Quantitative Analysis for Management, 13e (Render et al.)
Chapter 5 Forecasting
1) The Delphi method of forecasting is both iterative and qualitative.
Answer: TRUE
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
2) The three categories of forecasting models are time series, quantitative, and qualitative.
Answer: FALSE
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
3) Time series models extrapolate historical data from the variable of interest.
Answer: TRUE
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
4) Time series models rely on judgment in an attempt to incorporate qualitative or subjective
factors into the forecasting model.
Answer: FALSE
Diff: Easy
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
5) A time series exhibiting only random variations is best fit by a horizontal line.
Answer: TRUE
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
1
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6) An exponential forecasting method is a time series forecasting method.
Answer: TRUE
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
7) A trend-projection forecasting method is a causal forecasting method.
Answer: FALSE
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
8) A season represents a longer period of time than a cycle.
Answer: FALSE
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
9) The most common quantitative causal model is regression analysis.
Answer: TRUE
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
10) The trend component of a time series captures whether the level of the variable of interest is
generally increasing or decreasing over time.
Answer: TRUE
Diff: Easy
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
2
Copyright © 2018 Pearson Education, Inc.
11) The sales force composite method of forecasting uses the opinions of customers or potential
customers regarding their future purchasing plans.
Answer: FALSE
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
12) The naïve forecast for the next period is the actual value observed in the current period.
Answer: TRUE
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
13) Mean absolute deviation (MAD) is simply the sum of forecast errors.
Answer: FALSE
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
14) Time series models enable the forecaster to include specific representations of various
qualitative and quantitative factors.
Answer: FALSE
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
15) Four components of time series are trend, moving average, exponential smoothing, and
seasonality.
Answer: FALSE
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
3
Copyright © 2018 Pearson Education, Inc.
16) The fewer the periods over which one takes a moving average, the more accurately the
resulting forecast mirrors the actual data of the most recent time periods.
Answer: TRUE
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
17) In a weighted moving average, the weights assigned must sum to 1.
Answer: FALSE
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
18) A scatter diagram for a time series may be plotted on a two-dimensional graph with the
horizontal axis representing the variable to be forecast (such as sales).
Answer: FALSE
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
19) Scatter diagrams can be useful in spotting trends or cycles in data over time.
Answer: TRUE
Diff: Easy
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
20) Exponential smoothing cannot be used for data with a trend.
Answer: FALSE
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
4
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21) In a second order exponential smoothing, a low β gives less weight to more recent trends.
Answer: TRUE
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
22) An advantage of exponential smoothing over a simple moving average is that exponential
smoothing requires one to retain less data.
Answer: TRUE
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
23) When the smoothing constant α = 0, the exponential smoothing model is equivalent to the
naïve forecasting model.
Answer: FALSE
Diff: Difficult
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
24) Multiple regression models use dummy variables to adjust for seasonal variations in an
additive TIME SERIES model.
Answer: TRUE
Diff: Moderate
Topic: FORECASTING MODELS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Concept
25) Multiple regression can be used to develop a multiplicative decomposition model.
Answer: FALSE
Diff: Moderate
Topic: FORECASTING MODELS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Concept
5
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26) A seasonal index must be between -1 and +1.
Answer: FALSE
Diff: Moderate
Topic: ADJUSTING FOR SEASONAL VARIATIONS
LO: 5.6: Manipulate data to account for seasonal variations.
AACSB: Analytical thinking
Classification: Concept
27) The exponential smoothing with trend model uses two smoothing constants, one constant
works as in the exponential smoothing model and the other adjusts the line for presence of a
trend.
Answer: TRUE
Diff: Easy
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Concept
28) Deseasonalized data can be modeled as a straight line.
Answer: TRUE
Diff: Moderate
Topic: ADJUSTING FOR SEASONAL VARIATIONS
LO: 5.6: Manipulate data to account for seasonal variations.
AACSB: Analytical thinking
Classification: Concept
29) When the smoothing constant α = 1, the exponential smoothing model is equivalent to the
naïve forecasting model.
