Quantitative Analysis For Management 13th by Barry Render -Test Bank Instant Download - Complete Test Bank With Answers Sample Questions Are Posted Below 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: …
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Quantitative Analysis For Management 13th by Barry Render -Test Bank
Instant Download – Complete Test Bank With Answers
Sample Questions Are Posted Below
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
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
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
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
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
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
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?
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
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
35) Which of the following is considered a causal method of forecasting?
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
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?
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?
Answer: D
Diff: Easy
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
39) When is the exponential smoothing model equivalent to the naïve forecasting model?
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?
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?
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
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
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?
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.
Answer: A
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
45) As one increases the number of periods used in the calculation of a moving average
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
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?
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
48) The process of isolating linear trend and seasonal factors to develop more accurate forecasts is called
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?
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?
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
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 three-day weighted moving average where the weights are 3, 1, and 1 (the highest weight is for the most recent number).
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.
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?
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
54) Which of the following statements about the decomposition method is false?
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).
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)?
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
57) A TIME SERIES forecasting model in which the forecast for the next period is the actual value for the current period is the
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
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?
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
60) A method to measure how well predictions fit actual data is
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?
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
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
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?
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
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?
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
66) A tracking signal was calculated for a particular set of demand forecasts. This tracking signal was positive. This would indicate that
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.
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?
Answer: B
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
69) Consider the actual and forecast values contained in the table.
| Actual | Forecast | Actual | Forecast |
| 10 | 10.8 | 26 | 24.5 |
| 13 | 13.6 | 28 | 27.2 |
| 17 | 16.3 | 29 | 29.9 |
| 19 | 19 | 32 | 32.6 |
| 22 | 21.7 | 35 | 35.3 |
What is the bias of the forecast?
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 | Forecast | Actual | Forecast |
| 10 | 10.8 | 26 | 24.5 |
| 13 | 13.6 | 28 | 27.2 |
| 17 | 16.3 | 29 | 29.9 |
| 19 | 19 | 32 | 32.6 |
| 22 | 21.7 | 35 | 35.3 |
What is the MAD of the forecast?
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
71) Consider the actual and forecast values contained in the table.
| Actual | Forecast | Actual | Forecast |
| 10 | 10.8 | 26 | 24.5 |
| 13 | 13.6 | 28 | 27.2 |
| 17 | 16.3 | 29 | 29.9 |
| 19 | 19 | 32 | 32.6 |
| 22 | 21.7 | 35 | 35.3 |
What is the MSE of the forecast?
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 | Forecast | Actual | Forecast |
| 10 | 10.8 | 26 | 24.5 |
| 13 | 13.6 | 28 | 27.2 |
| 17 | 16.3 | 29 | 29.9 |
| 19 | 19 | 32 | 32.6 |
| 22 | 21.7 | 35 | 35.3 |
What is the MAPE of the forecast?
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
73) Consider the actual and forecast values contained in the table.
| Actual | Forecast | Actual | Forecast |
| 10 | 10.8 | 26 | 24.5 |
| 13 | 13.6 | 28 | 27.2 |
| 17 | 16.3 | 29 | 29.9 |
| 19 | 19 | 32 | 32.6 |
| 22 | 21.7 | 35 | 35.3 |
What is the tracking signal for the 5th point in the series (actual = 22 & forecast = 21.7)?
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.
| # | Actual | Forecast | # | Actual | Forecast |
| 1 | 10 | 10.8 | 6 | 26 | 24.5 |
| 2 | 13 | 13.6 | 7 | 28 | 27.2 |
| 3 | 17 | 16.3 | 8 | 29 | 29.9 |
| 4 | 19 | 19 | 9 | 32 | 32.6 |
| 5 | 22 | 21.7 | 10 | 35 | 35.3 |
At which observation is the tracking signal at its maximum value?
Answer: B
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
75) Consider the actual and forecast values contained in the table.
| # | Y | Forecast |
| 1 | 28 | 25.667 |
| 2 | 42 | 40.047 |
| 3 | 49 | 54.429 |
| 4 | 74 | 68.809 |
| 5 | 78 | 83.190 |
| 6 | 93 | 97.571 |
| 7 | 115 | 111.952 |
| 8 | 129 | 126.333 |
At which observation is the tracking signal at its maximum value?
Answer: C
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
76) Consider the actual and forecast values contained in the table.
| # | Y | Forecast |
| 1 | 28 | 25.667 |
| 2 | 42 | 40.047 |
| 3 | 49 | 54.429 |
| 4 | 74 | 68.809 |
| 5 | 78 | 83.190 |
| 6 | 93 | 97.571 |
| 7 | 115 | 111.952 |
| 8 | 129 | 126.333 |
What is the MAD?
