File: Ch12, Chapter 12: Introduction to Regression Analysis and Correlation
True/False
1. Correlation is a measure of the degree of relatedness of variables.
Ans: True
Response: See section 12.1 Correlation
Difficulty: Easy
2. If the correlation coefficient between two variables is -1, it means that the two variables are
not related.
Ans: False
Response: See section 12.1 Correlation
Difficulty: Hard
3. The process of constructing a mathematical model or function that can be used to predict or
determine one variable by another variable is called regression analysis.
Ans: True
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
4. In regression, the variable that is being predicted is usually referred to as the independent
variable.
Ans: False
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
5. In regression, the predictor variable is called the dependent variable.
Ans: False
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
6. The first step in simple regression analysis usually is to construct a scatter plot.
Ans: True
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
7. The slope of the regression line, y = 21 − 5x, is 5.
Ans: False
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: Medium
8. The slope of the regression line, y = 21 − 5x, is 21.
Ans: False
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: Medium
9. For the regression line, y = 21 − 5x, 21 is the y-intercept of the line.
Ans: True
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: Medium
10. The difference between the actual y value and the predicted y value found using a regression
equation is called the residual.
Ans: True
Response: See section 12.4 Residual Analysis
Difficulty: Easy
11. Data points that lie apart from the rest of the points are called deviants. .
Ans: False
Response: See section 12.4 Residual Analysis
Difficulty: Medium
12. One of the assumptions of simple regression analysis is that the error terms are exponentially
distributed
Ans: False
Response: See section 12.4 Residual Analysis
Difficulty: Medium
13. In simple regression analysis the error terms are assumed to be independent and normally
distributed with zero mean and constant variance.
Ans: True
Response: See section 12.4 Residual Analysis
Difficulty: Medium
14. One of the major uses of residual analysis is to test some of the assumptions underlying
regression.
Ans: True
Response: See section 12.4 Residual Analysis
Difficulty: Medium
15. The proportion of variability of the dependent variable (y) accounted for or explained by the
independent variable (x) is called the coefficient of correlation.
Ans: False
Response: See section 12.6 Coefficient of Determination
Difficulty: Medium
16. The coefficient of determination is the proportion of variability of the dependent variable (y)
accounted for or explained by the independent variable (x).
Ans: True
Response: See section 12.6 Coefficient of Determination
Difficulty: Medium
17. In a simple regression the coefficient of correlation is the square root of the coefficient of
determination.
Ans: False
Response: See section 12.6 Coefficient of Determination
Difficulty: Medium
18. In the simple regression model, y = 21 − 5x, if the coefficient of determination is 0.81, we
can say that the coefficient of correlation between y and x is 0.90.
Ans: False
Response: See section 12.6 Coefficient of Determination
Difficulty: Hard
19. The range of admissible values for the coefficient determination is −1 to +1.
Ans: False
Response: See section 12.6 Coefficient of Determination
Difficulty: Medium
20. A t-test is used to determine whether the coefficients of the regression model are significantly
different from zero.
Ans: True
Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing
the Overall Model
Difficulty: Medium
21. To determine whether the overall regression model is significant, the F-test is used.
Ans: True
Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing
the Overall Model
Difficulty: Medium
22. The F-value to test the overall significance of a regression model is computed by dividing the
sum of squares regression (SSreg) by the sum of squares error (SSerr).
Ans: False
Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing
the Overall Model
Difficulty: Medium
Multiple Choice
23. According to the following graphic, X and Y have _________.
130
120
Y
110
100
90
80
70
1400
1600
1800
2000
X
a) strong negative correlation
b) virtually no correlation
c) strong positive correlation
d) moderate negative correlation
e) weak negative correlation
Ans: c
Response: See section 12.1 Correlation
Difficulty: Easy
2200
2400
24. According to the following graphic, X and Y have _________.
130
120
Y
110
100
90
80
70
0
5
10
15
20
25
30
X
a) strong negative correlation
b) virtually no correlation
c) strong positive correlation
d) moderate negative correlation
e) weak negative correlation
Ans: b
Response: See section 12.1 Correlation
Difficulty: Easy
25. A cost accountant is developing a regression model to predict the total cost of producing a
batch of printed circuit boards as a function of batch size (the number of boards produced in one
lot or batch). The explanatory variable is ______.
