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Can the prediction be correct? If not, what is wrong?

Can the prediction be correct? If not, what is wrong?

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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Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 132 7.4 170 9.8 268 9.4 224 8.9 225 8.4 209 Q The regression equation is y=-162+ (43.1)x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) The best predicted weight for an overhead width of 2.1 cm, based on the regression equation, is -71.5 kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? OA. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. OB. The prediction cannot be correct because a weight of zero does not make sense and because there is not sufficient evidence of a linear correlation. OC. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OD. The prediction can be correct.
Transcribed Image Text:Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 7.2 132 7.4 170 9.8 268 9.4 224 8.9 225 8.4 209 Q The regression equation is y=-162+ (43.1)x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) The best predicted weight for an overhead width of 2.1 cm, based on the regression equation, is -71.5 kg. (Round to one decimal place as needed.) Can the prediction be correct? If not, what is wrong? OA. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. OB. The prediction cannot be correct because a weight of zero does not make sense and because there is not sufficient evidence of a linear correlation. OC. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OD. The prediction can be correct.
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