Data Analysis And Decision Making 4th Edition By S. Christian Albright – Test Bank
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Sample Test
CHAPTER 3: Finding Relationships Among Variables
MULTIPLE CHOICE
1. To
examine relationships between two categorical variables, we can use
2. Counts
and corresponding charts of the counts
3. Scatterplots
4. Histograms
5. None
of these options
ANS:
A
PTS:
1
MSC: AACSB: Analytic
2. Tables
used to display counts of a categorical variable are called
3. Crosstabs
c. Both
of these options
4. Contingency
tables d.
Neither of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic
3. The
Excel function that allows you to count using more than one criterion is
4. COUNTIF
5. COUNTIFS
6. SUMPRODUCT
7. VLOOKUP
8. HLOOKUP
ANS:
B
PTS:
1
MSC: AACSB: Analytic
4. Example
of comparison problems include
5. Salary
broken down by male and female subpopulations
6. Cost
of living broken down by region of a country
7. Recovery
rate for a disease broken down by patients who have taken a drug and patients
who have taken a placebo
8. Starting
salary of recent graduates broken down by academic major
9. All
of these options
ANS:
E
PTS:
1
MSC: AACSB: Analytic
5. The
most common data format is
6. Long
c.
Stacked
7. Short
d. Unstacked
ANS:
C
PTS:
1
MSC: AACSB: Analytic
6. A
useful way of comparing the distribution of a numerical variable across
categories of some categorical variable is
7. Side-by-side
boxplots
c. Both
of these options
8. Side-by-side
histograms d.
Neither of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic
7. We
study relationships among numerical variables using
8. Correlation
9. Covariance
10.
Scatterplots
11.
All of these options
12.
None of these options
ANS:
D
PTS:
1
MSC: AACSB: Analytic
8. Scatterplots
are also referred to as
9. Crosstabs
10.
Contingency charts
11.
X-Y charts
12.
All of these options
13.
None of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic
9. Correlation
and covariance measure
10.
The strength of a linear relationship between two numerical
variables
11.
The direction of a linear relationship between two numerical
variables
12.
The strength and direction of a linear relationship between two
numerical variables
13.
The strength and direction of a linear relationship between two
categorical variables
14.
None of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
10.
We can infer that there is a strong relationship between two
numerical variables when
11.
The points on a scatterplot cluster tightly around an upward
sloping straight line
12.
The points on a scatterplot cluster tightly around a downward
sloping straight line
13.
Either of these options
14.
Neither of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
11.
The limitation of covariance as a descriptive measure of
association is that it
12.
Only captures positive relationships
13.
Does not capture the units of the variables
14.
Is very sensitive to the units of the variables
15.
Is invalid if one of the variables is categorical
16.
None of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
12.
A the correlation is close to 0, then we expect to see
13.
An upward sloping cluster of points on the scatterplot
14.
A downward sloping cluster of points
15.
A cluster of points around a trendline
16.
A cluster of points with no apparent relationship
17.
We cannot say what the scatterplot should look like based on the
correlation
ANS:
D
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
13.
We are usually on the lookout for large correlations near
14.
+1
c.
Either of these options
15.
-1 d.
Neither of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
14.
The correlation is best interpreted
15.
By itself
16.
Along with the covariance
17.
Along with the corresponding scatterplot
18.
Along with the corresponding contingency chart
19.
Along with the mean and standard deviation
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
15.
Which of the following are considered measures of association?
16.
Mean and variance
17.
Variance and correlation
18.
Correlation and covariance
19.
Covariance and variance
20.
First quartile and third quartile
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
16.
Generally speaking, if two variables are unrelated (as one
increases, the other shows no pattern), the covariance will be
17.
a large positive number
18.
a large negative number
19.
a positive or negative number close to zero
20.
a positive or negative number close to +1 or -1
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
17.
A perfect straight line sloping downward would produce a
correlation coefficient equal to
18.
+1
19.
–1
20.
0
21.
+2
22.
–2
ANS:
B
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
18.
If Cov(X,Y) = – 16.0, variance of X = 25, variance of Y = 16
then the sample coefficient of correlation r is
19.
+ 1.60
20.
– 1.60
21.
– 0.80
22.
+ 0.80
23.
Cannot be determined from the given information
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
19.
A scatterplot allows one to see:
20.
whether there is any relationship between two variables
21.
what type of relationship there is between two variables
22.
Both options are correct
23.
Neither option is correct
ANS:
C
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
20.
