one routinely. Therefore, the commission samples a number of lakes and record the seasonal tch (thousands of bass per square mile of lake area) and size of lake (square miles). A simple ear regression was performed and the following R output obtained. Estimate Std. Error t value Pr(>|t|) Intercept) 2.5463 0.4427 5.7513 0.0000 ize 0.0667 0.3672 0.1818 0.8578 uppose a natural log transformation on seasonal catch was deemed necessary. Below is the ast-squares regression equation: In (ŷ) = 0.76 + 0.0914x. redict the seasonal catch for the lake with lake area of 2.5 square miles. Round your answer one decimal place. thousand

uring a season in order to restock the lake with the appropriate number of young fish. The ommission could get a fairly accurate assessment of the seasonal catch by extensive “netting veeps" of the lake before and after a season, but this technique is much too expensive to be one routinely. Therefore, the commission samples a number of lakes and record the seasonal tch (thousands of bass per square mile of lake area) and size of lake (square miles). A simple ear regression was performed and the following R output obtained. Estimate Std. Error t value Pr(>|t|) Intercept) 2.5463 0.4427 5.7513 0.0000 ize 0.0667 0.3672 0.1818 0.8578 uppose a natural log transformation on seasonal catch was deemed necessary. Below is the ast-squares regression equation: In (ŷ) = 0.76 + 0.0914x. redict the seasonal catch for the lake with lake area of 2.5 square miles. Round your answer one decimal place. thousand

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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A state fisheries commission wants to estimate the number of bass caught in a given lake during a season in order to restock the lake with the appropriate number of young fish. The commission could get a fairly accurate assessment of the seasonal catch by extensive “netting sweeps" of the lake before and after a season, but this technique is much too expensive to be done routinely. Therefore, the commission samples a number of lakes and record the seasonal catch (thousands of bass per square mile of lake area) and size of lake (square miles). A simple linear regression was performed and the following R output obtained. Estimate Std. Error t value Pr(>|t|) (Intercept) 2.5463 0.4427 5.7513 0.0000 size 0.0667 0.3672 0.1818 0.8578 Suppose a natural log transformation on seasonal catch was deemed necessary. Below is the least-squares regression equation: In (ý) = 0.76 + 0.0914x. Predict the seasonal catch for the lake with lake area of 2.5 square miles. Round your answer to one decimal place. thousand
Transcribed Image Text:A state fisheries commission wants to estimate the number of bass caught in a given lake during a season in order to restock the lake with the appropriate number of young fish. The commission could get a fairly accurate assessment of the seasonal catch by extensive “netting sweeps" of the lake before and after a season, but this technique is much too expensive to be done routinely. Therefore, the commission samples a number of lakes and record the seasonal catch (thousands of bass per square mile of lake area) and size of lake (square miles). A simple linear regression was performed and the following R output obtained. Estimate Std. Error t value Pr(>|t|) (Intercept) 2.5463 0.4427 5.7513 0.0000 size 0.0667 0.3672 0.1818 0.8578 Suppose a natural log transformation on seasonal catch was deemed necessary. Below is the least-squares regression equation: In (ý) = 0.76 + 0.0914x. Predict the seasonal catch for the lake with lake area of 2.5 square miles. Round your answer to one decimal place. thousand
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