The accompanying data represent the total compensation for 12 randomly selected chief executive officers (CEOs) and the company's stock performance.
Company Compensation Return
A 14.98 74.48
B 4.61 63.62
C 6.15 148.21
D 1.11 30.35
E 1.54 11.94
F 3.28 29.09
G 11.06 0.64
H 7.77 64.16
I 8.23 50.41
J 4.47 53.19
K 21.39 21.94
L 5.23 33.68
(a) Treating compensation as the explanatory variable, x, use technology to determine the estimates of β0 and β1.
The estimate of β1 is −0.217.
(Round to three decimal places as needed.)
The estimate of beta 0β0 is 50.1
(Round to one decimal place as needed.)
(b) Assuming that the residuals are normally distributed, test whether a linear relation exists between compensation and stock return at the α=0.01level of significance. What are the null and alternative hypotheses?
B. H0: β1=0 H1: β1≠0 Your answer is correct.
Compute the test statistic using technology. -0.10
(Round to two decimal places as needed.)
Compute the P-value using technology.
0.919
(Round to three decimal places as needed.)
State the appropriate conclusion. Choose the correct answer below.
A.
Reject H0. There is sufficient evidence to conclude that a linear relation exists between compensation and stock return.
B.
Reject H0. There is not sufficient evidence to conclude that a linear relation exists between compensation and stock return.
C.
Do not reject H0. There is sufficient evidence to conclude that a linear relation exists between compensation and stock return.
D.
Do not reject H0. There is not sufficient evidence to conclude that a linear relation exists between compensation and stock return.
Part a) and b) are correct.
Now we have to make conclusion.
Here p-value = 0.919 and = 0.01
Decision rule : if p-value , we reject the null hypothesis H0, otherwise we fail to reject H0
Our p-value = 0.919 > 0.01
So we fail to reject the null hypothesis H0 .
Conclusion : Do not reject H0 . There is There is not sufficient evidence to conclude that a linear relation exists between compensation and stock return.
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