Question

The accompanying data represent the total compensation for 12 randomly selected chief executive officers (CEOs) and the company's stock performance. Use the data to complete parts (a) through (d).

3 Click the icon to view the data table.

**Data Table of Compensation and Stock
Performance**

**Company Compensation (millions of
dollars) StockReturn (%)**

A 13.82 71.39

B 3.83 69.37

C 6.16 141.42

D 1.95 37.61

E 1.13 10.34

F 3.71 29.55

G 11.96 0.77

H 6.22 62.48

I 9.31 53.21

J 4.19 52.05

K 20.22 24.31

L 5.55 31.72

**(a)** Treating compensation as the explanatory
variable, x, use technology to determine the estimates of β0 and β1
.

The estimate of β1 is __________(Round to three decimal places as needed.)

The estimate of β0 is ___________(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 = 0.01 level of
significance.

What are the null and alternative hypotheses?

**A.** H0 : β0 ≠ 0

H1 : β0 = 0

**B.** H0 : β0 = 0

H1 : β0 ≠ 0

**C.** H0 : β1 ≠ 0

H1 : β1 = 0

**D.** H0 : β1 = 0

H1 : β1 ≠ 0

Compute the test statistic using technology. ___________(Round to two decimal places as needed.)

Compute the P-value using technology.____________(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.** Do not reject H0 . There is sufficient
evidence to conclude that a linear relation exists between
compensation and stock return.

**C.** Do not reject H0 . There is not sufficient
evidence to conclude that a linear relation exists between
compensation and stock return.

**D.** Reject H0 . There is not sufficient evidence
to conclude that a linear relation exists between compensation and
stock return.

**(c)** Assuming the residuals are normally
distributed, construct a 99% confidence interval for the slope of
the true least-squares regression line.

Lower bound = ____________ Upper bound =______________

(Round to two decimal places as needed.)

**(d)** Based on your results to parts (b) and (c),
would you recommend using the least-squares regression line to
predict the stock return of a company based on the CEO's
compensation? Why? What would be a good estimate of the stock
return based on the data in the table?

**A.** Based on the results from parts (b), the
regression line could be used to predict the stock return.

However, the results from part (b) indicate that the regression line should not be used. The results are not conclusive and further analysis of the data is needed.

**B.** The regression line could be used to predict
the stock return. The test in part (b) and the confidence interval
in part (c) both confirm that there is a relationship between

the variables.

**C.** Based on the results from parts (b), the
regression line should not be used to predict the stock return.
However, the results from part (c) indicate that the regression
line

should be used. The results are not conclusive and further analysis of the data is needed.

**D.** Based on the results from parts (b) and (c),
the regression line should not be used to predict the stock return.
The mean stock return would be a good estimate of the

stock return based on the data in the table.

Answer #1

Appplying regression on above data:

a) The estimate of β1 is =-0.555

b) The estimate of β0 is =52.755

**D.** H0 : β1 = 0

H1 : β1 ≠ 0

test statistic using technology =-0.27

P-value using technology =0.795

**C.** Do not reject H0 . There is not sufficient
evidence to conclude that a linear relation exists between
compensation and stock return.

c)

Lower bound =-7.146

Upper bound = 6.037

**D.** Based on the results from parts (b) and (c),
the regression line should not be used to predict the stock return.
The mean stock return would be a good estimate of the

stock return based on the data in the table.

The accompanying data represent the total compensation for 12
randomly selected chief executive officers (CEOs) and the
company's stock performance. Use the data to complete parts (a)
through (d).
Company Compensation Return
A 15.68 79.64
B 4.57 69.24
C 7.75 146.24
D 1.35 38.22
E 1.33 11.43
F 2.53 29.56
G 12.81 0.53
H 6.71 69.57
I 8.01 55.37
J 3.24 52.04
K 21.37 25.73
L 6.82 30.89
treating compensation as the explanatory variable, x, use
technology to determine...

Question:
The accompanying data represent the total compensation for 12
randomly selected chief executive officers (CEO) and the
company's stock performance in a recent year. Complete parts (a)
through (d) below.
Company Compensation
($mil) Stock Return (%)
Company
A 14.58 75.45
Company
B 4.07 63.99
Company
C 7.08 142.06
Company
D 1.07 32.69
Company
E 1.98 10.68
Company
F 3.79 30.69
Company
G 12.07
0.72
Company
H 7.56 69.43
Company
I 8.47 58.75
Company
J 4.05 55.95
Company
K 20.85 24.33
Company
L 6.66 32.25
(a) One would think that a higher stock return
would lead to a higher compensation. Based on this, what would...