Answer: TRUE
Diff: Difficult
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
30) Multiple regression may be used to forecast both trend and seasonal components present in a
time series.
Answer: TRUE
Diff: Moderate
Topic: FORECASTING MODELS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Concept
6
Copyright © 2018 Pearson Education, Inc.
31) Adaptive smoothing is analogous to exponential smoothing where the coefficients α and β
are periodically updated to improve the forecast.
Answer: TRUE
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
32) Bias is the average error of a forecast model.
Answer: TRUE
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
33) Which of the following is not classified as a qualitative forecasting model?
A) exponential smoothing
B) Delphi method
C) jury of executive opinion
D) sales force composite
Answer: A
Diff: Easy
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
34) A judgmental forecasting technique that uses decision makers, staff personnel, and
respondent to determine a forecast is called
A) exponential smoothing.
B) the Delphi method.
C) jury of executive opinion.
D) sales force composite.
Answer: B
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
7
Copyright © 2018 Pearson Education, Inc.
35) Which of the following is considered a causal method of forecasting?
A) exponential smoothing
B) moving average
C) linear regression
D) Delphi method
Answer: C
Diff: Moderate
Topic: TYPES OF FORECASTING MODELS
LO: 5.1: Understand and know when to use various families of forecasting models.
AACSB: Analytical thinking
Classification: Concept
36) A graphical plot with sales on the Y axis and time on the X axis is a
A) scatter diagram.
B) trend projection.
C) radar chart.
D) line graph.
Answer: A
Diff: Moderate
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Concept
37) Which of the following statements about scatter diagrams is true?
A) Time is always plotted on the y-axis.
B) It can depict the relationship among three variables simultaneously.
C) It is helpful when forecasting with qualitative data.
D) The variable to be forecasted is placed on the y-axis.
Answer: D
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
38) Which of the following is a technique used to determine forecasting accuracy?
A) exponential smoothing
B) regression
C) Delphi method
D) mean absolute percent error
Answer: D
Diff: Easy
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
8
Copyright © 2018 Pearson Education, Inc.
39) When is the exponential smoothing model equivalent to the naïve forecasting model?
A) α = 0
B) α = 0.5
C) α = 1
D) never
Answer: C
Diff: Difficult
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
40) Enrollment in a particular class for the last four semesters has been 120, 126, 110, and 130.
Suppose a one-semester moving average was used to forecast enrollment (this is sometimes
referred to as a naïve forecast). Thus, the forecast for the second semester would be 120, for the
third semester it would be 126, and for the last semester it would be 110. What would the MSE
be for this situation?
A) 196.00
B) 230.67
C) 100.00
D) 42.00
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
41) Which of the following methods tells whether the forecast tends to be too high or too low?
A) MAD
B) MSE
C) MAPE
D) bias
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
9
Copyright © 2018 Pearson Education, Inc.
42) Assume that you have tried three different forecasting models. For the first, the MAD = 2.5,
for the second, the MSE = 10.5, and for the third, the MAPE = 2.7. We can then say
A) the third method is the best.
B) the second method is the best.
C) method two is the least preferred.
D) We cannot make a determination as to which method is best.
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
43) Which of the following methods gives an indication of the percentage of forecast error?
A) MAD
B) MSE
C) MAPE
D) decomposition
Answer: C
Diff: Easy
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
44) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18,
14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a two-day
moving average.
A) 14
B) 13
C) 15
D) 28
Answer: A
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
10
Copyright © 2018 Pearson Education, Inc.
45) As one increases the number of periods used in the calculation of a moving average
A) greater emphasis is placed on more recent data.
B) less emphasis is placed on more recent data.
C) the emphasis placed on more recent data remains the same.
D) it requires a computer to automate the calculations.
Answer: B
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
46) Enrollment in a particular class for the last four semesters has been 122, 128, 100, and 155
(listed from oldest to most recent). The best forecast of enrollment next semester, based on a
three-semester moving average, would be
A) 116.7.