a) batch size
b) unit variable cost
c) fixed cost
d) total cost
e) total variable cost
Ans: a
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
26. A cost accountant is developing a regression model to predict the total cost of producing a
batch of printed circuit boards as a function of batch size (the number of boards produced in one
lot or batch). The dependent variable is ______.
a) batch size
b) unit variable cost
c) fixed cost
d) total cost
e) total variable cost
Ans: d
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
27. A cost accountant is developing a regression model to predict the total cost of producing a
batch of printed circuit boards as a linear function of batch size (the number of boards produced
in one lot or batch). The intercept of this model is the ______.
a) batch size
b) unit variable cost
c) fixed cost
d) total cost
e) total variable cost
Ans: c
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
28. A cost accountant is developing a regression model to predict the total cost of producing a
batch of printed circuit boards as a linear function of batch size (the number of boards produced
in one lot or batch). The slope of the accountant’s model is ______.
a) batch size
b) unit variable cost
c) fixed cost
d) total cost
e) total variable cost
Ans: b
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
29. From the following scatter plot, we can say that between y and x there is _______.
800
Y
600
400
200
0
0
20
40
X
60
80
a) perfect positive correlation
b) virtually no correlation
c) positive correlation
d) negative correlation
e) perfect negative correlation
Ans: c
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
30. From the following scatter plot, we can say that between y and x there is _______.
1200
1000
Y
800
600
400
200
0
0
20
40
X
60
80
a) perfect positive correlation
b) virtually no correlation
c) positive correlation
d) negative correlation
e) perfect negative correlation
Ans: d
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
31. From the following scatter plot, we can say that between y and x there is _______.
40000
30000
20000
Y
10000
0
-10000 0
20
40
60
80
-20000
-30000
X
a) perfect positive correlation
b) virtually no correlation
c) positive correlation
d) negative correlation
e) perfect negative correlation
Ans: b
Response: See section 12.2 Introduction to Simple Regression Analysis
Difficulty: Easy
32. In the regression equation, y = 75.65 + 0.50x, the slope is _______.
a) 0.50
b) 75.65
c) 1.00
d) 0.00
e) -0.50
Ans: a
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: Medium
33. In the regression equation, y = 75.65 + 0.50x, the intercept is _______.
a) 0.50
b) 75.65
c) 1.00
d) 0.00
e) -0.50
Ans: b
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: Medium
Y
34. Consider the following scatter plot and regression line. At x = 17, the residual (error term) is
_______.
600
500
400
300
200
100
0
0
10
20
30
40
50
60
70
X
a) positive
b) zero
c) negative
d) imaginary
e) unknown
Ans: a
Response: See section 12.4 Residual Analysis
Difficulty: Easy
Y
35. For following scatter plot and regression line, at x = 65 the residual is _______.
600
500
400
300
200
100
0
0
10
20
30
40
X
a) positive
b) zero
c) negative
d) imaginary
e) unknown
Ans: c
50
60
70
Response: See section 12.4 Residual Analysis
Difficulty: Easy
36. One of the assumptions made in simple regression is that ______________.
a) the error terms are normally distributed
b) the error terms have unequal variances
c) the model is nonlinear
d) the error terms are dependent
e) the error terms are all equal
Ans: a
Response: See section 12.4 Residual Analysis
Difficulty: Easy
37. One of the assumptions made in simple regression is that ______________.
a) the error terms are exponentially distributed
b) the error terms have unequal variances
c) the model is linear
d) the error terms are dependent
e) the model is nonlinear
Ans: c
Response: See section 12.4 Residual Analysis
Difficulty: Easy
38. The assumptions underlying simple regression analysis include ______________.
a) the error terms are exponentially distributed
