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.The regression equation isy=___+___x(Round to one decimal place as needed.)The best predicted weight for an overhead width of2.3cm is ___kg. (Round to one decimal place as needed.) Can the prediction be correct? What is 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)7.57.48.78.29.69.9Weight (kg)144170219171252272Click the icon to view the critical values of the Pearson correlation coefficient r.Critical Values of the Pearson Correlation Coefficient rThe regression equation is y =+x.(Round to one decimal place as needed.)Critical Values of the Pearson Correlation Coefficient rNOTE: To test Ho: p=0against H,: p# 0, reject Hif the absolute value of r isgreater than the criticalvalue in the table.Ina = 0.050.9500.8780.8110.7540.7070.6680.6320.6020.5760.5530.5320.5140.4970.4820.4880.4560.4440.3980.3610.3350.3120.2940.2790.2540.2360.2200.2070.196a = 0.05a = 0.010.9900.9500.9170.8750.8340.7080.7650.7350.7080.6840.6610.6410.6230.8060.5900.5750.5610.5050.4830.4300.4020.3780.3610.3300.3050.2880.2690.256a = 0.01The best predicted weight for an overhead width of 2.3 cm is kg.14(Round to one decimal place as needed.)15Can the prediction be correct? What is wrong with predicting the weight in this case?1817O 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.1011121314O 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.O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case15161718192025303540455080708090100In

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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.

The regression equation is

y=___+___x

(Round to one decimal place as needed.)

The best predicted weight for an overhead width of

2.3cm is ___kg.

(Round to one decimal place as needed.)

Can the prediction be correct? What is 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)
7.5
7.4
8.7
8.2
9.6
9.9
Weight (kg)
144
170
219
171
252
272
Click the icon to view the critical values of the Pearson correlation coefficient r.
Critical Values of the Pearson Correlation Coefficient r
The regression equation is y =+x.
(Round to one decimal place as needed.)
Critical Values of the Pearson Correlation Coefficient r
NOTE: To test Ho: p=0
against H,: p# 0, reject H
if the absolute value of r is
greater than the critical
value in the table.
In
a = 0.05
0.950
0.878
0.811
0.754
0.707
0.668
0.632
0.602
0.576
0.553
0.532
0.514
0.497
0.482
0.488
0.456
0.444
0.398
0.361
0.335
0.312
0.294
0.279
0.254
0.236
0.220
0.207
0.196
a = 0.05
a = 0.01
0.990
0.950
0.917
0.875
0.834
0.708
0.765
0.735
0.708
0.684
0.661
0.641
0.623
0.806
0.590
0.575
0.561
0.505
0.483
0.430
0.402
0.378
0.361
0.330
0.305
0.288
0.269
0.256
a = 0.01
The best predicted weight for an overhead width of 2.3 cm is kg.
14
(Round to one decimal place as needed.)
15
Can the prediction be correct? What is wrong with predicting the weight in this case?
18
17
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.
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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 of the available sample data.
O D. The prediction can be correct. There is nothing wrong with predicting the weight in this case
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