Deck 4: Summarizing Bivariate Data

Question 1

Compute the correlation coefficient.

A) 0.117
B) 46.143
C) 0.779
D) 0.883

Answer 1

0.883

Question 2

Compute the least-squares regression line for predicting y from x given the following summary statistics:

A) y=28.6666+2.4910 x
B) y=2.4910+28.6666 x
C) y=28.6666+0.2833 x
D) y=0.2833+28.6666 x

Answer 2

y=28.6666+2.4910 x

Question 3

One of the primary feeds for beef cattle is corn. The following table presents the average price in dollars for a bushel of corn and a pound of ribeye steak for 10 consecutive months. The least-squares regression line for predicting the ribeye price from the corn price is Predict the ribeye price in a month when the corn price was $6.28 per bushel.

A) $13.14 per lb
B) $14.52 per lb
C) $12.48 per lb
D) $13.86 per lb

Answer 3

$13.14 per lb

Question 4

The following table lists the heights in inches and weights in pounds of six football quarterbacks. The least-squares regression equation is . If two quarterbacks differ in height by 6 inches, by how much would you predict their weights to differ?

A) 4.58 pounds
B) 0.76 pounds
C) 6.00 pounds
D) 952.64 pounds

Question 5

One of the primary feeds for beef cattle is corn. The following table presents the average price in dollars for a bushel of corn and a pound of ribeye steak for 10 consecutive months. The correlation coefficient between the corn price and the ribeye price is 0.918. Which of the
Following is the best interpretation of the correlation coefficient?

A) The price of ribeye tends to go down and the price of corn goes up.
B) The changes in corn price and ribeye price tend to go up and down together.
C) There is no correlation between the price of corn and the price of ribeye.
D) Increasing corn prices cause ribeye prices to increase.

Question 6

One of the primary feeds for beef cattle is corn. The following table presents the average price in dollars for a bushel of corn and a pound of ribeye steak for 10 consecutive months. Construct a scatter plot of the price of ribeye (y) versus the price of corn (x).

A)
B)
C)
D)

Question 7

For which of the following scatter plots is the correlation coefficient an appropriate summary?

A)
B)
C)
D)

Question 8

The common cricket can be used as a crude thermometer. The colder the temperature, the slower the rate of chirping. The table below shows the average chirp rate of a cricket at various temperatures. The least-squares regression line for predicting the temperature from the chirp rate is Predict the temperature if the chirp rate is 1.6 chirps per second.

A) 51 ºF
B) 48 ºF
C) 44 ºF
D) 22 ºF

Question 9

The following table presents the average price in dollars for a dozen eggs and a gallon of milk in several recent years. The least-squares regression equation is . If the price of eggs differs by $0.25
From one year to the next, by how much would you expect the price of milk to differ?

A) $1.50
B) -$0.09
C) $0.38
D) $0.09

Question 10

One of the primary feeds for beef cattle is corn. The following table presents the average price in dollars for a bushel of corn and a pound of ribeye steak for 10 consecutive months. The least-squares regression line for predicting the ribeye price from the corn price is y = 6.3662 + 1.0315x.
If the price of corn differs by $0.15 per bushel, by how much would you expect the price of ribeye to
Differ?

A) $6.52 per lb
B) -$0.95 per lb
C) $0.15 per lb
D) $0.95 per lb

Question 11

The common cricket can be used as a crude thermometer. The colder the temperature, the slower the rate of chirping. The table below shows the average chirp rate of a cricket at various temperatures. Compute the least-squares regression line for predicting the temperature from the chirp rate.

A) y=9.9492+34.0748 x
B) y=12.7143+34.0748 x
C) y=34.0748+12.7143 x
D) y=34.0748+9.9492 x

Question 12

The following table presents the number of police officers (per 100,000 citizens) and the annual murder rate (per 100,000 citizens) for a sample of cities. Compute the correlation coefficient between the per capita number of police officers and the per capita
Murder rate.

A) -0.726
B) -0.666
C) 0.444
D) -0.444

Question 13

The common cricket can be used as a crude thermometer. The colder the temperature, the slower the rate of chirping. The table below shows the average chirp rate of a cricket at various temperatures. The least-squares regression line for predicting the temperature from the chirp rate is y = 32.298 + 12.297x.
If two chirp rates differ by 1.5 chirps per second, by how much would the temperature differ?

A) 48 ºF
B) 17 ºF
C) 18 ºF
D) 21 ºF

Question 14

The following table presents the number of police officers (per 100,000 citizens) and the annual murder rate (per 100,000 citizens) for a sample of cities. Construct a scatter plot of the per capita murder rate (y) versus the per capita number of police officers(x))

A)

B)

C)

D)

Question 15

One of the primary feeds for beef cattle is corn. The following table presents the average price in dollars for a bushel of corn and a pound of ribeye steak for 10 consecutive months. Compute the least-squares regression line for predicting the ribeye price from the corn price.

