Homework Help is Here – Start Your Trial Now!
Math
Statistics
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 1.6 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) 91 76 O 79 146 81 184 9.2 219 9.6 242 Weight (kg) 220 133 Click the icon to view the critical values of the Pearson correlation coefficient r CO The regression equation is y=+x (Round to one decimal place as needed) The best predicted weight for an overhead width of 16 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? OA. 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 OB. 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 a negative weight does not make sense 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.

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 1.6 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) 91 76 O 79 146 81 184 9.2 219 9.6 242 Weight (kg) 220 133 Click the icon to view the critical values of the Pearson correlation coefficient r CO The regression equation is y=+x (Round to one decimal place as needed) The best predicted weight for an overhead width of 16 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? OA. 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 OB. 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 a negative weight does not make sense 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.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section: Chapter Questions
Problem 4SGR
See similar textbooks
icon
Related questions
Question
100%

Please help.
Thanks so much 

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 1.6 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) 7.9 9.1 9.6 8.1 184 7.6 133 9.2 219 Weight (kg) 146 220 242 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y=+x (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.6 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? OA. 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. OB. 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 a negative weight does not make sense. 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.
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 1.6 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) 7.9 9.1 9.6 8.1 184 7.6 133 9.2 219 Weight (kg) 146 220 242 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y=+x (Round to one decimal place as needed.) The best predicted weight for an overhead width of 1.6 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? OA. 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. OB. 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 a negative weight does not make sense. 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.
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 2 steps with 2 images

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
Glencoe Algebra 1, Student Edition, 9780079039897…
Glencoe Algebra 1, Student Edition, 9780079039897…
Algebra
ISBN:
9780079039897
Author:
Carter
Publisher:
McGraw Hill
SEE MORE TEXTBOOKS