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 14.62 74.84
B 3.29 60.79
C 6.65 141.68
D 1.92 33.03
E 1.78 11.89
F 2.88 30.99
G 11.37 0.92
H 6.81 64.52
I 8.91 52.12
J 3.17 52.88
K 20.67 23.68
L 6.43 31.83
(a) Treating compensation as the explanatory
variable, x, use technology to determine the estimates of β0 and
β1.
The estimate of...

The data in the accompanying table represent the heights and
weights of a random sample of professional baseball players.
Complete parts (a) through (c) below.
Player Height_(inches) Weight_(pounds)
Player_1 76 225
Player_2 75 195
Player_3 72 180
Player_4 82 231
Player_5 69 185
Player_6 74 190
Player_7 75 228
Player_8 71 200
Player_9 75 230
(b) Determine the least-squares regression line. Test whether
there is a linear relation between height and weight at the α=0.05
level of significance.
Determine the...

The accompanying data represent the total compensation for 12
randomly selected chief executive officers (CEO) and the
company's stock performance in a recent year. Complete parts (a)
through (d) below.
Company
Compensation ($mil)
Stock Return (%)
Company A
14.5514.55
75.4475.44
Company B
4.094.09
64.0464.04
Company C
7.127.12
142.09142.09
Company D
1.051.05
32.6932.69
Company E
1.971.97
10.6610.66
Company F
3.723.72
30.6130.61
Company G
12.0112.01
0.720.72
Company H
7.567.56
69.4569.45
Company I
8.438.43
58.7558.75
Company J
4.044.04
55.9855.98
Company K
20.9220.92
24.2924.29...

The accompanying data represent the total compensation for 12
randomly selected chief executives officers(CEO) and the company's
stock performance in a recent year
Company Compensation($mil)Stock Return(%)
Company A 14.56 75.49
Company B 4.09 63.96
Company C 7.07 142.03
Company D 1.04 32.66
Company E 1.91 10.63
Company F 3.76 30.69
Company G 12.03 0.78
Company H 7.63 69.43
Company I 8.47 58.66
Company J 4.06 55.91
Company k 20.92 24.31
Company L 6.67 32.25
(a). One would think that a...

The accompanying data represent the total compensation for 12
randomly selected chief executive officers (CEOs) and the
company's stock performance. Use the data to complete parts (a)
through (d). LOADING... Click the icon to view the data table.
(a) Treating compensation as the explanatory variable, x, use
technology to determine the estimates of beta 0 and beta 1. The
estimate of beta 1 is nothing. (Round to three decimal places as
needed.) Enter your answer in the answer box and...

The accompanying data represent the total compensation for 12
randomly selected chief executive officers (CEO) and the
company's stock performance in a recent year.
Company Compensation ($mil) Stock Return
(%)
Company A 14.52 75.46
Company B 4.07 64.01
Company C 7.15 142.09
Company D 1.07 32.67
Company E 1.93 10.67
Company F 3.79 30.69
Company G 12.01 0.75
Company H 7.58 69.39
Company I 8.44 58.65
Company J 4.06 55.91
Company K 20.89 24.29
Company L 6.61 32.23
(c) Determine...

The data in the accompanying table represent the heights and
weights of a random sample of professional baseball players.
Complete parts (a) through (c) below.
Player Height_(inches)
Weight_(pounds)
Player_1 75 227
Player_2 75 195
Player_3 72 180
Player_4 82 231
Player_5 69 185
Player_6 74 190
Player_7 75 228
Player_8 71 200
Player_9 75 230
(a) Draw a scatter diagram of the data, treating
height as the explanatory variable and weight as the response
variable.
(b) Determine the least-squares...

A pediatrician wants to determine the relation that may exist
between a child's height and head circumference. She randomly
selects 5 children and measures their height and head
circumference. The data are summarized below. A normal probability
plot suggests that the residuals are normally distributed. Complete
parts (a) and (b) below.
Height (inches), x
26
27.75
25.5
27.5
24.5
Head Circumference (inches), y
17.3
17.6
17.1
17.5
17.1
(a) Use technology to determine sb1.
sb1=____ (Round to four decimal places...

ADVERTISEMENT

Get Answers For Free

Most questions answered within 1 hours.

ADVERTISEMENT

asked 1 minute ago

asked 8 minutes ago

asked 10 minutes ago

asked 10 minutes ago

asked 11 minutes ago

asked 11 minutes ago

asked 12 minutes ago

asked 13 minutes ago

asked 19 minutes ago

asked 22 minutes ago

asked 32 minutes ago

asked 34 minutes ago