The tool that provides useful information about a data set by
breaking it down into subpopulations is the:
21.
histogram
c.
pivot table
22.
scatterplot
d.
spreadsheet
ANS:
C
PTS:
1
MSC: AACSB: Analytic
21.
The tables that result from pivot tables are called:
22.
samples
c.
specimens
23.
sub-tables
d. crosstabs
ANS:
D
PTS:
1
MSC: AACSB: Analytic
22.
Which of the following statements are false?
23.
Contingency tables are traditional statistical terms for pivot
tables that list counts.
24.
Time series plot is a chart showing behavior over time of a time
series variable.
25.
Pivot table is a table in Excel that summarizes data broken down
by one or more numerical variables.
26.
None of these options
ANS:
C
PTS:
1
MSC: AACSB: Analytic
23.
Which of the following are true statements of pivot tables?
24.
They allow us to “slice and dice” data in a variety of ways.
25.
Statisticians often refer to them as contingency tables or
crosstabs.
26.
Pivot tables can list counts, averages, sums, and other summary
measures, whereas contingency tables list only counts.
27.
All of these options
ANS:
D
PTS:
1
MSC: AACSB: Analytic
TRUE/FALSE
1. Counts
for categorical variable are often expressed as percentages of the total.
ANS:
T
PTS:
1
MSC: AACSB: Analytic
2. An
example of a joint category of two variables is the count of all non-drinkers
who are also nonsmokers.
ANS:
T
PTS:
1
MSC: AACSB: Analytic
3. Joint
categories for categorical variables cannot be used to make inferences about
the relationship between the individual categorical variables.
ANS:
F
PTS:
1
MSC: AACSB: Analytic
4. Problems
in data analysis where we want to compare a numerical variable across two or
more subpopulations are called comparison problems.
ANS:
T
PTS:
1
MSC: AACSB: Analytic
5. Side-by-side
boxplots allow you to quickly see how two or more categories of a numerical
variable compare
ANS:
T
PTS:
1
MSC: AACSB: Analytic
6. We
must specify appropriate bins for side-by-side histograms in order to make fair
comparisons of distributions by category.
ANS:
T
PTS:
1
MSC: AACSB: Analytic
7. Correlation
and covariance can be used to examine relationships between numerical variables
and categorical variables that have been coded numerically.
ANS:
F
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
8. A
trend line on a scatterplot is a line or a curve that fits the scatter as well
as possible
ANS:
T
PTS:
1
MSC: AACSB: Analytic
9. To
form a scatterplot of X versus Y, X and Y must be paired
ANS:
T
PTS:
1
MSC: AACSB: Analytic
10.
Correlation has the advantage of being in the same original
units as the X and Y variables
ANS:
F
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
11.
Correlation is a single-number summary of a scatterplot
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
12.
We do not even try to interpret correlations numerically except
possibly to check whether they are positive or negative
ANS:
F
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
13.
The cutoff for defining a large correlation is >0.7 or
<-0.7.
ANS:
F
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
14.
Generally speaking, if two variables are unrelated, the
covariance will be a positive or negative number close to zero
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
15.
The correlation between two variables is a unitless and is
always between –1 and +1.
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
16.
If the standard deviations of X and Y are 15.5 and 10.8,
respectively, and the covariance of X and Y is 128.8, then the coefficient of
correlation r is approximately 0.77.
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
17.
It is possible that the data points are close to a curve and
have a correlation close to 0, because correlation is relevant only for
measuring linear relationships.
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
18.
If the coefficient of correlation r = 0 .80, the standard
deviations of X and Y are 20 and 25, respectively, then Cov(X, Y) must be 400.
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
19.
The advantage that the coefficient of correlation has over the
covariance is that the former has a set lower and upper limit.
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
20.
If the standard deviation of X is 15, the covariance of X and Y
is 94.5, the coefficient of correlation r = 0.90, then the variance of Y is
7.0.
ANS:
F
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
21.
The scatterplot is a graphical technique used to describe the
relationship between two numerical variables.
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
22.
Statisticians often refer to the pivot tables as contingency
tables or crosstabs.
ANS:
T
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
23.
If we draw a straight line through the points in a scatterplot
and most of the points fall close to the line, there is a strong positive
linear relationship between the two variables.
ANS:
F
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
SHORT ANSWER
NARRBEGIN: SA_47_49
Below you will find current annual salary data and related
information for 30 employees at Gamma Technologies, Inc. These data include
each selected employees gender (1 for female; 0 for male), age, number of years
of relevant work experience prior to employment at Gamma, number of years of
employment at Gamma, the number of years of post-secondary education, and
annual salary. The tables of correlations and covariances are presented below.