Answer: D
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
77) Consider the actual and forecast values contained in the table.
| # | Y | Forecast |
| 1 | 28 | 25.667 |
| 2 | 42 | 40.047 |
| 3 | 49 | 54.429 |
| 4 | 74 | 68.809 |
| 5 | 78 | 83.190 |
| 6 | 93 | 97.571 |
| 7 | 115 | 111.952 |
| 8 | 129 | 126.333 |
What is the MAPE?
Answer: A
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
78) Consider the actual and forecast values contained in the table.
| # | Y | Forecast |
| 1 | 28 | 25.667 |
| 2 | 42 | 40.047 |
| 3 | 49 | 54.429 |
| 4 | 74 | 68.809 |
| 5 | 78 | 83.190 |
| 6 | 93 | 97.571 |
| 7 | 115 | 111.952 |
| 8 | 129 | 126.333 |
What is the bias?
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
79) Demand for Y is shown in the table.
| # | Y |
| 1 | 28 |
| 2 | 42 |
| 3 | 49 |
| 4 | 74 |
| 5 | 78 |
| 6 | 93 |
| 7 | 115 |
| 8 | 129 |
What is the intercept of the appropriate trend equation?
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
80) Demand for Y is shown in the table.
| # | Y |
| 1 | 28 |
| 2 | 42 |
| 3 | 49 |
| 4 | 74 |
| 5 | 78 |
| 6 | 93 |
| 7 | 115 |
| 8 | 129 |
What is the slope of the appropriate trend equation?
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
81) Demand for Y is shown in the table.
| # | Y |
| 1 | 28 |
| 2 | 42 |
| 3 | 49 |
| 4 | 74 |
| 5 | 78 |
| 6 | 93 |
| 7 | 115 |
| 8 | 129 |
Develop a forecast using a trend line. What is the forecast for period 12?
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
82) Demand for Y is shown in the table.
| # | Y |
| 1 | 28 |
| 2 | 42 |
| 3 | 49 |
| 4 | 74 |
| 5 | 78 |
| 6 | 93 |
| 7 | 115 |
| 8 | 129 |
Develop a forecast using a trend line. What is the forecast for period 10?
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.
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
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
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.
Answer: B
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
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 | Donut Sales |
| January | 32 |
| February | 33 |
| March | 28 |
| April | 39 |
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 | Donut Sales |
| January | 32 |
| February | 33 |
| March | 28 |
| April | 39 |
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
88) The following table represents the new members that have been acquired by a fitness center.
| Month | New members |
| Jan | 45 |
| Feb | 60 |
| March | 57 |
| April | 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.
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 | New members |
| Jan | 45 |
| Feb | 60 |
| March | 57 |
| April | 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.
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
90) The following table shows the number of pies consumed by the deans’ suite during a monthly pie-eating contest.
| Month | Forecast # Pies | Actual # Pies |
| January | 18 | 15 |
| February | 20 | 18 |
| March | 23 | 26 |
| April | 29 | 31 |
| May | 37 | 34 |
| June | 44 | 39 |
| July | 50 | 45 |
What is the forecast bias?
Answer: A
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
91) The following table shows the number of pies consumed by the deans’ suite during a monthly pie-eating contest.
| Month | Forecast # Pies | Actual # Pies |
| January | 18 | 15 |
| February | 20 | 18 |
| March | 23 | 26 |
| April | 29 | 31 |
| May | 37 | 34 |
| June | 44 | 39 |
| July | 50 | 45 |
What is the forecast tracking signal for June?
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 | Forecast # Pies | Actual # Pies |
| January | 18 | 15 |
| February | 20 | 18 |
| March | 23 | 26 |
| April | 29 | 31 |
| May | 37 | 34 |
| June | 44 | 39 |
| July | 50 | 45 |
What is the forecast MAD?
Answer: C
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
93) The following table shows the number of pies consumed by the deans’ suite during a monthly pie-eating contest.
| Month | Forecast # Pies | Actual # Pies |
| January | 18 | 15 |
| February | 20 | 18 |
| March | 23 | 26 |
| April | 29 | 31 |
| May | 37 | 34 |
| June | 44 | 39 |
| July | 50 | 45 |
What is the forecast tracking signal for March?
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?
Answer: D
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
95) The tracking signal is the running sum of the forecast errors divided by the
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
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
Answer: C
Diff: Easy
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
98) One MAD is equivalent to approximately
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
101) For the data below:
| Month | Wiper Blade Sales | Month | Wiper Blade Sales |
| January | 39 | July | 1 |
| February | 36 | August | 15 |
| March | 16 | September | 5 |
| April | 26 | October | 24 |
| May | 10 | November | 13 |
| June | 12 | December | 31 |
(a) Develop a scatter diagram.
(b) Develop a three-month moving average.
