Consider the following data. Do heavier cars really use more gasoline? Suppose a car is chosen at random. Let x be the weight of the car (in hundreds of pounds), and let y be the miles per gallon (mpg). The following information is based on data taken from Consumer Reports (Vol. 62, No. 4). x 25 44 32 47 23 40 34 52 29 y | 33 22 23 13 17 21 14 A USE SALT Given the least squares line ŷ = 43.8915 - 0.6031x: (a) Make a residual plot for the least-squares model. Be sure to plot the horizontal line at y = 0. y y 1아 1아 5 20 30 40 50 60 20 30 40 50 y y 1아 1아 -5 20 30 40 50 60 20 30 40 50 (b) Use the residual plot to comment about the appropriateness of the least squares model for these data. The residuals do not seem to be scattered randomly around the horizontal line at 0. There do not appear to be any outliers. The residuals do not seem to be scattered randomly around the horizontal line at 0. There do appear to be outliers. The residuals seem to be scattered randomly around the horizontal line at 0. There do not appear to be any outliers. The residuals seem to be scattered randomly around the horizontal line at 0. There do appear to be outliers.

Consider the following data. Do heavier cars really use more gasoline? Suppose a car is chosen at random. Let x be the weight of the car (in hundreds of pounds), and let y be the miles per gallon (mpg). The following information is based on data taken from Consumer Reports (Vol. 62, No. 4). x 25 44 32 47 23 40 34 52 29 y | 33 22 23 13 17 21 14 A USE SALT Given the least squares line ŷ = 43.8915 - 0.6031x: (a) Make a residual plot for the least-squares model. Be sure to plot the horizontal line at y = 0. y y 1아 1아 5 20 30 40 50 60 20 30 40 50 y y 1아 1아 -5 20 30 40 50 60 20 30 40 50 (b) Use the residual plot to comment about the appropriateness of the least squares model for these data. The residuals do not seem to be scattered randomly around the horizontal line at 0. There do not appear to be any outliers. The residuals do not seem to be scattered randomly around the horizontal line at 0. There do appear to be outliers. The residuals seem to be scattered randomly around the horizontal line at 0. There do not appear to be any outliers. The residuals seem to be scattered randomly around the horizontal line at 0. There do appear to be outliers.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Concept explainers

Estimation

A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.

Question

100%

Consider the following data. Do heavier cars really use more gasoline? Suppose a car is chosen at random. Let x be the weight of
the car (in hundreds of pounds), and let y be the miles per gallon (mpg). The following information is based on data taken from
Consumer Reports (Vol. 62, No. 4).
|x25
34 52
14
44
32
23
47
13
23
40
y| 33
22
29
17
21
A USE SALT
Given the least squares line 9 - 43.8915 - 0.6031x:
(a) Make a residual plot for the least-squares model. Be sure to plot the horizontal line at y = 0.
y
y
1아
1아
-5
20
30
40
50
60
20
30
40 50
y
1아
10f
5
20
30
40
50
60
20
30
40
50
(b) Use the residual plot to comment about the appropriateness of the least squares model for these data.
The residuals do not seem to be scattered randomly around the horizontal line at 0. There do not appear to be any
outliers.
The residuals do not seem to be scattered randomly around the horizontal line at 0. There do appear to be
outliers.
The residuals seem to be scattered randomly around the horizontal line at 0. There do not appear to be any outliers.
The residuals seem to be scattered randomly around the horizontal line at 0. There do appear to be outliers.

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