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

1.7

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	=0.05	=0.01	NOTE: To test H0: p=0 against H1: 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	=0.05	=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.