b) the error terms have unequal variances
c) the model is nonlinear
d) the error terms are dependent
e) the error terms are independent
Ans: e
Response: See section 12.4 Residual Analysis
Difficulty: Easy
39. The assumption of constant error variance in regression analysis is called _______.
a) heteroscedasticity
b) homoscedasticity
c) residuals
d) linearity
e) nonnormality
Ans: b
Response: See section 12.4 Residual Analysis
Difficulty: Medium
40. The total of the squared residuals is called the _______.
a) coefficient of determination
b) sum of squares of error
c) standard error of the estimate
d) R-squared
e) coefficient of correlation
Ans: b
Response: See section 12.5 Standard Error of the Estimate
Difficulty: Easy
41. A standard deviation of the error of the regression model is called the _______.
a) coefficient of determination
b) sum of squares of error
c) standard error of the estimate
d) R-squared
e) coefficient of correlation
Ans: c
Response: See section 12.5 Standard Error of the Estimate
Difficulty: Easy
42. A simple regression model developed for ten pairs of data resulted in a sum of squares of
error, SSE = 125. The standard error of the estimate is _______.
a) 12.5
b) 3.5
c) 15.6
d) 3.95
e) 25
Ans: d
Response: See section 12.5 Standard Error of the Estimate
Difficulty: Medium
43. In regression analysis, R-squared is also called the _______.
a) residual
b) coefficient of determination
c) coefficient of correlation
d) standard error of the estimate
e) sum of squares of regression
Ans: b
Response: See section 12.6 Coefficient of Determination
Difficulty: Easy
44. The numerical value of the coefficient of determination must be _______.
a) between -1 and +1
b) between -1 and 0
c) between 0 and 1
d) equal to SSE/(n-2)
e) between -100 and +100
Ans: c
Response: See section 12.6 Coefficient of Determination
Difficulty: Easy
45. The numerical value of the coefficient of correlation must be _______.
a) between -1 and +1
b) between -1 and 0
c) between 0 and 1
d) equal to SSE/(n-2)
e) between 0 and -1
Ans: a
Response: See section 12.6 Coefficient of Determination
Difficulty: Easy
46. For a certain data set the regression equation is y = 21 - 3x. The correlation coefficient
between y and x in this data set _______.
a) must be 0
b) is negative
c) must be 1
d) is positive
e) must be >1
Ans: b
Response: See section 12.6 Coefficient of Determination
Difficulty: Hard
47. For a certain data set the regression equation is y = 2 + 3x. The correlation coefficient
between y and x in this data set _______.
a) must be 0
b) is negative
c) must be 1
d) is positive
e) must be 3
Ans: d
Response: See section 12.6 Coefficient of Determination
Difficulty: Hard
48. The coefficient of correlation in a simple regression analysis is = - 0.6. The coefficient of
determination for this regression would be _______.
a) 0.6
b) - 0.6 or + 0.6
c) 0.13
d) - 0.36
e) 0.36
Ans: e
Response: See section 12.6 Coefficient of Determination
Difficulty: Hard
49. The proportion of variability of the dependent variable accounted for or explained by the
independent variable is called the _______.
a) sum of squares error
b) coefficient of correlation
c) coefficient of determination
d) covariance
e) regression sum of squares
Ans: c
Response: See section 12.6 Coefficient of Determination
Difficulty: Easy
50. If x and y in a regression model are totally unrelated, _______.
a) the correlation coefficient would be -1
b) the coefficient of determination would be 0
c) the coefficient of determination would be 1
d) the SSE would be 0
e) the MSE would be 0s
Ans: b
Response: See section 12.6 Coefficient of Determination
Difficulty: Easy
51. If there is perfect negative correlation between two sets of numbers, then _______.
a) r = 0
b) r = -1
c) r = +1
d) SSE=1
e) MSE = 1
Ans: b
Response: See section 12.1 Correlation
Difficulty: Easy
52. A researcher has developed a regression model from fourteen pairs of data points. He wants
to test to determine if the slope is significantly different from zero. He uses a two- tailed test and
 = 0.01. The critical table t value is _______.
a) 2.650
b) 3.012
c) 3.055
d) 2.718
e) 2.168
Ans: c
Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing
the Overall Model
Difficulty: Easy
53. A researcher has developed a regression model from fifteen pairs of data points. He wants to
test to determine if the slope is significantly different from zero. He uses a two-tailed test and  =
0.10. The critical table t value is _______.
a) 1.771
b) 1.350
c) 1.761
d) 2.145
e) 2.068
Ans: a
Response: See section 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing
the Overall Model
Difficulty: Easy
54. In a regression analysis if SST = 200 and SSR = 200, r 2 = _________.
a) 0.25
b) 0.75
c) 0.00
d) 1.00
e) -1.00
Ans: d
Response: See section 12.6 Coefficient of Determination
Difficulty: Easy
55. A manager wishes to predict the annual cost (y) of an automobile based on the number of
miles (x) driven. The following model was developed: y = 1,550 + 0.36x.
If a car is driven 15,000 miles, the predicted cost is ____________.
a) 2090
b) 3850
c) 7400
d) 6950
e) 5400
Ans: d
Response: See section 12.8 Estimation
Difficulty: Medium
56. A manager wishes to predict the annual cost (y) of an automobile based on the number of
miles (x) driven. The following model was developed: y = 1,550 + 0.36x.
If a car is driven 30,000 miles, the predicted cost is _____________.
a) 10,800
b) 12,350
c) 2,630
d) 9,250
e) 10,250
Ans: b
Response: See section 12.8 Estimation
Difficulty: Easy
57. A manager wishes to predict the annual cost (y) of an automobile based on the number of
miles (x) driven. The following model was developed: y = 1,550 + .36x.
If a car is driven 20,000 miles, the predicted cost is ____________.
a) 7,200
b) 5,650
c) 8,750
d) 2,270
e) 6,750
Ans: c
Response: See section 12.8 Estimation
Difficulty: Easy
58. A manager wants to predict the cost (y) of travel for salespeople based on the number of days
(x) spent on each sales trip. The following model has been developed: y = $400 + 120x. If a trip
took 4 days, the predicted cost of the trip is _____________.
a) 480
b) 880
c) 524
d) 2080
e) 1080
Ans: b
Response: See section 12.8 Estimation
Difficulty: Easy
59. A manager wants to predict the cost (y) of travel for salespeople based on the number of days
(x) spent on each sales trip. The following model has been developed: y = $400 + 120x. If a trip
took 3 days, the predicted cost of the trip is _____________.
a) 760
b) 360
c) 523
d) 1560
e) 1080
Ans: a
Response: See section 12.8 Estimation
Difficulty: Easy
60. The following data is to be used to construct a regression model:
x
y
5
8
7
9
4 15 12 9
12 26 16 13
The value of the intercept is ________.
a) 1.36
b) 2.16
c) 0.68
d) 0.57
e) 2.36
Ans: b
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: Medium
61. The following data is to be used to construct a regression model:
x
y
5
8
7
9
4 15 12 9
12 26 16 13
The value of the slope is ____________.
a) 2.36
b) 2.16
c) 0.68
d) 0.57
e) 1.36
Ans: e
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: Medium
62. The following data is to be used to construct a regression model:
x
y
5
8
7
9
4 15 12 9
12 26 16 13
The regression equation is _______________.
a) y = 2.16 + 1.36x
b) y = 1.36 + 2.16x
c) y = 0.68 + 0.57x
d) y = 0.57 + 0.68x
e) y = 0.57 - 0.68x
Ans: a
Response: See section 12.3 Determining the Equation of the Regression Line
Difficulty: High
63. The following residuals plot indicates _______________.
a) a nonlinear relation
b) a nonconstant error variance
c) the simple regression assumptions are met
d) the sample is biased
e) the sample is random
Ans: b
Response: See section 12.4 Residual Analysis
Difficulty: Easy
64. The following residuals plot indicates _______________.
a) a nonlinear relation
b) a nonconstant error variance
c) the simple regression assumptions are met
d) the sample is biased
e) a random sample
Ans: a
Response: See section 12.4 Residual Analysis
Difficulty: Easy
65. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing
costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is
production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in
$100's). Regression analysis of the data yielded the following tables.
