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. The regression equation is \[ \text { Commodity }=-43.987621+23.368407 \text { Oil } \] $\begin{array}{lrrrr} \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \\ \text { Constant } & -43.987621 & 87.188705 & -0.504511 & 0.640428 \\ \text { Oil } & 23.368407 & 0.872214 & 2.715396 & 0.05324 \\ \hline \end{array}$ Predict the commodity price when the oil price is $114 per barrel.

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

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. $\begin{array} { c c } \hline \begin{array} { c } \text { Chirp Rate } \\ \text { (chirps/second) } \end{array} & \begin{array} { c } \text { Temperature } \\ \left( { } ^ { \circ } \mathrm { F } \right) \end{array} \\ \hline 2.8 & 70.5 \\ 1.7 & 54.7 \\ 3.3 & 71.6 \\ 2.7 & 69 \\ 2.3 & 54.1 \\ 3.4 & 73.1 \\ \hline \end{array}$ 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 A) 48 ºF B) 17 ºF C) 18 ºF D) 21 ºF

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. \[\begin{array} { c c } \hline \text { Corn Price } ( \$ / b u ) & \text { Ribeye Price } ( \$ / l b ) \\ \hline 6.09 & 12.59 \\ 6.45 & 13.23 \\ 6.32 & 13.62 \\ 6.25 & 12.89 \\ 6.33 & 13.22 \\ 6.53 & 14.10 \\ \hline \end{array}\] 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 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

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. $\begin{array} { c c } \hline \begin{array} { c } \text { Chirp Rate } \\ \text { (chirps/second) } \end{array} & \begin{array} { c } \text { Temperature } \\ \left( { } ^ { \circ } \mathrm { F } \right) \end{array} \\ \hline 3.7 & 67.8 \\ 2.6 & 64.6 \\ 1.9 & 55.1 \\ 1.8 & 44.3 \\ 3.9 & 71 \\ 2.8 & 67.8 \\ \hline \end{array}$ 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 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

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. $\begin{array} { c c } \hline \text { Corn Price (\$/bu) } & \text { Ribeye Price } ( \$ / \mathrm { lb } ) \\ \hline 6.25 & 13.57 \\ 5.81 & 12.99 \\ 5.79 & 13.01 \\ 6.64 & 13.70 \\ 6.24 & 13.54 \\ 6.03 & 12.71 \\ 5.99 & 12.47 \\ 6.28 & 13.50 \\ 6.33 & 13.02 \\ 5.87 & 12.40 \\ \hline \end{array}$ Compute the correlation coefficient between the corn price and the ribeye price.

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

Compute the correlation coefficient. $\begin{array} { c | c c c c c c } x & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline y & 108 & 102 & 113 & 116 & 153 & 149 \end{array}$

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

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. \[\begin{array} { c c } \hline \text { Corn Price (\$/bu) } & \text { Ribeye Price (\$/lb) } \\ \hline 5.97 & 12.20 \\ 6.37 & 12.86 \\ 5.92 & 12.51 \\ 5.91 & 12.28 \\ 5.79 & 12.67 \\ 6.35 & 13.13 \\ \hline \end{array}\] 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 A) $6.52 per lb B) -$0.95 per lb C) $0.15 per lb D) $0.95 per lb

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. \[\begin{array} { c c } \hline \text { Police Officers (per 100,000) } & \text { Murders (per } 100,000 \text { per yr) } \\ \hline 1109 & 46 \\ 1238 & 44 \\ 1235 & 38 \\ 1298 & 40 \\ 1077 & 49 \\ 1076 & 42 \\ \hline \end{array}\] 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 A) -0.726 B) -0.666 C) 0.444 D) -0.444

Exam 4: Summarizing Bivariate Data

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?

(Multiple Choice)

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Question 21

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. The regression equation is $\text { Commodity }=-43.987621+23.368407 \text { Oil }$ Predictor Coef SE Coef T P Constant -43.987621 87.188705 -0.504511 0.640428 Oil 23.368407 0.872214 2.715396 0.05324 Predict the commodity price when the oil price is $114 per barrel.

(Multiple Choice)

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Question 22

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. Chirp Rate (chirps/second) Temperature 2.8 70.5 1.7 54.7 3.3 71.6 2.7 69 2.3 54.1 3.4 73.1 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?

(Multiple Choice)

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Question 23

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. Corn Price (\ /bu) Ribeye Price (\ /lb) 6.09 12.59 6.45 13.23 6.32 13.62 6.25 12.89 6.33 13.22 6.53 14.10 Compute the least-squares regression line for predicting the ribeye price from the corn price.

(Multiple Choice)

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Question 24

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. Chirp Rate (chirps/second) Temperature 3.7 67.8 2.6 64.6 1.9 55.1 1.8 44.3 3.9 71 2.8 67.8 Compute the least-squares regression line for predicting the temperature from the chirp rate.

(Multiple Choice)

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Question 25

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. Corn Price ( \/ bu) Ribeye Price (\ /) 6.25 13.57 5.81 12.99 5.79 13.01 6.64 13.70 6.24 13.54 6.03 12.71 5.99 12.47 6.28 13.50 6.33 13.02 5.87 12.40 Compute the correlation coefficient between the corn price and the ribeye price.

(Multiple Choice)

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Question 26

Compute the correlation coefficient. x 5 6 7 8 9 10 y 108 102 113 116 153 149

(Multiple Choice)

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Question 27

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. Corn Price ( \/ bu) Ribeye Price (\ /) 5.89 12.94 6.66 14.20 6.07 13.05 5.72 12.86 6.20 13.61 6.42 13.24 5.81 12.98 6.68 13.45 5.75 12.13 5.90 12.79 Construct a scatter plot of the price of ribeye (y) versus the price of corn (x).

(Multiple Choice)

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Question 28

Characterize the relationship shown in the figure.

(Multiple Choice)

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Question 29

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

(Multiple Choice)

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Question 30

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. Corn Price ( \/ bu) Ribeye Price ( \/ lb) 5.97 12.20 6.37 12.86 5.92 12.51 5.91 12.28 5.79 12.67 6.35 13.13 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?

(Multiple Choice)

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Question 31

For the following data set, how much of the variation in the outcome variable is explained by the least-squares regression line? x 1 3 2 8 9 5 y 7 18 5 13 15 9

(Multiple Choice)

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Question 32

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. Police Officers (per 100,000) Murders (per 100,000 per yr) 1109 46 1238 44 1235 38 1298 40 1077 49 1076 42 Compute the correlation coefficient between the per capita number of police officers and the per capita Murder rate.

(Multiple Choice)

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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). What is the correlation between the oil price and the commodity price?

Compute the correlation coefficient. x 5 6 7 8 9 10 y 108 102 113 116 153 149

Characterize the relationship shown in the figure.

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

For the following data set, how much of the variation in the outcome variable is explained by the least-squares regression line? x 1 3 2 8 9 5 y 7 18 5 13 15 9

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