Data on pollution and cost of medical care for elderly people were given in Exercise 5.17 and are also shown here. The following data give a measure of pollution (micrograms of particulate matter per cubic meter of air) and the cost of medical care per person over age 65 for six geographic regions of the United States: The equation of the least-squares regression line for this data set is , where y ! medical cost and x ! pollution. a. Compute the six residuals. b. What is the value of the correlation coefficient for this data set? Does the value of r indicate that the linear relationship between pollution and medical cost is strong, moderate, or weak? Explain. c. Construct a residual plot. Are there any unusual features of the plot?
Data on pollution and cost of medical care for elderly people were given in Exercise 5.17 and are also shown here. The following data give a measure of pollution (micrograms of particulate matter per cubic meter of air) and the cost of medical care per person over age 65 for six geographic regions of the United States: The equation of the least-squares regression line for this data set is , where y ! medical cost and x ! pollution. a. Compute the six residuals. b. What is the value of the correlation coefficient for this data set? Does the value of r indicate that the linear relationship between pollution and medical cost is strong, moderate, or weak? Explain. c. Construct a residual plot. Are there any unusual features of the plot?
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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Question
Data on pollution and cost of medical care for elderly people were given in Exercise 5.17 and are also shown here. The following data give a measure of pollution (micrograms of particulate matter per cubic meter of air) and the cost of medical care per person over age 65 for six geographic regions of the United States:
The equation of the least-squares regression line for this
data set is , where y ! medical cost
and x ! pollution.
a. Compute the six residuals.
b. What is the value of the
data set? Does the value of r indicate that the linear relationship between pollution and medical cost is strong,
moderate, or weak? Explain.
c. Construct a residual plot. Are there any unusual features of the plot?
d. The observation for the West, (40.0, 899), has an x
value that is far removed from the other x values in the
sample. Is this observation influential in determining the
values of the slope and/or intercept of the least-squares
line? Justify your answer.
Transcribed Image Text:5.38 • Data on pollution and cost of medical care for
elderly people were given in Exercise 5.17 and are also
shown here. The following data give a measure of pollu-
tion (micrograms of particulate matter per cubic meter of
air) and the cost of medical care per person over age 65
for six geographic regions of the United States:
Cost of
Medical
Region
Pollution
Care
North
30.0
915
Upper South
Deep South
West South
31.8
891
32.1
26.8
968
972
Big Sky
30.4
952
West
40.0
899
The equation of the least-squares regression line for this
data set is ŷ = 1082.2 – 4.691x, where y = medical cost
and x = pollution.
a. Compute the six residuals.
b. What is the value of the correlation coefficient for this
data set? Does the value of r indicate that the linear rela-
tionship between pollution and medical cost is strong,
moderate, or weak? Explain.
c. Construct a residual plot. Are there any unusual fea-
tures of the plot?
d. The observation for the West, (40.0, 899), has an a
value that is far removed from the other x valucs in the
sample. Is this observation influential in determining the
values of the slope and/or intercept of the least-squares
line? Justify your answer.
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