Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal ift overhead width measured from a photograph is 1.6 cm. Can the prediction be correct? What is wrong with predicting the we in this case? Use a significance level of 0.05. Overhead Width (cm) 7.8 7.4 9.7 7.3 8.2 9.4 Weight (kg) 153 275 177 190 286 216 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 O 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 there is not sufficient evidence of a linear correlation. The width in this cas 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 sufficien 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 weight in this case.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
icon
Related questions
icon
Concept explainers
Question
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)
7.8
7.4
9.7
7.3
8.2
9.4
Weight (kg)
177
190
286
153
216
275
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?
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.
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 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 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) 7.8 7.4 9.7 7.3 8.2 9.4 Weight (kg) 177 190 286 153 216 275 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? 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. 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 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 weight in this case.
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 3 steps

Blurred answer
Knowledge Booster
Correlation, Regression, and Association
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman