Chapter 13 - Regression and Forecasting Models
1. Forecasting models can be divided into three groups. They are:
a. time series, optimization, and simulation methods
b. judgmental, regression, and extrapolation methods
c. judgmental, random, and linear methods
d. linear, non-linear, and extrapolation methods
ANSWER:
b
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1
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13.1 Introduction
OTHER:
BUSPROG - Communication |DISC - Regression and Forecasting
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2. In regression analysis, the variable we are trying to explain or predict is called the
a. independent variable
b. dependent variable
c. regression variable
d. statistical variable
ANSWER:
b
POINTS:
1
DIFFICULTY:
Easy |Bloom's Knowledge
QUESTION TYPE: Multiple Choice
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TOPICS:
13.2 Overview of Regression Models
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BUSPROG - Communication |DISC - Regression and Forecasting
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3. In multiple regression, the regression coefficients reflect the expected change in:
a. Y when the associated X value increases by one unit, holding the other variables constant
b. X when the associated Y value increases by one unit, holding the other variables constant
c. Y when the associated X value decreases by one unit, holding the other variables constant
d. X when the associated Y value decreases by one unit, holding the other variables constant
ANSWER:
a
POINTS:
1
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Easy |Bloom's Evaluation
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TOPICS:
13.4 Multiple Regression Models
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Chapter 13 - Regression and Forecasting Models
4. The biggest challenge of regression is:
a. differentiating the independent variable(s) from the dependent variable(s)
b. determining which independent variable(s) to include
c. collecting accurate data
d. properly coding the variables
ANSWER:
b
POINTS:
1
DIFFICULTY:
Easy |Bloom's Evaluation
QUESTION TYPE: Multiple Choice
HAS VARIABLES: False
TOPICS:
13.4 Multiple Regression Models
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5. The adjusted R2 adjusts R2 for:
a. non-linearity
b. outliers
c. low correlation
d. the number of explanatory variables in a multiple regression model
ANSWER:
d
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
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13.4 Multiple Regression Models - Solution
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6. A "fan" shape in a scatterplot indicates:
a. nonconstant error variance
b. a nonlinear relationship
c. the absence of outliers
d. sampling error
ANSWER:
a
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
QUESTION TYPE: Multiple Choice
HAS VARIABLES: False
TOPICS:
13.4 Multiple Regression Models - A Caution about Regression Assumptions
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Chapter 13 - Regression and Forecasting Models
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7. The term autocorrelation refers to:
a. the analyzed data refers to itself
b. the sample is related too closely to the population
c. the data are in a loop (values repeat themselves)
d. time series variables are usually related to their own past values
ANSWER:
d
POINTS:
1
DIFFICULTY:
Easy |Bloom's Comprehension
QUESTION TYPE: Multiple Choice
HAS VARIABLES: False
TOPICS:
13.4 Multiple Regression Models - A Caution about Regression Assumptions
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
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8. Which of the following is not one of the commonly used summary measures for forecast errors?
a. MAE (mean absolute error)
b. MFE (mean forecast error)
c. RMSE (root mean square error)
d. MAPE (mean absolute percentage error)
ANSWER:
b
POINTS:
1
DIFFICULTY:
Easy |Bloom's Knowledge
QUESTION TYPE: Multiple Choice
HAS VARIABLES: False
TOPICS:
13.5 Overview of Time Series Models - Measures of Forecast Error
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
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9. When using the moving average method, you must select ____ which represent(s) the number of terms in the moving
average.
a. a smoothing constant
b. the explanatory variables
c. an alpha value
d. a span
ANSWER:
d
POINTS:
1
DIFFICULTY:
Easy |Bloom's Comprehension
QUESTION TYPE: Multiple Choice
HAS VARIABLES: False
TOPICS:
13.6 Moving Average Models
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Chapter 13 - Regression and Forecasting Models
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10. A model that uses temperature, season of the year (fall, winter, spring, summer), and whether or not it is a weekend, to
predict the # of customers for the day would include how many independent variables?