Coefficients Standard Error t Statistic p-value
Intercept
3.996
1.161268
3.441065 0.004885
x
0.358
0.102397
3.496205 0.004413
Source
Regression
Residual
Total
df
SS
MS
F
1 9.858769 9.858769 12.22345
11
8.872 0.806545
12 18.73077
Louis's regression model is ________________.
a) y = -0.358 + 3.996x
b) y = 0.358 + 3.996x
Se = 0.898
2
r = 0.526341
c) y = -3.996 + 0.358x
d) y = 3.996 - 0.358x
e) y = 3.996 + 0.358x
Ans: e
Response: See section 12.10 Interpreting the Output
Difficulty: Easy
66. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing
costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is
production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in
$100's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept
3.996
x
0.358
Source
Regression
Residual
Total
Standard Error t Statistic p-value
1.161268
3.441065 0.004885
0.102397
3.496205 0.004413
df
SS
MS
F
1 9.858769 9.858769 12.22345
11
8.872 0.806545
12 18.73077
Se = 0.898
2
r = 0.526341
The correlation coefficient between Louis's variables is ________________.
a) -0.73
b) 0.73
c) 0.28
d) -0.28
e) 0.00
Ans: b
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
67. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing
costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is
production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in
$100's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept
3.996
Standard Error t Statistic p-value
1.161268
3.441065 0.004885
x
Source
Regression
Residual
Total
0.358
0.102397
3.496205 0.004413
df
SS
MS
F
1 9.858769 9.858769 12.22345
11
8.872 0.806545
12 18.73077
Se = 0.898
2
r = 0.526341
Louis's sample size (n) is ________________.
a) 13
b) 14
c) 12
d) 24
e) 1
Ans: a
Response: See section 12.10 Interpreting the Output
Difficulty: Easy
68. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing
costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is
production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in
$100's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept
3.996
x
0.358
Source
Regression
Residual
Total
Standard Error t Statistic p-value
1.161268
3.441065 0.004885
0.102397
3.496205 0.004413
df
SS
MS
F
1 9.858769 9.858769 12.22345
11
8.872 0.806545
12 18.73077
Using  = 0.05, Louis should ________________.
a) increase the sample size
b) suspend judgment
c) not reject H0: 1 = 0
d) reject H0: 1 = 0
e) do not reject H0: 0 = 0
Ans: d
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
Se = 0.898
r2 = 0.526341
69. Louis Katz, a cost accountant at Papalote Plastics, Inc. (PPI), is analyzing the manufacturing
costs of a molded plastic telephone handset produced by PPI. Louis's independent variable is
production lot size (in 1,000's of units), and his dependent variable is the total cost of the lot (in
$100's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept
3.996
x
0.358
Source
Regression
Residual
Total
Standard Error t Statistic p-value
1.161268
3.441065 0.004885
0.102397
3.496205 0.004413
df
SS
MS
F
1 9.858769 9.858769 12.22345
11
8.872 0.806545
12 18.73077
Se = 0.898
2
r = 0.526341
For a lot size of 10,000 handsets, Louis' model predicts total cost will be _____.
a) $4,031.80
b) $757.60
c) $3,960.20
d) $354.01
e) $1873.077
Ans: b
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
70. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is
analyzing household budget data collected by her firm. Abby's dependent variable is monthly
household expenditures on groceries (in $'s), and her independent variable is annual household
income (in $1,000's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept 39.14942
x
1.792312
Source
Regression
Residual
Total
Standard Error t Statistic p-value
22.30182
1.755436 0.109712
0.407507
4.398234 0.001339
df
SS
MS
F
1 16850.99 16850.99 19.34446
9 7839.915 871.1017
10 24690.91
Se = 29.51443
r2 = 0.682478
Abby's regression model is __________.