B) 168.3.
C) 135.0.
D) 127.7.
Answer: D
Diff: Easy
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
47) Which of the following methods produces a particularly stiff penalty in periods with large
forecast errors?
A) MAD
B) MSE
C) MAPE
D) decomposition
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
11
Copyright © 2018 Pearson Education, Inc.
48) The process of isolating linear trend and seasonal factors to develop more accurate forecasts
is called
A) regression.
B) decomposition.
C) smoothing.
D) monitoring.
Answer: B
Diff: Moderate
Topic: FORECASTING MODELS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Concept
49) Sales for boxes of Girl Scout cookies over a 4-month period were forecasted as follows: 100,
120, 115, and 123. The actual results over the 4-month period were as follows: 110, 114, 119,
115. What was the MAD of the 4-month forecast?
A) 0
B) 5
C) 7
D) 108
Answer: C
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
50) Sales for boxes of Girl Scout cookies over a 4-month period were forecasted as follows: 100,
120, 115, and 123. The actual results over the 4-month period were as follows: 110, 114, 119,
115. What was the MSE of the 4-month forecast?
A) 0
B) 5
C) 7
D) 54
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
12
Copyright © 2018 Pearson Education, Inc.
51) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18,
14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a threeday weighted moving average where the weights are 3, 1, and 1 (the highest weight is for the
most recent number).
A) 12.8
B) 13.0
C) 70.0
D) 14.0
Answer: D
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
52) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18,
14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a two-day
weighted moving average where the weights are 3 and 1.
A) 14.5
B) 13.5
C) 14
D) 12.25
Answer: A
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
53) Which of the following is not considered to be one of the components of a time series?
A) trend
B) seasonality
C) variance
D) cycles
Answer: C
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Concept
13
Copyright © 2018 Pearson Education, Inc.
54) Which of the following statements about the decomposition method is false?
A) The process of "deseasonalizing" involves multiplying by a seasonal index.
B) Dummy variables are used in a regression model as part of an additive approach to
seasonality.
C) Computing seasonal indices is the first step of the decomposition method.
D) Data is "deseasonalized" after the trend line is found.
Answer: D
Diff: Difficult
Topic: FORECASTING MODELS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Concept
55) Enrollment in a particular class for the last four semesters has been 120, 126, 110, and 130
(listed from oldest to most recent). Develop a forecast of enrollment next semester using
exponential smoothing with an alpha = 0.2. Assume that an initial forecast for the first semester
was 120 (so the forecast and the actual were the same).
A) 118.96
B) 121.17
C) 130
D) 120
Answer: B
Diff: Difficult
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
56) Demand for soccer balls at a new sporting goods store is forecasted using the following
regression equation:
Y = 98 + 2.2X where X is the number of months that the store has been in existence.
Let April be represented by X = 4. April is assumed to have a seasonality index of 1.15. What is
the forecast for soccer ball demand for the month of April (rounded to the nearest integer)?
A) 123
B) 107
C) 100
D) 115
Answer: B
Diff: Moderate
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
14
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57) A TIME SERIES forecasting model in which the forecast for the next period is the actual
value for the current period is the
A) Delphi model.
B) Holt's model.
C) naïve model.
D) exponential smoothing model.
Answer: C
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
58) In picking the smoothing constant for an exponential smoothing model, we should look for a
value that
A) produces a nice-looking curve.
B) equals the utility level that matches with our degree of risk aversion.
C) produces values which compare well with actual values based on a standard measure of error.
D) causes the least computational effort.
Answer: C
Diff: Easy
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
59) Which of the following is not considered one of the steps to developing the decomposition
method?
A) compute seasonal indices using CMAs
B) find the equation of the trend line using the deseasonalized data
C) forecast for future periods using the trend line
D) add the seasonal index to the trend forecast
Answer: D
Diff: Difficult
Topic: FORECASTING MODELS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Concept
15
Copyright © 2018 Pearson Education, Inc.
