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 2.3 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) 7.7 7.5 8.8 8.2 7.6 9.4 Weight (kg) 146 167 215 165 161 242 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 2.3 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 there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OC. 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 D. 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 2.3 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) 7.7 7.5 8.8 8.2 7.6 9.4 Weight (kg) 146 167 215 165 161 242 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 2.3 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 there is not sufficient evidence of a linear correlation. The width in this case is beyond the scope of the available sample data. OC. 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 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
Related questions
Question
Transcribed Image Text:Critical values of the pearson correlation coefficient r
Critical Values of the Pearson Correlation Coefficientr
x = 0.05
x = 0.01
INOTE: To test Ho: p=0
n
Jagainst H,: p#0, reject Ho
if the absolute value of r is
greater than the critical
value in the table.
4
0.950
0.990
0.878
0.959
0.811
0.917
7
0.754
0.875
8
0.707
0.834
9.
0.666
0.798
10
0.632
0.765
11
0.602
0.735
12
0.576
0.708
13
0.553
0.684
14
0.532
0.661
15
0.514
0.641
16
0.497
0.623
17
0.482
0.606
18
0.468
0.590
19
0.456
0.575
20
0.444
0.561
25
0.396
0.505
30
0.361
0.463
35
0.335
0.430
40
0.312
0.402
45
0.294
0.378
50
0.279
0.361
60
0.254
0.330
70
0.236
0.305
80
0.220
0.286
90
0.207
0.269
100
0.196
0.256
a = 0.05
X = 0.01
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 2.3 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)
7.7
7.5
8.8
8.2
7.6
9.4
Weight (kg)
146
167
215
165
161
242
Click the icon to view the critical values of the Pearson correlation coefficient r.
The regression equation is y = + x
X.
(Round to one decimal place as needed.)
The best predicted weight for an overhead width of 2.3 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.
B. 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.
C. The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation.
D. The prediction can be correct. There is nothing wrong with predicting the weight in this case.
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