ELEMENTARY STATISTICS(LL)(FD)
3rd Edition
ISBN: 9781260707458
Author: Navidi
Publisher: MCGRAW-HILL CUSTOM PUBLISHING
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Chapter 13, Problem 7RE
a.
To determine
To find: The least square line for predicting energy consumption
b.
To determine
To find: The
c.
To determine
To find: Whether the income is useful in predicting energy consumption.
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The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage
x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, =y−41.990.51x
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Based on the sample data and the regression line, complete the following.
(a)For these data, mileages that are less than the mean of the mileages tend to be paired with used selling prices that are ▼(Choose one) the mean of the used selling prices.
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In a particular manufacturing process, the useful life of a cutting tool is linearly related to the speed at which the tool is operated. The data in the accompanying table were derived from life tests for the two different brands of cutting tools currently used in the production process. For which brand would you
feel more confident using the least squares line to predict useful life for a given cutting speed?
E Click the icon to view the data.
Since the standard deviation (s = for Brand B is
than the standard deviation for Brand A (s = ), Brand B would be a
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Useful Life (hours)
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Brand A
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30.0
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3.5
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30.0
5.4
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40.0
5.4
6.0
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4.0
4.5
40.0
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2.8
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1.0
The cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen cadets, each bought new two years ago, and each sold used within the past month . For each cadet in the sample, we have listed both the mileage x (in thousands of miles) that the cadet had on its odometer at the time it was sold used and the price y (in thousand dollars) at which the cadet was sold used. The least-squares regression line for these data has equation of y=41.79-0.50x. This line is shown in the scatter plot below.
A) for these data, used selling prices that are greater than the mean of the used selling prices tend fo be paired with mileages that are ___ the mean of the mileages ?
B) according to the regression equation, for an increase of one thousand miles in cadet mileage, there is a corresponding decrease of how many thousand dollars in the used selling price?
Chapter 13 Solutions
ELEMENTARY STATISTICS(LL)(FD)
Ch. 13.1 - Prob. 7ECh. 13.1 - Prob. 8ECh. 13.1 - In Exercises 9 and 10, determine whether the...Ch. 13.1 - Prob. 10ECh. 13.1 - Prob. 11ECh. 13.1 - Prob. 12ECh. 13.1 - Prob. 13ECh. 13.1 - Prob. 14ECh. 13.1 - Prob. 15ECh. 13.1 - Prob. 16E
Ch. 13.1 - Prob. 17ECh. 13.1 - Prob. 18ECh. 13.1 - Prob. 19ECh. 13.1 - Prob. 20ECh. 13.1 - Prob. 21ECh. 13.1 - Prob. 22ECh. 13.1 - Prob. 23ECh. 13.1 - Prob. 24ECh. 13.1 - Prob. 25ECh. 13.1 - Prob. 26ECh. 13.1 - Prob. 27ECh. 13.1 - Prob. 28ECh. 13.1 - Prob. 26aECh. 13.1 - Calculator display: The following TI-84 Plus...Ch. 13.1 - Prob. 28aECh. 13.1 - Prob. 29ECh. 13.1 - Prob. 30ECh. 13.1 - Confidence interval for the conditional mean: In...Ch. 13.2 - Prob. 3ECh. 13.2 - Prob. 4ECh. 13.2 - Prob. 5ECh. 13.2 - Prob. 6ECh. 13.2 - Prob. 7ECh. 13.2 - Prob. 8ECh. 13.2 - Prob. 9ECh. 13.2 - Prob. 10ECh. 13.2 - Prob. 11ECh. 13.2 - Prob. 12ECh. 13.2 - Prob. 13ECh. 13.2 - Prob. 14ECh. 13.2 - Prob. 15ECh. 13.2 - Prob. 16ECh. 13.2 - Prob. 17ECh. 13.2 - Dry up: Use the data in Exercise 26 in Section...Ch. 13.2 - Prob. 19ECh. 13.2 - Prob. 20ECh. 13.2 - Prob. 21ECh. 13.3 - Prob. 7ECh. 13.3 - Prob. 8ECh. 13.3 - Prob. 9ECh. 13.3 - In Exercises 9 and 10, determine whether the...Ch. 13.3 - Prob. 11ECh. 13.3 - Prob. 12ECh. 13.3 - Prob. 13ECh. 13.3 - For the following data set: Construct the multiple...Ch. 13.3 - Engine emissions: In a laboratory test of a new...Ch. 13.3 - Prob. 16ECh. 13.3 - Prob. 17ECh. 13.3 - Prob. 18ECh. 13.3 - Prob. 19ECh. 13.3 - Prob. 20ECh. 13.3 - Prob. 21ECh. 13.3 - Prob. 22ECh. 13.3 - Prob. 23ECh. 13 - A confidence