60) A method to measure how well predictions fit actual data is
A) decomposition
B) smoothing
C) tracking signal
D) regression
Answer: C
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
61) If the Q1 demand for a particular year is 200 and the seasonal index is 0.85, what is the
deseasonalized demand value for Q1?
A) 170
B) 185
C) 215
D) 235.29
Answer: D
Diff: Moderate
Topic: FORECASTING METHODS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Application
62) In the exponential smoothing with trend adjustment forecasting method, β is the
A) new forecast.
B) Y-axis intercept.
C) independent variable.
D) trend smoothing constant.
Answer: D
Diff: Moderate
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Concept
16
Copyright © 2018 Pearson Education, Inc.
63) Using the additive decomposition model, what would be the period 2, Q3 forecast using the
following equation: = 20 + 3.2X1 + 1.5X2 + 0.8X3 + 0.6X4?
A) 23.2
B) 25
C) 27
D) 27.2
Answer: D
Diff: Moderate
Topic: FORECASTING MODELS–TREND, SEASONAL, AND RANDOM VARIATIONS
LO: 5.7: Apply forecast models for trends, seasonal variations, and random variations.
AACSB: Analytical thinking
Classification: Application
64) The computer monitoring of tracking signals and self-adjustment is referred to as
A) exponential smoothing.
B) adaptive smoothing.
C) trend projections.
D) trend smoothing.
Answer: B
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
65) Which of the following is not a characteristic of trend projections?
A) The variable being predicted is the Y variable.
B) Time is the X variable.
C) It is useful for predicting the value of one variable based on time trend.
D) A negative intercept term always implies that the dependent variable is decreasing over time.
Answer: D
Diff: Moderate
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Concept
17
Copyright © 2018 Pearson Education, Inc.
66) A tracking signal was calculated for a particular set of demand forecasts. This tracking signal
was positive. This would indicate that
A) demand is greater than the forecast.
B) demand is less than the forecast.
C) demand is equal to the forecast.
D) the MAD is negative.
Answer: A
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
67) A seasonal index of ________ indicates that the season is average.
A) 10
B) 0.5
C) 0
D) 1
Answer: D
Diff: Moderate
Topic: ADJUSTING FOR SEASONAL VARIATIONS
LO: 5.6: Manipulate data to account for seasonal variations.
AACSB: Analytical thinking
Classification: Application
68) The errors in a particular forecast are as follows: 4, -3, 2, 5, -1. What is the tracking signal of
the forecast?
A) 0.4286
B) 2.3333
C) 5
D) 1.4
Answer: B
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
18
Copyright © 2018 Pearson Education, Inc.
69) Consider the actual and forecast values contained in the table.
Actual
10
13
17
19
22
Forecast
10.8
13.6
16.3
19
21.7
Actual
26
28
29
32
35
Forecast
24.5
27.2
29.9
32.6
35.3
What is the bias of the forecast?
A) 0.01
B) 0.06
C) 0.09
D) 0.13
Answer: A
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
70) Consider the actual and forecast values contained in the table.
Actual
10
13
17
19
22
Forecast
10.8
13.6
16.3
19
21.7
Actual
26
28
29
32
35
Forecast
24.5
27.2
29.9
32.6
35.3
What is the MAD of the forecast?
A) 0.60
B) 0.65
C) 0.70
D) 0.75
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
19
Copyright © 2018 Pearson Education, Inc.
71) Consider the actual and forecast values contained in the table.
Actual
10
13
17
19
22
Forecast
10.8
13.6
16.3
19
21.7
Actual
26
28
29
32
35
Forecast
24.5
27.2
29.9
32.6
35.3
What is the MSE of the forecast?
A) 0.369
B) 0.468
C) 0.573
D) 0.624
Answer: C
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
72) Consider the actual and forecast values contained in the table.
Actual
10
13
17
19
22
Forecast
10.8
13.6
16.3
19
21.7
Actual
26
28
29
32
35
Forecast
24.5
27.2
29.9
32.6
35.3
What is the MAPE of the forecast?