a) y = 39.15 + 2.79x
b) y = 39.15 - 1.79x
c) y = 1.79 + 39.15x
d) y = -1.79 + 39.15x
e) y = 39.15 + 1.79x
Ans: e
Response: See section 12.10 Interpreting the Output
Difficulty: Easy
71. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is
analyzing household budget data collected by her firm. Abby's dependent variable is monthly
household expenditures on groceries (in $'s), and her independent variable is annual household
income (in $1,000's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept 39.14942
x
1.792312
Source
Regression
Residual
Total
Standard Error t Statistic p-value
22.30182
1.755436 0.109712
0.407507
4.398234 0.001339
df
SS
MS
F
1 16850.99 16850.99 19.34446
9 7839.915 871.1017
10 24690.91
Se = 29.51443
r2 = 0.682478
The correlation coefficient between the two variables in this regression is __________.
a) 0.682478
b) -0.83
c) 0.83
d) -0.68
e) 1.0008
Ans: c
Response: See section 12.10 Interpreting the Output
Difficulty: Easy
72. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is
analyzing household budget data collected by her firm. Abby's dependent variable is monthly
household expenditures on groceries (in $'s), and her independent variable is annual household
income (in $1,000's). Regression analysis of the data yielded the following tables.
Intercept
x
Source
Regression
Residual
Total
Coefficients
39.14942
1.792312
Standard Error t Statistic p-value
22.30182
1.755436 0.109712
0.407507
4.398234 0.001339
df
SS
MS
F
1 16850.99 16850.99 19.34446
9 7839.915 871.1017
10 24690.91
Se = 29.51443
r2 = 0.682478
Abby's sample size (n) is __________.
a) 8
b) 10
c) 11
d) 20
e) 12
Ans: c
Response: See section 12.10 Interpreting the Output
Difficulty: Easy
73. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is
analyzing household budget data collected by her firm. Abby's dependent variable is monthly
household expenditures on groceries (in $'s), and her independent variable is annual household
income (in $1,000's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept 39.14942
x
1.792312
Source
Regression
Residual
Total
Standard Error t Statistic p-value
22.30182
1.755436 0.109712
0.407507
4.398234 0.001339
df
SS
MS
F
1 16850.99 16850.99 19.34446
9 7839.915 871.1017
10 24690.91
Using  = 0.05, Abby should ________________.
a) reject H0: 1 = 0
b) not reject H0: 1 = 0
c) increase the sample size
d) suspend judgment
e) reject H0: 0 = 0
Ans: a
Se = 29.51443
r2 = 0.682478
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
74. Abby Kratz, a market specialist at the market research firm of Saez, Sikes, and Spitz, is
analyzing household budget data collected by her firm. Abby's dependent variable is monthly
household expenditures on groceries (in $'s), and her independent variable is annual household
income (in $1,000's). Regression analysis of the data yielded the following tables.
Coefficients
Intercept 39.14942
x
1.792312
Source
Regression
Residual
Total
Standard Error t Statistic p-value
22.30182
1.755436 0.109712
0.407507
4.398234 0.001339
df
SS
MS
F
1 16850.99 16850.99 19.34446
9 7839.915 871.1017
10 24690.91
Se = 29.51443
r2 = 0.682478
For a household with $50,000 annual income, Abby's model predicts monthly grocery
expenditures of ________________.
a) $150.35
b) $50.35
c) $1,959.29
d) $128.65
e) $1286.50
Ans: d
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
75. Alan Bissell, market analyst for City Sound Mart, is analyzing the relation between heavy
metal CD sales and the size of the teenage population. He gathers data from six sales districts.
Alan’s dependent variable is annual heavy metal CD sales (in $1,000,000's), and his independent
variable is teenage population (in 1,000's). Regression analysis of the data yielded the following
tables.
Coefficients Standard Error t Statistic p-value
Intercept -0.14156
0.292143
-0.48455 0.653331
x
0.105195
0.013231
7.950352 0.001356
Source df
SS
MS
F
Regression 1 3.550325 3.550325 63.20809
Se = 0.237
r2 = 0.940483
Residual
Total
4
5
0.224675 0.056169
3.775
Alan’s regression model can be written as: __________.
a) y = 7.950352 - 0.48455x
b) y = -0.48455 + 7.950352x
c) y = -0.14156 + 0.105195x
d) y = 0.105195 - 0.14156x
e) y = 0.105195 + 0.14156x
Ans: c
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
76. Alan Bissell, market analyst for City Sound Mart, is analyzing the relation between heavy
metal CD sales and the size of the teenage population. He gathers data from six sales districts.