a. 3
b. 5
c. 6
d. 7
ANSWER:
b
POINTS:
1
DIFFICULTY:
Easy |Bloom's Comprehension
QUESTION TYPE: Multiple Choice
HAS VARIABLES: False
TOPICS:
13.7 Exponential Smoothing Models - Winter's Method for Seasonality
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
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DATE MODIFIED: 10/21/2017 10:04 PM
11. The residual is defined as the difference between the actual and predicted, or fitted values of the response variable.
a. True
b. False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.2 Overview of Regression Models - The Least Squares Line
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
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12. The least squares line is the line that minimizes the sum of the residuals.
a. True
b. False
ANSWER:
False
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.2 Overview of Regression Models - The Least Squares Line
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BUSPROG - Analytic |DISC - Regression and Forecasting
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Chapter 13 - Regression and Forecasting Models
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13. A useful graph in almost any regression analysis is a scatterplot of residuals (on the vertical axis) versus fitted values
(on the horizontal axis), where a "good" fit not only has small residuals, but it has residuals scattered randomly around
zero with no apparent pattern.
a. True
b. False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.3 Simple Regression Models - Discussion of the Results
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:51 PM
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14. In reference to the equation
, the value 0.10 is the expected change in Y per unit change in X.
a. True
b. False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy |Bloom's Evaluation
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.3 Simple Regression Models
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
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15. In regression analysis, we can often use the standard error of estimate se to judge which of several potential regression
equations is the most useful.
a. True
b. False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.2 Overview of Regression Models - Measures of Goodness of Fit
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
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Chapter 13 - Regression and Forecasting Models
16. The percentage of variation explained R2 is the square of the correlation between the observed Y values and the fitted
Y values.
a. True
b. False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.4 Multiple Regression Models - Discussion of the Results
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:51 PM
DATE MODIFIED: 10/21/2017 10:04 PM
17. The adjusted R2 is used primarily to monitor whether extra explanatory variables really belong in a multiple regression
model.
a. True
b. False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.4 Multiple Regression Models - Discussion of the Results
OTHER:
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18. A time series can consist of four different components: trend, seasonal, cyclical, and random (or noise).
a. True
b. False
ANSWER:
True
POINTS:
1
DIFFICULTY:
Easy |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.5 Overview of Time Series Models
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:51 PM
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19. The smoothing constant used in simple exponential smoothing is analogous to the span in moving averages.
a. True
b. False
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Chapter 13 - Regression and Forecasting Models
ANSWER:
POINTS:
DIFFICULTY:
QUESTION TYPE:
HAS VARIABLES:
TOPICS:
OTHER:
DATE CREATED:
DATE MODIFIED:
True
1
Easy |Bloom's Comprehension
True / False
False
13.7 Exponential Smoothing Models - Simple Exponential Smoothing
BUSPROG - Analytic |DISC - Regression and Forecasting
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10/21/2017 10:04 PM
20. Winter's method is an exponential smoothing method, which is appropriate for a series with trend but no seasonality.
a. True
b. False
ANSWER:
False
POINTS:
1
DIFFICULTY:
Easy |Bloom's Comprehension
QUESTION TYPE: True / False
HAS VARIABLES: False
TOPICS:
13.7 Exponential Smoothing Models - Winter's Method for Seasonality
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:51 PM
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Exhibit 13-1
An express delivery service company recently conducted a study to investigate the relationship between the cost of
shipping a package (Y), the package weight in pounds (X1), and the distance shipped in miles (X2). Twenty packages were
randomly selected from among the large number received for shipment, and a detailed analysis of the shipping cost was
conducted for each package. The sample information is shown in the table below:
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Chapter 13 - Regression and Forecasting Models
21. Refer to Exhibit 13-1. Estimate a simple linear regression model involving shipping cost and package weight. Interpret
the slope coefficient of the least squares line as well as R2.
ANSWER:
POINTS:
DIFFICULTY:
QUESTION TYPE:
HAS VARIABLES:
PREFACE NAME:
TOPICS:
OTHER:
DATE CREATED:
DATE MODIFIED:
As the package weight increases by one pound, the cost of shipping the package increases by $1.49
on average. This simple linear regression model explains 59.85% of the total variation in the cost of
shipment.