A) 2.92%
B) 3.08%
C) 3.17%
D) 3.26%
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
20
Copyright © 2018 Pearson Education, Inc.
73) Consider the actual and forecast values contained in the table.
Actual
10
13
17
19
22
Forecast
10.8
13.6
16.3
19
21.7
Actual
26
28
29
32
35
Forecast
24.5
27.2
29.9
32.6
35.3
What is the tracking signal for the 5th point in the series (actual = 22 & forecast = 21.7)?
A) -0.833
B) -1.333
C) 0.833
D) 1.333
Answer: A
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
74) Consider the actual and forecast values contained in the table.
#
1
2
3
4
5
Actual
10
13
17
19
22
Forecast
10.8
13.6
16.3
19
21.7
#
6
7
8
9
10
Actual
26
28
29
32
35
At which observation is the tracking signal at its maximum value?
A) Observation #5
B) Observation #7
C) Observation #4
D) Observation #6
Answer: B
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
21
Copyright © 2018 Pearson Education, Inc.
Forecast
24.5
27.2
29.9
32.6
35.3
75) Consider the actual and forecast values contained in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Forecast
25.667
40.047
54.429
68.809
83.190
97.571
111.952
126.333
At which observation is the tracking signal at its maximum value?
A) Observation #5
B) Observation #7
C) Observation #4
D) Observation #6
Answer: C
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
22
Copyright © 2018 Pearson Education, Inc.
76) Consider the actual and forecast values contained in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Forecast
25.667
40.047
54.429
68.809
83.190
97.571
111.952
126.333
What is the MAD?
A) 3.99
B) 3.93
C) 3.86
D) 3.79
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
23
Copyright © 2018 Pearson Education, Inc.
77) Consider the actual and forecast values contained in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Forecast
25.667
40.047
54.429
68.809
83.190
97.571
111.952
126.333
What is the MAPE?
A) 5.92%
B) 6.02%
C) 6.12%
D) 6.22%
Answer: A
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
24
Copyright © 2018 Pearson Education, Inc.
78) Consider the actual and forecast values contained in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Forecast
25.667
40.047
54.429
68.809
83.190
97.571
111.952
126.333
What is the bias?
A) -0.5
B) 0
C) 1.33
D) 1.75
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
25
Copyright © 2018 Pearson Education, Inc.
79) Demand for Y is shown in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
What is the intercept of the appropriate trend equation?
A) 14.38
B) 2.88
C) 11.28
D) 5.48
Answer: C
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
26
Copyright © 2018 Pearson Education, Inc.
80) Demand for Y is shown in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
What is the slope of the appropriate trend equation?
A) 14.38
B) 2.88
C) 11.28
D) 5.48
Answer: A
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
27
Copyright © 2018 Pearson Education, Inc.
81) Demand for Y is shown in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Develop a forecast using a trend line. What is the forecast for period 12?
A) 181.7
B) 183.9
C) 185.1
D) 187.3
Answer: B
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
28
Copyright © 2018 Pearson Education, Inc.
82) Demand for Y is shown in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Develop a forecast using a trend line. What is the forecast for period 10?
A) 151.7
B) 153.9
C) 155.1
D) 157.3
Answer: C
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
83) Daily humidity in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98
(yesterday). Forecast the humidity today using a three-day moving average.
A) 90
B) 88
C) 94
D) 92
Answer: D
Diff: Easy
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
29
Copyright © 2018 Pearson Education, Inc.
84) Daily humidity in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98
(yesterday). Forecast the humidity today using a two-day moving average
A) 92
B) 88
C) 94
D) 89
Answer: A
Diff: Easy
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
85) Daily humidity in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98
(yesterday). Calculate the MAD based on a two-day moving average, covering all days in which
you can have a forecast and an actual humidity level.
A) 3.9
B) 4.1
C) 4.3
D) 4.5
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
30
Copyright © 2018 Pearson Education, Inc.