(c) Compute MAD.
Answer:
(a) scatter diagram
(b)
| Month | Automobile
Battery Sales |
3-Month
Moving Avg. |
Absolute Deviation |
| January | 39 | – | – |
| February | 36 | – | – |
| March | 16 | – | – |
| April | 26 | 30.333 | 4.333 |
| May | 10 | 26 | 16 |
| June | 12 | 17.333 | 5.333 |
| July | 1 | 16 | 15 |
| August | 15 | 7.667 | 7.333 |
| September | 5 | 9.333 | 4.333 |
| October | 24 | 7 | 17 |
| November | 13 | 14.667 | 1.667 |
| December | 31 | 14 | 17 |
| January | – | 22.667 | – |
(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
102) For the data below:
| Month | Diaper Sales | Month | Diaper Sales |
| January | 39 | July | 1 |
| February | 36 | August | 15 |
| March | 16 | September | 5 |
| April | 26 | October | 24 |
| May | 10 | November | 13 |
| June | 12 | December | 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
(b)
| Month |
Diaper Sales |
α = 0.2 |
Squared
Error α = 0.2 |
α = 0.9 |
Squared
Error α = 0.9 |
| January | 39 | ||||
| February | 36 | 39 | 9 | 39 | 9 |
| March | 16 | 38.4 | 501.76 | 36.3 | 412.1 |
| April | 26 | 33.92 | 62.73 | 18.03 | 63.52 |
| May | 10 | 32.34 | 498.89 | 25.20 | 231.13 |
| June | 12 | 27.87 | 251.82 | 11.52 | .23 |
| July | 1 | 24.69 | 561.46 | 11.95 | 119.95 |
| August | 15 | 19.96 | 24.56 | 2.09 | 166.53 |
| September | 5 | 18.97 | 195.02 | 13.71 | 75.86 |
| October | 24 | 16.17 | 61.28 | 5.87 | 328.66 |
| November | 13 | 17.74 | 22.44 | 22.19 | 84.4 |
| December | 31 | 16.79 | 201.92 | 13.92 | 291.77 |
(c) MSE(α = .2) = 217.35; MSE(α = .9) = 162.10
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
103) For the data below:
| Year | Cat Sales | Year | Cat Sales |
| 1990 | 116 | 1997 | 109 |
| 1991 | 95 | 1998 | 44 |
| 1992 | 39 | 1999 | 54 |
| 1993 | 69 | 2000 | 61 |
| 1994 | 98 | 2001 | 73 |
| 1995 | 84 | 2002 | 82 |
| 1996 | 37 | 2003 | 110 |
(a) Develop a scatter diagram.
(b) Develop a five-year moving average forecast.
(c) Find MAPE.
Answer:
(a) scatter diagram
(b)
| Year | Cat Sales | Forecast | |Pct. Error| |
| 1990 | 116 | ||
| 1991 | 95 | ||
| 1992 | 39 | ||
| 1993 | 69 | ||
| 1994 | 98 | ||
| 1995 | 84 | 83.4 | .714% |
| 1996 | 37 | 77 | 108.108% |
| 1997 | 109 | 65.4 | 40% |
| 1998 | 44 | 79.4 | 80.455% |
| 1999 | 54 | 74.4 | 37.778% |
| 2000 | 61 | 65.6 | 7.541% |
| 2001 | 73 | 61 | 16.438% |
| 2002 | 82 | 68.2 | 16.829% |
| 2003 | 110 | 62.8 | 42.909% |
(c) MAPE = 38.975%
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
104) Use simple exponential smoothing with α = 0.7 to forecast llama sales for February through May. Assume that the forecast for January was for 22 llamas.
| Month | Llama Sales |
| January | 42 |
| February | 33 |
| March | 28 |
| April | 59 |
Answer: Forecasts for February through May are: 43, 36, 26.2, and 39.36.
Diff: Moderate
Topic: VARIOUS
LO: 5.2: Compare moving averages, exponential smoothing, and other time-series models.
AACSB: Analytical thinking
Classification: Application
105) Average starting salaries for students using a placement service at a university have been steadily increasing. A study of the last four graduating classes indicates the following average salaries: $60,000, $72,000, $84,500, and $96,000 (last graduating class). Predict the starting salary for the next graduating class using a simple exponential smoothing model with α = 0.25. Assume that the initial forecast was $55,000.
Answer: Forecast for next period = $73,699. Not too shabby!
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
106) Use simple exponential smoothing with α = 0.75 to forecast the yurt sales for February through May. Assume that the forecast for January was for 25 yurts.
| Month | Yurt Sales |
| January | 28 |
| February | 72 |
| March | 98 |
| April | 126 |
Answer: Forecast for Feb. through May = 27.25, 60.813, 88.703, and 116.68
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Application
107) The following table represents the new members that have been acquired by a fitness center.
| Month | New members |
| Jan | 45 |
| Feb | 60 |
| March | 57 |
| April | 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.