1
Moderate |Bloom's Analysis
Subjective Short Answer
False
Exhibit 13-1
13.3 Simple Regression Models - Discussion of the Results
BUSPROG - Analytic |DISC - Regression and Forecasting
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Chapter 13 - Regression and Forecasting Models
22. Refer to Exhibit 13-1. Add the second explanatory variable (distance shipped) to the regression model. Estimate and
interpret the slopes of this expanded model.
ANSWER:
POINTS:
DIFFICULTY:
QUESTION TYPE:
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PREFACE NAME:
TOPICS:
OTHER:
DATE CREATED:
DATE MODIFIED:
Now, holding all else constant, the cost of shipping a package rises by approximately $1.29 when the
package weight increases by one pound. Furthermore, holding all else constant, the cost of shipping a
package rises by approximately $0.04 when the distance shipped increases by one mile.
1
Moderate |Bloom's Analysis
Subjective Short Answer
False
Exhibit 13-1
13.4 Multiple Regression Models - Discussion of the Results
BUSPROG - Analytic |DISC - Regression and Forecasting
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10/21/2017 10:04 PM
23. Refer to Exhibit 13-1. How does the R2 value for this multiple regression model compare to that of the simple
regression model estimated above? Interpret the adjusted R2 values for the two models.
ANSWER:
Both the R2 and adjusted R2 values have increased considerably with the addition of the second
explanatory variable; the multiple regression model fits the given data better than did the simple
linear model. The R2 and adjusted R2 values are quite similar for the multiple regression model.
Therefore, both explanatory variables are adding to the explanation of the variation in the cost of
shipment.
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Analysis
QUESTION TYPE: Subjective Short Answer
HAS VARIABLES: False
PREFACE NAME: Exhibit 13-1
TOPICS:
13.4 Multiple Regression Models - Discussion of the Results
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:51 PM
DATE MODIFIED: 10/21/2017 10:04 PM
Exhibit 13-2
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Chapter 13 - Regression and Forecasting Models
The station manager of a local television station is interested in predicting the amount of television (in hours) that people
will watch in the viewing area. The explanatory variables are: X1 age (in years), X2 education (highest level obtained, in
years) and X3 family size (number of family members in household). The multiple regression output is shown below:
Summary measures
Multiple R
0.8440
R-Square
0.7123
Adj R-Square 0.6644
StErr of
0.5598
Estimate
ANOVA Table
Source
df
Explained
3
Unexplained
18
SS
13.9682
5.6413
MS
4.6561
0.3134
F
14.8564
p-value
0.0000
Regression coefficients
Constant
Age
Education
Family Size
Coefficient
1.683
−0.0498
0.2135
0.0405
Std Err
1.1696
0.0199
0.0503
0.0784
t-value
1.4389
−2.5018
4.2426
0.5168
p-value
0.1674
0.0222
0.0005
0.6116
24. Refer to Exhibit 13-2. Use the information above to estimate the linear regression model.
ANSWER:
POINTS:
DIFFICULTY:
QUESTION TYPE:
HAS VARIABLES:
PREFACE NAME:
TOPICS:
OTHER:
DATE CREATED:
DATE MODIFIED:
1
Easy |Bloom's Analysis
Subjective Short Answer
False
Exhibit 13-2
13.4 Multiple Regression Models - Discussion of the Results
BUSPROG - Analytic |DISC - Regression and Forecasting
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25. Refer to Exhibit 13-2. Interpret each of the estimated regression coefficients of the regression model above.
ANSWER:
This model shows that the number of hours people spend watching television decreases by 0.0498
hours on average with every additional year in age (while holding education level and family size
constant); increases by 0.2135 hours on average with a person's education level increasing by one
year (while holding age and family size constant), and increases by 0.0405 hours on average as the
family size increases by one person (while holding age and education level constant).
POINTS:
1
DIFFICULTY:
Easy |Bloom's Analysis
QUESTION TYPE: Subjective Short Answer
HAS VARIABLES: False
PREFACE NAME: Exhibit 13-2
TOPICS:
13.4 Multiple Regression Models - Discussion of the Results
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Chapter 13 - Regression and Forecasting Models
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26. Refer to Exhibit 13-2. Identify and interpret the percentage of variation explained (R2) for the model.
