Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 8.1 Weight (kg) 8.3 218 9.5 264 8.8 207 9.4 8.9 O 240 179 255 9 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =D•D. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.7 cm is kg (Round to one decimal place as needed.) Can the prediction I correct? What is wrong with predicting the weight in this case? O A. 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. O B. The prediction cannot be correct because a negative weight 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. There is nothing wrong with predicting the weight in this case.

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6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05.
Overhead Width (cm)
Weight (kg)
8.1
8.3
9.5
8.8
9.4
8.9
179
218
264
207
255
240
Click the icon to view the critical values of the Pearson correlation coefficient r.
The regression equation is y =O+x.
(Round to one decimal place as needed.)
The best predicted weight for an overhead width of 1.7 cm is kg.
(Round to one decimal place as needed.)
Can the prediction be correct? What is wrong with predicting the weight in this case?
O A. 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.
O B. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation.
O C. 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
available sample data.
O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.
Transcribed Image Text:Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) 8.1 8.3 9.5 8.8 9.4 8.9 179 218 264 207 255 240 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =O+x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.7 cm is kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. 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. O B. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation. O C. 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 available sample data. O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.
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