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)8.18.39.58.89.48.9179218264207255240Click 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 ofavailable sample data.O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.

Given data, X Y X*Y X*X 8.1 179 1449.9 65.61 8.3 218 1809.4 68.89 9.5 264 2508 90.25…

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.

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.CR: Chapter Review Exercises

Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...