Question

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 least-squares regression line. Choose the correct answer below.

A.^y =4.1174.117x−98.9

B.^y= −98.9x+4.117

C.^y=4.117x−100.9

D.^y =8.117x−98.9

Test whether there is a linear relation between height and weight at the α=0.05 level of significance.

State the null and alternative hypotheses. Choose the correct answer below.

A .H0:β0=0

H1:β0≠0

B. H0: β1=0

H1:β1>0

C. H0:β0=0

H1:β0>0

D. H0: β1=0

H1:β1≠0

Determine the P-value for this hypothesis test.

P-value=_____ (Round to three decimal places as needed.)

State the appropriate conclusion at the α=0.05 level of significance. Choose the correct answer below.

A.Reject H0. There is not sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

B.Do not reject H0. There is sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

C.Reject H0. There is sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

D.Do not reject H0. There is not sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

(c) Remove the values listed for Player 4 from the data table. Test whether there is a linear relation between height and weight. Do you think that Player 4 is influential?

Determine the P-value for this hypothesis test.

P-value=_____ (Round to three decimal places as needed.)

State the appropriate conclusion at the α=0.05 level of significance. Choose the correct answer below.

A.Do not reject H0. There is sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

B.Reject H0. There is not sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

C.Do not reject H0. There is not sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

D.Reject H0. There is sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

Do you think that Player 4 is influential?

A.No

B.Yes

Answer #1

Regression equation is

A.^y =4.117x − 98.9

D) Ho :- beta1 = 0. Vs. beta1 not equal to 0

t statistics = 2.7186

P value = 0.030

P value = 0.030 < 0.05 = alpha

C.Reject H0. There is sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

Remove player 4

Regression equation is

Y ^ = - 214.0030 + 5.6985 x

T stat = 2.2847

P value = 0.06240 > 0.05 = alpha

Fail to reject Ho

C.Do not reject H0. There is not sufficient evidence to conclude that a linear relation exists between the height and weight of baseball players.

Yes , player 4 is influential.

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...

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 ...

For the data set shown? below, complete parts? (a) through? (d)
below. x 3 4 5 7 8 y 4 6 8 12 13 ?(a)??Find the estimates of beta 0
and beta 1. beta 0almost equalsb 0equals nothing ?(Round to three
decimal places as? needed.) beta 1almost equalsb 1equals nothing
?(Round to three decimal places as? needed.) ?(b)??Compute the
standard? error, the point estimate for sigma. s Subscript eequals
nothing ?(Round to four decimal places as? needed.) ?(c)??Assuming
the residuals...

For the data set shown below, complete parts (a) through (d)
below. x 3 4 5 7 8 y 5 7 6 13 14 (a) Find the estimates of beta 0
and beta 1. beta 0almost equalsb 0equals nothing (Round to three
decimal places as needed.) beta 1almost equalsb 1equals nothing
(Round to three decimal places as needed.) (b) Compute the
standard error, the point estimate for sigma. s Subscript eequals
nothing (Round to four decimal places as needed.) (c) ...

Consider the data.
xi
2
6
9
13
20
yi
5
16
8
24
23
(a)
What is the value of the standard error of the estimate? (Round
your answer to three decimal places.)
(b)
Test for a significant relationship by using the t
test. Use α = 0.05.
State the null and alternative hypotheses.
H0: β0 ≠ 0
Ha: β0 = 0
H0: β1 ≠ 0
Ha: β1 =
0
H0: β1 = 0
Ha: β1 ≠ 0
H0:...

Consider the following data on x = weight (pounds) and
y = price ($) for 10 road-racing bikes.
Brand
Weight
Price ($)
A
17.8
2,100
B
16.1
6,250
C
14.9
8,370
D
15.9
6,200
E
17.2
4,000
F
13.1
8,600
G
16.2
6,000
H
17.1
2,680
I
17.6
3,400
J
14.1
8,000
These data provided the estimated regression equation
ŷ = 28,503 − 1,434x.
For these data, SSE = 6,833,947.38 and SST = 51,535,800. Use the
F test to determine...

You may need to use the appropriate technology to answer this
question.
Consider the following data on x = weight (pounds) and
y = price ($) for 10 road-racing bikes.
Brand
Weight
Price ($)
A
17.8
2,100
B
16.1
6,250
C
14.9
8,370
D
15.9
6,200
E
17.2
4,000
F
13.1
8,500
G
16.2
6,000
H
17.1
2,580
I
17.6
3,300
J
14.1
8,000
These data provided the estimated regression equation
ŷ = 28,458 − 1,433x.
For these data, SSE...

Consider the following data on price ($) and the overall score
for six stereo headphones tested by a certain magazine. The overall
score is based on sound quality and effectiveness of ambient noise
reduction. Scores range from 0 (lowest) to 100 (highest).
Brand
Price ($)
Score
A
180
74
B
150
71
C
95
63
D
70
54
E
70
38
F
35
24
(a)
The estimated regression equation for this data is
ŷ = 21.659 + 0.323x,
where x...

You may need to use the appropriate technology to answer this
question.
Consider the following data on price ($) and the overall score
for six stereo headphones tested by a certain magazine. The overall
score is based on sound quality and effectiveness of ambient noise
reduction. Scores range from 0 (lowest) to 100 (highest).
Brand
Price ($)
Score
A
180
76
B
150
69
C
95
63
D
70
56
E
70
38
F
35
28
(a)
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