Alan’s dependent variable is annual heavy metal CD sales (in $1,000,000's), and his independent
variable is teenage population (in 1,000's). Regression analysis of the data yielded the following
tables.
Intercept
x
Source
Regression
Residual
Total
Coefficients
-0.14156
0.105195
Standard Error t Statistic p-value
0.292143
-0.48455 0.653331
0.013231
7.950352 0.001356
df
SS
MS
F
1 3.550325 3.550325 63.20809
4 0.224675 0.056169
5
3.775
Se = 0.237
r2 = 0.940483
The numerical value of the correlation coefficient between the CD sales and the size of teenage
population is __________.
a) 0.969785
b) 0.940483
c) 0.224675
d) -0.14156
e) 1.000000
Ans: a
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
77. Alan Bissell, market analyst for City Sound Mart, is analyzing the relation between heavy
metal CD sales and the size of the teenage population. He gathers data from six sales districts.
Alan’s dependent variable is annual heavy metal CD sales (in $1,000,000's), and his independent
variable is teenage population (in 1,000's). Regression analysis of the data yielded the following
tables.
Coefficients
Intercept -0.14156
x
0.105195
Source
Regression
Residual
Total
Standard Error t Statistic p-value
0.292143
-0.48455 0.653331
0.013231
7.950352 0.001356
df
SS
MS
F
1 3.550325 3.550325 63.20809
4 0.224675 0.056169
5
3.775
Se = 0.237
r2 = 0.940483
Alan’s sample size is __________.
a) 2
b) 4
c) 6
d) 8
e) 10
Ans: c
Response: See section 12.10 Interpreting the Output
Difficulty: Easy
78. Alan Bissell, market analyst for City Sound Mart, is analyzing the relation between heavy
metal CD sales and the size of the teenage population. He gathers data from six sales districts.
Alan’s dependent variable is annual heavy metal CD sales (in $1,000,000's), and his independent
variable is teenage population (in 1,000's). Regression analysis of the data yielded the following
tables.
Coefficients
Intercept -0.14156
x
0.105195
Source
Regression
Residual
Total
Standard Error t Statistic p-value
0.292143
-0.48455 0.653331
0.013231
7.950352 0.001356
df
SS
MS
F
1 3.550325 3.550325 63.20809
4 0.224675 0.056169
5
3.775
Se = 0.237
r2 = 0.940483
Using  = 0.05, Alan should ________________.
a) increase the sample size
b) not reject H0: 1 = 0
c) reject H0: 1 = 0
d) suspend judgment
e) reject H0: 0 = 0
Ans: c
Response: See section 12.10 Interpreting the Output
Difficulty: Medium
79. Alan Bissell, market analyst for City Sound Mart, is analyzing the relation between heavy
metal CD sales and the size of the teenage population. He gathers data from six sales districts.
Alan’s dependent variable is annual heavy metal CD sales (in $1,000,000's), and his independent
variable is teenage population (in 1,000's). Regression analysis of the data yielded the following
tables.
Intercept
x
Source
Regression
Residual
Total
Coefficients
-0.14156
0.105195
Standard Error t Statistic p-value
0.292143
-0.48455 0.653331
0.013231
7.950352 0.001356
df
SS
MS
F
1 3.550325 3.550325 63.20809
4 0.224675 0.056169
5
3.775
Se = 0.237
r2 = 0.940483
For a sales district with 20,000 teenagers, Alan’s model predicts annual CD sales of
________________.
a) $1,947.08
b) $2,104.04
c) $2,103,900
d) $1,962,340
e) $2,908,089
Ans: d
Response: See section 12.10 Interpreting the Output
Difficulty: Medium