11. 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 2.4 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) 7.4 7.9 9.6 8.6 9.7 9.1 Weight (kg) 130 181 244 177 243 226 4. Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y (Round to one decimal place as needed.) %3D X. The best predicted weight for an overhead width of 2.4 cm is (Round to one decimal place as needed.) kg. Can the prediction be correct? What is wrong with predicting the weight in this case? O A. 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. 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 a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O D. The prediction can be correct. There is nothing wrong with predicting the wejght in this case

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Author:Amos Gilat
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11. 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 2.4 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)
7.4
7.9
9.6
8.6
9.7
9.1
Weight (kg)
130
181
244
177
243
226
4.
Click the icon to view the critical values of the Pearson correlation coefficient r.
The regression equation is y
(Round to one decimal place as needed.)
%3D
X.
The best predicted weight for an overhead width of 2.4 cm is
(Round to one decimal place as needed.)
kg.
Can the prediction be correct? What is wrong with predicting the weight in this case?
O A. 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.
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 a negative weight does not make sense. The width
in this case is beyond the scope of the available sample data.
O D. The prediction can be correct. There is nothing wrong with predicting the wejght in this case
Transcribed Image Text:11. 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 2.4 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) 7.4 7.9 9.6 8.6 9.7 9.1 Weight (kg) 130 181 244 177 243 226 4. Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y (Round to one decimal place as needed.) %3D X. The best predicted weight for an overhead width of 2.4 cm is (Round to one decimal place as needed.) kg. Can the prediction be correct? What is wrong with predicting the weight in this case? O A. 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. 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 a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O D. The prediction can be correct. There is nothing wrong with predicting the wejght in this case
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