Table of Correlations
Gender
Age Prior
Exp
Gamma Exp
Education
Salary
Gender
1.000
Age
-0.111 1.000
Prior
Exp
0.054 0.800 1.000
Gamma Exp
-0.203 0.916
0.587 1.000
Education
-0.039 0.518
0.434 0.342 1.000
Salary -0.154
0.923 0.723
0.870 0.617 1.000
Table of Covariances (variances on the diagonal)
Gender
Age Prior
Exp
Gamma Exp
Education
Salary
Gender
0.259
Age
-0.633 134.051
Prior
Exp
0.117 39.060 19.045
Gamma Exp
-0.700 72.047 17.413 49.421
Education
-0.033 9.951 3.140
3.987 2.947
Salary
-1825.97
249702.35
73699.75
143033.29
24747.68
584640062
NARREND
1. Which
two variables have the strongest linear relationship with annual salary?
ANS:
Age at 0.923 and Gamma experience at 0.870 have the strongest linear
relationship with annual salary.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
2. For
which of the two variables, number of years of prior work experience or number
of years of post-secondary education, is the relationship with salary stronger?
Justify your answer.
ANS:
Prior work experience is stronger at 0.723 versus 0.617 for
number of years of post-secondary education.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
3. How would
you characterize the relationship between gender and annual salary?
ANS:
It is a somewhat weak relationship at –0.154. Also, the negative
value tells us that the salaries are decreasing as the gender value increases.
This indicates that the salaries are lower for females (1) than for males (0).
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
4. The
percentage of the US population without health insurance coverage for samples
from the 50 states and District of Columbia for both 2003 and 2004 produced the
following table of correlations.
Table of Correlations:
Percent 2003
1.000
Percent 2003 Percent 2004
Percent 2004
0.903 1.000
What does the table for the two given sets of percentages tell
you in this case?
ANS:
There is a very large positive correlation between these two
sets of percentages. This indicates that the percentages tend to move together,
although not perfectly.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
NARRBEGIN: SA_51_53
An economic development researcher wants to understand the
relationship between the average monthly expenditure on utilities for
households in a particular middle-class neighborhood and each of the following
household variables: family size, approximate location of the household within
the neighborhood, and indication of whether those surveyed owned or rented
their home, gross annual income of the first household wage earner, gross
annual income of the second household wage earner (if applicable), size of the
monthly home mortgage or rent payment, and the total indebtedness (excluding
the value of a home mortgage) of the household.
The correlation for each pairing of variables are shown in the
table below:
Table of correlations
NARREND
5. Which
of the variables have a positive linear relationship with the household’s
average monthly expenditure on utilities?
ANS:
Ownership has a strong positive linear relationship with the
average expenditure on utilities. Also, family size, income of the first
household wage earner, income of the second household wage earned, monthly home
mortgage or rent payment, and the total indebtedness of the household have
moderate to weak positive relationships with the average expenditure on
utilities.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
6. Which
of the variables have a negative linear relationship with the household’s
average monthly expenditure on utilities?
ANS:
Location of the household has a weak negative linear
relationship with the average expenditure on utilities
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
7. Which
of the variables have essentially no linear relationship with the household’s
average monthly expenditure on utilities?
ANS:
It appears that family size has a very weak relationship with
the average expenditure on utilities
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
8. Three
samples, regarding the ages of teachers, are selected randomly as shown below:
Sample A:
17 22 20 18 23
Sample B:
30 28 35 40 25
Sample C:
44 39
54
21 52
How is the value of the correlation coefficient r affected in
each of the following cases?
4. a)
Each X value is multiplied by 4.
5. b)
Each X value is switched with the corresponding Y value.
6. c)
Each X value is increased by 2.
ANS:
1. a)
The value of does not change.
2. b)
The value of does not change.
3. c)
The value of does not change.
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
9. The
students at small community college in Iowa apply to study either English or
Business. Some administrators at the college are concerned that women are being
discriminated against in being allowed admittance, particularly in the business
program. Below, you will find two pivot tables that show the percentage of
students admitted by gender to the English program and the Business school. The
data has also been presented graphically. What do the data and graphs indicate?
