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)
 8.4  7.8  9.4  8.8  8.2  8.9 


Weight (kg)
 178  183  245  193  190  225 
Critical Values of the Parson Correlation Coefficient r
n
 α=0.05
 α=0.01
 NOTE: To test H0: p=0 against H1: ρ≠0, reject H0 if the absolute value of r is greater than the critical value in the table.


4
 0.950
 0.990
 
5
 0.878
 0.959
 
6
 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
 
n
 α=0.05
 α=0.01

The regression equation is
y= 141.0 + 40.0x
(Round to one decimal place as needed.)
Part 2
The best predicted weight for an overhead width of
2.4 cm is ______ kg.
(Round to one decimal place as needed.)
Part 3
Can the prediction be correct? What is wrong with predicting the weight in this case?
A.
The prediction cannot be correct because a negative weight does not make sense and because there is not sufficient evidence of a linear correlation.
B.
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.
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 of the available sample data.
D.
The prediction can be correct. There is nothing wrong with predicting the weight in this case.
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