ANSWER:
The percentage of variation explained R2 = 0.7123; this represents 71.23% of the variation in the
hours spent watching television can be explained by this regression equation.
POINTS:
1
DIFFICULTY:
Easy |Bloom's Analysis
QUESTION TYPE: Subjective Short Answer
HAS VARIABLES: False
PREFACE NAME: Exhibit 13-2
TOPICS:
13.4 Multiple Regression Models - Discussion of the Results
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:51 PM
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Exhibit 13-3
The quarterly numbers of applications for home mortgage loans at a branch office of a large bank are recorded in the table
below.
Quarter-Year
1-13
2-13
3-13
4-13
1-14
2-14
3-14
4-14
1-15
2-15
3-15
4-15
1-16
2-16
3-16
4-16
1-17
2-17
3-17
4-17
1-18
2-18
Applications
96
114
112
81
97
103
120
99
105
110
117
96
74
94
100
96
95
122
113
100
102
96
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Chapter 13 - Regression and Forecasting Models
3-18
4-18
116
98
27. Refer to Exhibit 13-3. Obtain a time series chart. Which of the forecasting models (one or more) do you think should
be used for forecasting based on this chart? Why?
ANSWER:
There is no apparent trend or seasonality, so a simple smoothing model (moving average or simple
exponential smoothing) is a reasonable choice. It appears the data are random, so it may be difficult
to find a model that fits well.
POINTS:
DIFFICULTY:
QUESTION TYPE:
HAS VARIABLES:
PREFACE NAME:
TOPICS:
OTHER:
DATE CREATED:
DATE MODIFIED:
1
Moderate |Bloom's Analysis
Subjective Short Answer
False
Exhibit 13-3
13.5 Overview of Time Series Models
BUSPROG - Analytic |DISC - Regression and Forecasting
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10/21/2017 10:04 PM
28. Refer to Exhibit 13-3. Use a moving average model to forecast these data, requesting 4 quarters of future forecasts.
Use a span of 4 quarters.
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Chapter 13 - Regression and Forecasting Models
ANSWER:
POINTS:
DIFFICULTY:
QUESTION TYPE:
HAS VARIABLES:
PREFACE NAME:
TOPICS:
OTHER:
DATE CREATED:
DATE MODIFIED:
1
Moderate |Bloom's Analysis
Subjective Short Answer
False
Exhibit 13-3
13.6 Moving Average Models
BUSPROG - Analytic |DISC - Regression and Forecasting
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10/21/2017 10:04 PM
29. Refer to Exhibit 13-3. Use simple exponential smoothing to forecast these data, requesting 4 quarters of future
forecasts. Use the default smoothing constant of 0.10. Is this better than the moving average model?
ANSWER:
The MAPE has increased from 9.24% to 9.5%, so this model fits the data worse than the moving
average model.
POINTS:
1
DIFFICULTY:
Moderate |Bloom's Analysis
QUESTION TYPE: Subjective Short Answer
HAS VARIABLES: False
PREFACE NAME: Exhibit 13-3
TOPICS:
13.7 Exponential Smoothing Models - Simple Exponential Smoothing
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:52 PM
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Chapter 13 - Regression and Forecasting Models
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30. Refer to Exhibit 13-3. Obtain a simple exponential smoothing forecast again, this time optimizing the smoothing
constant. Does it make much of an improvement?
ANSWER:
It doesn't seem to matter much whether we use a smoothing constant of 0.10 or the optimal smoothing
constant (which turns out to be 0.079). Neither model fits the data very well, and the MAPE is still
higher than the MAPE for the moving average model.
POINTS:
1
DIFFICULTY:
Easy |Bloom's Analysis
QUESTION TYPE: Subjective Short Answer
HAS VARIABLES: False
PREFACE NAME: Exhibit 13-3
TOPICS:
13.7 Exponential Smoothing Models - Simple Exponential Smoothing
OTHER:
BUSPROG - Analytic |DISC - Regression and Forecasting
DATE CREATED: 5/17/2017 3:52 PM
DATE MODIFIED 10/21/2017 10:04 PM
:
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