English program
Gender
No
Yes Total
Female 46.0% 54.0% 100%
Male 60.8%
39.2% 100%
Total 53.5%
46.5% 100%
Business school
Gender
No
Yes Total
Female 69.2% 30.8% 100%
Male 64.1%
35.9% 100%
Total 65.4%
34.6% 100%
ANS:
These data indicate that a smaller percentage of women are being
admitted to the business program. Only 30.8% of women are being admitted to the
business program compared to 35.9% for men. However, it is also important to
note that only 34.6% of all applicants (women and men) are admitted to the
business program compared to 46.5% for the English program. Maybe the males
should say something about being discriminated against in being admitted to the
English program.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
10.
A sample of 30 schools produced the pivot table shown below for
the average percentage of students graduating from high school. Use this table
to determine how the type of school (public or Catholic) that students attend
affects their chance of graduating from high school.
ANS:
The percentages in the right column suggest that if we look at
all schools, the rate of graduation is much higher in Catholic schools than in
public schools. But a look at the breakdowns in the three ethnic group columns
shows that this difference is due primarily to schools that are black and
Latino. There isn’t much difference in graduation rates between Catholic and
public schools that are white.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
11.
A data set from a sample of 399 Michigan families was collected.
The characteristics of the data include family size (large or small), number of
cars owned by family (1, 2, 3, or 4), and whether family owns a foreign car.
Excel produced the pivot table shown below.
Use this pivot table to determine how family size and number of
cars owned influence the likelihood that a family owns a foreign car.
ANS:
The pivot table shows that the more cars a family owns, the more
likely it is that they own a foreign car (makes sense!). Also, the percentage
of large families who own a foreign car is larger than the similar percentage
of small families (36.0% versus 10.4%).
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
NARRBEGIN: SA_58_67
A sample of 150 students at a State University was taken after
the final business statistics exam to ask them whether they went partying the
weekend before the final or spent the weekend studying, and whether they did
well or poorly on the final. The following table contains the result.
Did Well in
Exam
Did Poorly in Exam
Studying for
Exam
60 15
Went Partying
22 53
NARREND
12.
Of those in the sample who went partying the weekend before the
final exam, what percentage of them did well in the exam?
ANS:
22 out of 75, or 29.33%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
13.
Of those in the sample who did well on the final exam, what
percentage of them went partying the weekend before the exam?
ANS:
22 out of 82, or 26.83%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
14.
What percentage of the students in the sample went partying the
weekend before the final exam and did well in the exam?
ANS:
22 out of 150, or 14.67%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
15.
What percentage of the students in the sample spent the weekend
studying and did well in the final exam?
ANS:
60 out of 150, or 40%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
16.
What percentage of the students in the sample went partying the
weekend before the final exam and did poorly on the exam?
ANS:
53 out of 150, or 35.33%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
17.
If the sample is a good representation of the population, what
percentage of the students in the population should we expect to spend the
weekend studying and do poorly on the final exam?
ANS:
15 out of 150, or 10%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
18.
If the sample is a good representation of the population, what
percentage of those who spent the weekend studying should we expect to do
poorly on the final exam?
ANS:
15 out of 75, or 20%
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
19.
If the sample is a good representation of the population, what
percentage of those who did poorly on the final exam should we expect to have
spent the weekend studying?
ANS:
15 out of 68, or 22.06%
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
20.
Of those in the sample who went partying the weekend before the
final exam, what percentage of them did poorly in the exam?
ANS:
53 out of 75, or 70.67%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
21.
Of those in the sample who did well in the final exam, what
percentage of them spent the weekend before the exam studying?
ANS:
60 out of 82, or 73.17%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
22.
A health magazine reported that a man’s weight at birth has a
significant impact on the chance that the man will suffer a heart attack during
his life. A statistician analyzed a data set for a sample of 798 men, and
produced the pivot table and histogram shown below. Determine how birth weight
influences the chances that a man will have a heart attack.
ANS:
The above pivot table shows counts (as percentages of row) of
heart attack versus birth weight, where birth weight has been grouped into
categories. The percentages in each category with heart attacks have then been
plotted versus weight at birth as shown in the histogram. It appears that the
likelihood of a heart attack is greatest for light babies, and then decreases
steadily, but increases slightly for the heaviest babies.
PTS:
1
MSC: AACSB: Analytic
23.