86) Use simple exponential smoothing with α = 0.4 to forecast donut sales for March. Assume
that the forecast for January was for 28 donuts.
Month
January
February
March
April
Donut Sales
32
33
28
39
A) 30.18
B) 30.62
C) 30.96
D) 31.24
Answer: C
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
87) Use simple exponential smoothing with α = 0.9 to forecast donut sales for April. Assume that
the forecast for January was for 28 donuts.
Month
January
February
March
April
Donut Sales
32
33
28
39
A) 29.93
B) 29.17
C) 30.22
D) 28.49
Answer: D
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
31
Copyright © 2018 Pearson Education, Inc.
88) The following table represents the new members that have been acquired by a fitness center.
Month
Jan
Feb
March
April
New members
45
60
57
65
Assuming α = 0.3, β = 0.4, an initial forecast of 40 for January, and an initial trend adjustment of
0 for January, use exponential smoothing with trend adjustment to come up with a forecast for
May on new members.
A) 58.57
B) 63.23
C) 52.25
D) 55.81
Answer: A
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
89) The following table represents the new members that have been acquired by a fitness center.
Month
Jan
Feb
March
April
New members
45
60
57
65
Assuming α = 0.3, β = 0.4, an initial forecast of 40 for January, and an initial trend adjustment of
0 for January, use exponential smoothing with trend adjustment to come up with a forecast for
April on new members.
A) 58.57
B) 63.23
C) 52.25
D) 55.81
Answer: C
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
32
Copyright © 2018 Pearson Education, Inc.
90) The following table shows the number of pies consumed by the deans' suite during a monthly
pie-eating contest.
Month
January
February
March
April
May
June
July
Forecast # Pies
18
20
23
29
37
44
50
Actual # Pies
15
18
26
31
34
39
45
What is the forecast bias?
A) -1.86
B) -1.04
C) 1.04
D) 1.86
Answer: A
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
33
Copyright © 2018 Pearson Education, Inc.
91) The following table shows the number of pies consumed by the deans' suite during a monthly
pie-eating contest.
Month
January
February
March
April
May
June
July
Forecast # Pies
18
20
23
29
37
44
50
Actual # Pies
15
18
26
31
34
39
45
What is the forecast tracking signal for June?
A) -0.75
B) -2.67
C) 0.75
D) 2.67
Answer: B
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
92) The following table shows the number of pies consumed by the deans' suite during a monthly
pie-eating contest.
Month
January
February
March
April
May
June
July
Forecast # Pies
18
20
23
29
37
44
50
Actual # Pies
15
18
26
31
34
39
45
What is the forecast MAD?
A) 2.17
B) 2.66
C) 3.29
D) 3.75
Answer: C
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
34
Copyright © 2018 Pearson Education, Inc.
93) The following table shows the number of pies consumed by the deans' suite during a monthly
pie-eating contest.
Month
January
February
March
April
May
June
July
Forecast # Pies
18
20
23
29
37
44
50
Actual # Pies
15
18
26
31
34
39
45
What is the forecast tracking signal for March?
A) -0.17
B) -0.33
C) -0.55
D) -0.75
Answer: D
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
94) Tim gave his new intern the task of computing the tracking signal for a brand-new
forecasting technique. The first month's data was available so the intern pounded away at his
computer keyboard for the better part of an hour before finally running up to Tim brandishing a
piece of paper with the answer. Which of these numbers should Tim not see for the first month's
tracking signal result?
A) 0
B) 1
C) -1
D) 2
Answer: D
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
35
Copyright © 2018 Pearson Education, Inc.
95) The tracking signal is the running sum of the forecast errors divided by the
A) MAD.
B) MSE.
C) RSFE.
D) bias.
Answer: A
Diff: Easy
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
96) Plossl and Wight suggest a reasonable limit for the tracking signal for high-volume stock
items is considered to be
A) ±3.
B) ±4.
C) ±8.
D) ±9.
Answer: B
Diff: Easy
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
97) Plossl and Wight suggest a reasonable limit for the tracking signal for low-volume stock
items is considered to be
A) ±3.