interval for 1 is to be constructed...Ch. 13 - A confidence interval for a mean response and a...Ch. 13 - Prob. 3CQCh. 13 - Prob. 4CQCh. 13 - Prob. 5CQCh. 13 - Prob. 6CQCh. 13 - Construct a 95% confidence interval for 1.Ch. 13 - Prob. 8CQCh. 13 - Prob. 9CQCh. 13 - Prob. 10CQCh. 13 - Prob. 11CQCh. 13 - Prob. 12CQCh. 13 - Prob. 13CQCh. 13 - Prob. 14CQCh. 13 - Prob. 15CQCh. 13 - Prob. 1RECh. 13 - Prob. 2RECh. 13 - Prob. 3RECh. 13 - Prob. 4RECh. 13 - Prob. 5RECh. 13 - Prob. 6RECh. 13 - Prob. 7RECh. 13 - Prob. 8RECh. 13 - Prob. 9RECh. 13 - Prob. 10RECh. 13 - Air pollution: Following are measurements of...Ch. 13 - Icy lakes: Following are data on maximum ice...Ch. 13 - Prob. 13RECh. 13 - Prob. 14RECh. 13 - Prob. 15RECh. 13 - Prob. 1WAICh. 13 - Prob. 2WAICh. 13 - Prob. 1CSCh. 13 - Prob. 2CSCh. 13 - Prob. 3CSCh. 13 - Prob. 4CSCh. 13 - Prob. 5CSCh. 13 - Prob. 6CSCh. 13 - Prob. 7CS
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forward
- The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation y = 40.63 - 0.46x . This line is shown in the scatter plot below. Mileage, x(in thousands) Used selling price, y(in thousands of dollars) 23.9 29.5 37.6 22.6 20.8 30.9 23.3 33.1 28.3 26.1 27.3 29.9 27.7 29.8 20.9 30.3 25.8 27.2 34.4 25.9 23.9 27.4 23.8 27.5 23.2 31.4 15.6 34.0 29.4 27.7 Send…arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation y=41.52-0.49x. This line is shown in the scatter plot below. Mileage, x (in thousands) 15.7 23.3 27.5 20.7 23.9 23.6 21.1 25.6 26.5 24.2 37.7 22.9 28.0 29.4 34.1 Send data to calculator V Used selling price, y (in thousands of dollars) Send data to Excel 34.5 33.9 29.9 31.2 26.9 27.3 31.4 26.4 30.8 30.3 22.8 30.5 25.9 28.1 25.5 Based on the sample data and the regression line, complete the following. Used selling price (in thousands of dollars) 40+ 35 30- 25-…arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation y = 40.86-0.45x. This line is shown in the scatter plot below. Mileage, x (in thousands) 29.7 27.8 26.8 24.2 21.1 23.0 24.3 15.4 37.7 23.6 34.4 27.8 23.5 20.9 259 Used selling price, y (in thousands of dollars) 27.4 29.4 31.2 30.1 31.7 31.7 27.2 34.2 23.4 28.2 26.3 26.5 33.7 30.6 267 Used selling price (in thousands of dollars) 40- 35+ 30- 25- 20 THIS 15 X 20 X X X 30 Mileagex (in thousands) X 35 40arrow_forward
- The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, Ŷ=42.39-0.52x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation Ŷ=42.80-0.53x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y = 41.51-0.49x. This line is shown in the scatter plot below. Mileage, x (in thousands) Used selling price, y (in thousands of dollars) 24.2 27.6 26.9 30.0 28.1 25.5 40+ 20.5 30.4 15.5 34.5 21.1 31.0 24.1 29.8 30- 23.4 28.3 37.8 23.3 27.7 29.5 20- 23.6 33.2 39.3 21.3 23.3 31.3 Mileage (in thousands) 25.7 26.4 34.4 25.4 29.4 28.8 Send data to calculator Send data to Excel Based on the…arrow_forward
- The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least- squares regression line for these data has equation y=41.49 -0.48x. This line is shown in the scatter plot below. Mileage, x (in thousands) 21.1 28.1 34.3 15.5 27.3 22.6 24.4 27.8 39.2 37.9 25.8 23.7 24.5 23.4 29.7 20.7 Send data to calculator V Used selling price, y (in thousands of dollars) 32.0 26.5 26.0 33.5 30.5 29.9 30.5 30.4 21.5 22.8 26.5 27.8 27.7 34.1 27.8 31.0 Used selling price (in thousands of dollars) Based on the sample data and the regression line, complete the following. 40- 30- FINS (a) For…arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation ŷ=42.39-0.52x. This line is shown in the scatter plot below. Mileage, x (in thousands) Used selling price, y (in thousands of dollars) 23.0 37.6 23.7 28.0 38.9 21.6 25.7 27.1 20.9 31.8 20.9 31.2 29.1 27.6 24.2 30.0 28.2 25.9 22.8 31.4 15.4 34.5 23.5 33.2 24.1 27.5 27.7 30.8 26.8 30.5 34.4 25.3 Send data to calculator Send data to Excel Based on the sample data and the regression line, complete the following. Used selling price (in thousands of dollars) 40- +…arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=42.91-0.54.x. This line is shown in the scatter plot below. Mileage, x (in thousands) 28.3 29.3 34.5 23.4 15.2 27.2 24.3 21.1 37.5 23.8 24.3 21.0 38.9 26.0 28.1 23.0 Used selling price, y (in thousands of dollars) 25.9 27.9 24.9 33.5 33.9 30.8 27.3 31.7 22.5 28.9 29.6 31.4 20.7 26.2 29.9 31.2 Send data to calc... ✔ Send data to Excel Used selling price (in thousands of dollars) 30+ 25-…arrow_forward
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