The table shown below contains information technology (IT)
investment as a percentage of total investment for eight countries during the
1990s. It also contains the average annual percentage change in employment
during the 1990s. Explain how these data shed light on the question of whether
IT investment creates or costs jobs. (Hint: Use the data to construct a
scatterplot)
Country
% IT % Change
Netherlands
2.5% 1.6%
Italy
4.1% 2.2%
Germany
4.5% 2.0%
France 5.5% 1.8%
Canada 8.3% 2.7%
Japan 8.3% 2.7%
Britain 8.3% 3.3%
U.S.
12.4% 3.7%
ANS:
The scatterplot displayed below shows there is a clear and
surprisingly consistent upward trend in these data — the larger the IT
investment percentage, the larger the percentage increase in employment (at
least among these 8 countries).
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
24.
There are two scatterplots shown below. The first chart shows
the relationship between the size of the home and the selling price. The second
chart examines the relationship between the number of bedrooms in the home and
its selling price. Which of these two variables (the size of the home or the
number of bedrooms) seems to have the stronger relationship with the home’s
selling price? Justify your answer.
ANS:
The relationship between selling price and house size (in square
feet) seems to be a stronger relationship. The correlation value is higher for
house size (0.657 to 0.452). The house size and the number of bedrooms seem to
be closely related, but the house size variable seems to offer more
information. The number of bedrooms is a discrete variable.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
25.
The following scatterplot compares the selling price and the
appraised value.
Is there a linear relationship between these two variables? If
so, how would you characterize the relationship?
ANS:
Yes, there is a linear relationship. Correlation value = 0.877
represents a rather strong relationship. You can also see from the scatterplot,
that there is a positive relationship between the selling price and the
appraisal value.
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
NARRBEGIN: SA_72_81
A recent survey data collected from 1000 randomly selected
Internet users. The characteristics of the users include their gender, age,
education, marital status and annual income. Using Excel, the following pivot
tables were produced.
NARREND
26.
Approximate the percentage of these Internet users who are men
under the age of 30.
ANS:
Approximately 19%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
27.
Approximate the percentage of these Internet users who are
single with no formal education beyond high school.
ANS:
Approximately 16%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
28.
Approximate the percentage of these Internet users who are
currently employed.
ANS:
Approximately 77%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
29.
What is the average annual salary of the employed Internet users
in this sample?
ANS:
Approximately $60,564
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
30.
Approximate the percentage of these Internet users who are
married with formal education beyond high school.
ANS:
Approximately 37%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
31.
What percentage of these Internet users who are married.
ANS:
Approximately 69%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
32.
Approximate the percentage of these Internet users who are in
the 58-71 age group.
ANS:
Approximately 9%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
33.
Approximate the percentage of these internet users who are
women.
ANS:
Approximately 39%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
34.
What percentage of these internet users has formal education
beyond high school?
ANS:
Exactly 52%
PTS: 1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
35.
Approximate the percentage of these internet users who are women
in the 30-43 age group.
ANS:
Approximately 15%
PTS:
1
MSC: AACSB: Analytic | AACSB: Descriptive Statistics
NARRBEGIN: SA_82_84
Economists believe that countries with more income inequality
have lower unemployment rates. An economist in 1996 developed the Table below
which contains the following information for ten countries during the 1980-1995
time period:
- The
change from 1980 to 1995 in ratio of the average wage of the top 10% of
all wage earners to the median wage
- The
change from 1980 to 1995 in unemployment rate.
Income inequality vs. Unemployment rate
Country
WIR Change UR Change
Germany
-6.0% 6.0%
France -3.5% 5.6%
Italy
1.0% 5.2%
Japan 0.0% 0.6%
Australia
5.0% 2.4%
Sweden
4.0% 5.9%
Canada 5.5% 2.0%
New Zealand
9.5% 4.0%
Britain 15.6% 2.5%
U.S.
15.8% -1.8%
NARREND
36.
Explain why the ratio of the average wage of the top 10% of all
wage earners to the median measures income inequality.
ANS:
If this ratio is high, then a relatively large share of all income
is being made by the people in the upper 10% — hence “inequality”. (Of course,
by definition, they’re making more than 10% of all income, but this ratio
measures how much more.)
PTS:
1
MSC: AACSB: Analytic | AACSB: Statistical Inference
37.
Do these data help to confirm or contradict the hypothesis that
increased wage inequality leads to lower unemployment levels? [Hint: construct
a scatterplot]
ANS:
The scatterplot shown above indicates that except possibly for
the one point indicated (Japan), there is a clear downward trend to these
points — when the wage inequality ratio is up (change is positive), the
unemployment rate tends to be down (change negative), and vice versa. So these
data support the hypothesis.
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