B) ±4.
C) ±8.
D) ±9.
Answer: C
Diff: Easy
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
36
Copyright © 2018 Pearson Education, Inc.
98) One MAD is equivalent to approximately
A) 0.8.
B) 1.2.
C) 1.6.
D) 2.0.
Answer: A
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
99) Demand for a particular type of battery fluctuates from one week to the next. A study of the
last six weeks provides the following demands (in dozens): 4, 5, 3, 2, 8, 10 (last week).
(a) Forecast demand for the next week using a two-week moving average.
(b) Forecast demand for the next week using a three-week moving average.
Answer:
(a) (8 + 10)/2 = 9
(b) (2 + 8 + 10)/3 = 6.67
Diff: Easy
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
100) Daily high temperatures in the city of Houston for the last week have been: 93, 94, 93, 95,
92, 86, 98 (yesterday).
(a) Forecast the high temperature today using a three-day moving average.
(b) Forecast the high temperature today using a two-day moving average.
(c) Calculate the mean absolute deviation based on a two-day moving average, covering all days
in which you can have a forecast and an actual temperature.
Answer:
(a) (92 + 86 + 98)/3 = 92
(b) (86 + 98)/2 = 92
(c) MAD = (|93 - 9.35| + |95 - 93.5| + |92 - 94| + |86 - 93.5| + |98 - 89|) / 5 = 20.5 / 5 = 4.1
Diff: Moderate
Topic: VARIOUS
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models. 5.3:
Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
37
Copyright © 2018 Pearson Education, Inc.
101) For the data below:
Month
January
February
March
April
May
June
Wiper Blade Sales
39
36
16
26
10
12
Month
July
August
September
October
November
December
Wiper Blade Sales
1
15
5
24
13
31
(a) Develop a scatter diagram.
(b) Develop a three-month moving average.
(c) Compute MAD.
Answer:
(a) scatter diagram
38
Copyright © 2018 Pearson Education, Inc.
(b)
Month
January
February
March
April
May
June
July
August
September
October
November
December
January
Automobile
Battery Sales
39
36
16
26
10
12
1
15
5
24
13
31
-
3-Month
Moving Avg.
30.333
26
17.333
16
7.667
9.333
7
14.667
14
22.667
Absolute Deviation
4.333
16
5.333
15
7.333
4.333
17
1.667
17
-
(c) MAD = 9.778
Diff: Difficult
Topic: VARIOUS
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models. 5.3:
Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
39
Copyright © 2018 Pearson Education, Inc.
102) For the data below:
Month
January
February
March
April
May
June
Diaper Sales
39
36
16
26
10
12
Month
July
August
September
October
November
December
Diaper Sales
1
15
5
24
13
31
(a) Develop a scatter diagram.
(b) Develop an exponential smoothing forecast using an alpha of 0.2 and a separate exponential
smoothing forecast using an alpha of 0.9.
(c) Compute the MSE for both forecasts from part b. Which is more accurate?
Answer:
(a) scatter diagram
40
Copyright © 2018 Pearson Education, Inc.
24
Copyright © 2018 Pearson Education, Inc.
78) Consider the actual and forecast values contained in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Forecast
25.667
40.047
54.429
68.809
83.190
97.571
111.952
126.333
What is the bias?
A) -0.5
B) 0
C) 1.33
D) 1.75
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
25
Copyright © 2018 Pearson Education, Inc.
79) Demand for Y is shown in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
What is the intercept of the appropriate trend equation?
A) 14.38
A) 14.38
B) 2.88
C) 11.28
D) 5.48
Answer: C
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
26
Copyright © 2018 Pearson Education, Inc.
80) Demand for Y is shown in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
What is the slope of the appropriate trend equation?
A) 14.38
B) 2.88
C) 11.28
D) 5.48
Answer: A
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
27
Copyright © 2018 Pearson Education, Inc.
81) Demand for Y is shown in the table.