A) y=-5.491+0.3372 x
B) y=2.9654-5.491 x
C) y=5.491+0.3372 x
D) y=-5.491+2.9654 x

Question 16

A blood pressure measurement consists of two numbers: the systolic pressure, which is the maximum pressure taken when the heart is contracting, and the diastolic pressure, which is the
Minimum pressure taken at the beginning of the heartbeat. Blood pressures were measured, in
Millimeters of mercury (mmHg), for a sample of eight adults. The following table presents the
Results. The least-squares regression equation is . If the systolic pressures of two patients
Differ by 8 mmHg, by how much would you predict their diastolic pressures to differ?

A) 8.56 mmHg
B) 4.49 mmHg
C) 0.56 mmHg
D) 0.07 mmHg

Question 17

The following table presents the number of police officers (per 100,000 citizens) and the annual murder rate (per 100,000 citizens) for a sample of cities. The correlation coefficient between the per capita number of police officers and the per capita murder rates
-0)899. Which of the following is the best interpretation of the correlation coefficient?

A) The per capita murder rate tends to go down as the per capita number of police officers goes up.
B) Higher murder rates make it more difficult for cities to hire police officers.
C) The per capita number of police officers and the per capita murder rates are positively associated.
D) More per capita police officers results in fewer per capita murders.

Question 18

Characterize the relationship shown in the figure.

A) positive linear
B) positive nonlinear
C) negative nonlinear
D) negative linear

Question 19

The following table shows the per-person carbon dioxide emissions for the United States and for the rest of the world over six years. The least-squares regression equation is . If the non-U.S. emissions differ by 0.5
From one year to the next, by how much would you predict the U.S. emissions to differ?

A) -0.92
B) 0.46
C) -1.83
D) -0.46

Question 20

One of the primary feeds for beef cattle is corn. The following table presents the average price in dollars for a bushel of corn and a pound of ribeye steak for 10 consecutive months. Compute the correlation coefficient between the corn price and the ribeye price.

A) 0.621
B) 0.721
C) 0.279
D) 0.520

Question 21

The following MINITAB output presents the least squares regression line for predicting the price of a certain commodity from the price of a barrel of oil. Write the equation of the least-squares regression line.

A) y=30.483819+1.633495 x
B) y=0.90565+4.861308 x
C) y=0.416341+0.008272 x
D) y=1.633495+30.483819 x

Question 22

An automotive engineer computed a least-squares regression line for predicting the gas mileage (miles per gallon, or mpg) of a certain vehicle from its speed in mph. The results are presented in
The following Excel output.



Predict the gas mileage when the vehicle is traveling at 56 mph.

A) 25.2 mpg
B) 49 mpg
C) 28 mpg
D) 31 mpg

Question 23

The following display from a graphing calculator presents the least-squares regression line for predicting the price of a certain commodity (y) from the price of a barrel of oil (x). Write the equation of the least-squares regression line.

A) y=4.71+0.48909 x
B) y=4.71+0.49 x
C) y=0.49+0.48909 x
D) y=0.49+4.71 x

Question 24

The following display from a graphing calculator presents the least-squares regression line for predicting the price of a certain commodity (y) from the price of a barrel of oil (x). What is the correlation between the oil price and the commodity price?

A) 0.76153
B) 0.59
C) 5.04
D) 0.57993

Question 25

An automotive engineer computed a least-squares regression line for predicting the gas mileage (mile per gallon) of a certain vehicle from its speed in mph. The results are presented in the
Following Excel output.


Write the equation of the least-squares regression line.

A) y=-38.394979+0.18832886 x
B) y=38.3949789-0.18832886 x
C) y=38.3949789+0.18832886 x
D) y=-0.18832886+38.3949789 x

Question 26

As with many other construction materials, the price of gravel (per ton) depends on the quantity of material ordered. The following table presents the unit cost (dollars/ton) for gravel for various order sizes (in
Tons). Compute the coefficient of determination.

A) -0.0767
B) -0.9233
C) 0.8525
D) 0.1475

Question 27

For the following data set, how much of the variation in the outcome variable is explained by the least-squares regression line?

A) 48.1%
B) 73.1%
C) 26.9%
D) 51.9%

Question 28

For the following data set, compute the coefficient of determination.

A) 0.272
B) 0.728
C) 0.074
D) 0.926

Question 29

The following MINITAB output presents the lest squares regression line for predicting the price of a certain commodity from the price of a barrel of oil. Predict the commodity price when the oil price is $114 per barrel.

A) $215
B) $226
C) $249
D) $170

Question 30

Of points 1, 2, and 3 shown below, which is the most influential?

A) point 1
B) point 3
C) All have the same influence.
D) point 2

Question 31

MINITAB-style residual plots are shown below. Which one of these plots indicates that it was appropriate to compute a least-squares regression line?

A)

B)

C)

D)

Question 32

As with many other construction materials, the price of gravel (per ton) depends on the quantity of material ordered. The following table presents the unit cost (dollars/ton) for gravel for various order sizes (in tons). Which of the following graphs is the correct residual plot for the data set? (Hint: create your own residual plot
And compare it to those shown below.)

A)

B)

C)

D)

Question 33

The following display from a graphing calculator presents the least-squares regression line for predicting the price of a certain commodity (y) from the price of a barrel of oil (x). Predict the commodity price when oil costs $107 per barrel.

A) $62
B) $530
C) $83
D) $36