#
1
2
3
4
5
6
7
8
Y
28
42
49
74
78
93
115
129
Develop a forecast using a trend line. What is the forecast for period 12?
A) 181.7
B) 183.9
C) 185.1
D) 187.3
Answer: B
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
28
Copyright © 2018 Pearson Education, Inc.
82) Demand for Y is shown in the table.
#
1
2
3
4
Y
28
42
49
74
5
6
7
8
78
93
115
129
Develop a forecast using a trend line. What is the forecast for period 10?
A) 151.7
B) 153.9
C) 155.1
D) 157.3
Answer: C
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
83) Daily humidity in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98
(yesterday). Forecast the humidity today using a three-day moving average.
A) 90
B) 88
C) 94
D) 92
Answer: D
Diff: Easy
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
29
Copyright © 2018 Pearson Education, Inc.
84) Daily humidity in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98
(yesterday). Forecast the humidity today using a two-day moving average
A) 92
B) 88
C) 94
D) 89
Answer: A
Diff: Easy
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
85) Daily humidity in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98
(yesterday). Calculate the MAD based on a two-day moving average, covering all days in which
you can have a forecast and an actual humidity level.
A) 3.9
B) 4.1
C) 4.3
D) 4.5
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
30
Copyright © 2018 Pearson Education, Inc.
86) Use simple exponential smoothing with α = 0.4 to forecast donut sales for March. Assume
that the forecast for January was for 28 donuts.
Month
January
February
March
April
Donut Sales
32
33
28
39
A) 30.18
B) 30.62
C) 30.96
D) 31.24
Answer: C
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
87) Use simple exponential smoothing with α = 0.9 to forecast donut sales for April. Assume that
the forecast for January was for 28 donuts.
Month
January
February
March
April
Donut Sales
32
33
28
39
A) 29.93
B) 29.17
C) 30.22
D) 28.49
Answer: D
Diff: Moderate
Topic: COMPONENTS OF A TIME SERIES
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
31
Copyright © 2018 Pearson Education, Inc.
88) The following table represents the new members that have been acquired by a fitness center.
Month
Jan
Feb
March
April
New members
45
60
57
65
Assuming α = 0.3, β = 0.4, an initial forecast of 40 for January, and an initial trend adjustment of
0 for January, use exponential smoothing with trend adjustment to come up with a forecast for
May on new members.
A) 58.57
B) 63.23
C) 52.25
D) 55.81
Answer: A
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
89) The following table represents the new members that have been acquired by a fitness center.
Month
Jan
Feb
March
April
New members
45
60
57
65
Assuming α = 0.3, β = 0.4, an initial forecast of 40 for January, and an initial trend adjustment of
0 for January, use exponential smoothing with trend adjustment to come up with a forecast for
April on new members.
A) 58.57
B) 63.23
C) 52.25
D) 55.81
Answer: C
Diff: Difficult
Topic: FORECASTING MODELS–TREND AND RANDOM VARIATIONS
LO: 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
32
Copyright © 2018 Pearson Education, Inc.
90) The following table shows the number of pies consumed by the deans' suite during a monthly
pie-eating contest.
Month
January
February
March
April
May
June
July
Forecast # Pies
18
20
23
29
37
44
50
What is the forecast bias?
A) -1.86
B) -1.04
C) 1.04
D) 1.86
Actual # Pies
15
18
26
31
34
39
45
Answer: A
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
33
Copyright © 2018 Pearson Education, Inc.
91) The following table shows the number of pies consumed by the deans' suite during a monthly
pie-eating contest.
Month
January
February
March
April
May
June
July
Forecast # Pies
18
20
23
29
37
44
50
Actual # Pies
15
18
26
31
34
39
45
What is the forecast tracking signal for June?
A) -0.75
B) -2.67
C) 0.75
D) 2.67
Answer: B
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
92) The following table shows the number of pies consumed by the deans' suite during a monthly
pie-eating contest.
Month
January
February
March
April
May
Forecast # Pies
18
20
23
29
37
Actual # Pies
15
18
26
31
34