Answer:
| Month | New members | Ft | Tt | FITt |
| Jan | 45 | 40 | 0 | 40 |
| Feb | 60 | 41.5 | 0.6 | 42.1 |
| March | 57 | 47.47 | 2.748 | 50.218 |
| April | 65 | 52.2526 | 3.56184 | 55.81444 |
| May | 58.57011 | 4.664107 | 63.23422 |
May forecast = 58.57
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
108) The following table represents the number of applicants at a popular private college in the last four years.
| Month | New members |
| 2007 | 10,067 |
| 2008 | 10,940 |
| 2009 | 11,116 |
| 2010 | 10,999 |
Assuming α = 0.2, β = 0.3, an initial forecast of 10,000 for 2007, and an initial trend adjustment of 0 for 2007, use exponential smoothing with trend adjustment to come up with a forecast for 2011 on the number of applicants.
Answer:
| Month | # of applicants | Ft | Tt | FITt |
| 2007 | 10,067 | 10,000 | 0 | 10000 |
| 2008 | 10,940 | 10013.4 | 4.02 | 10017.42 |
| 2009 | 11,116 | 10201.94 | 59.3748 | 10261.31 |
| 2010 | 10,999 | 10432.25 | 110.6562 | 10542.9 |
| 2011 | 10634.12 | 138.0219 | 10772.15 |
2011 Forecast = 10,634
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
109) Given the following data, if MAD = 1.25, determine what the actual demand must have been in period 2 (A2).
| Time Period | Actual (A) | Forecast (F) | |A – F| |
| 1 | 2 | 3 | 1 |
| 2 | A2 = ? | 4 | – |
| 3 | 6 | 5 | 1 |
| 4 | 4 | 6 | 2 |
Answer: A2 = 3 or A2 = 5
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
110) Calculate (a) MAD, (b) MSE, and (c) MAPE for the following forecast versus actual sales figures. (Please round to four decimal places for MAPE.)
| Forecast | Actual |
| 100 | 95 |
| 110 | 108 |
| 120 | 123 |
| 130 | 130 |
Answer:
(a) MAD = 10/4 = 2.5
(b) MSE = 38/4 = 9.5
(c) MAPE = (0.0956/4)100 = 2.39%
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Application
111) Use the sales data given below to determine:
| Year | Sales (units) | Year | Sales (units) |
| 1995 | 130 | 1999 | 169 |
| 1996 | 140 | 2000 | 182 |
| 1997 | 152 | 2001 | 194 |
| 1998 | 160 | 2002 | ? |
(a) The least squares trend line.
(b) The predicted value for 2002 sales.
(c) The MAD.
(d) The unadjusted forecasting MSE.
Answer:
(a) = 119.14 + 10.46X
(b) 119.14 + 10.46(8) = 202.82
(c) MAD = 1.01
(d) MSE = 1.71
Diff: Difficult
Topic: VARIOUS
LO: 5.4: Apply forecast models for random variations. 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
112) For the data below:
| Year | Automobile Sales | Year | Automobile Sales |
| 1990 | 116 | 1977 | 119 |
| 1991 | 105 | 1998 | 34 |
| 1992 | 29 | 1999 | 34 |
| 1993 | 59 | 2000 | 48 |
| 1994 | 108 | 2001 | 53 |
| 1995 | 94 | 2002 | 65 |
| 1996 | 27 | 2003 | 111 |
(a) Determine the least squares regression line.
(b) Determine the predicted value for 2004.
(c) Determine the MAD.
(d) Determine the unadjusted forecasting MSE.
Answer:
(a) = 85.15 – 1.8X
(b) 85.15 – 1.8 (15) = 58.15
(c) MAD = 30.09
(d) MSE = 1,121.66
Diff: Difficult
Topic: VARIOUS
LO: 5.3: Calculate measures of forecast accuracy. 5.5: Apply forecast models for trends and random variations.
AACSB: Analytical thinking
Classification: Application
113) Given the following gasoline data:
| Quarter | Year 1 | Year 2 |
| 1 | 150 | 156 |
| 2 | 140 | 148 |
| 3 | 185 | 201 |
| 4 | 160 | 174 |
(a) Compute the seasonal index for each quarter.
(b) Suppose we expect year 3 to have annual demand of 800. What is the forecast value for each quarter in year 3?
Answer:
(a)
| Quarter | Year 1 | Year 2 | Average two-
year demand |
Quarterly demand | Average seasonal index |
| 1 | 150 | 156 | 153 | 164.25 | .932 |
| 2 | 140 | 148 | 144 | 164.25 | .877 |
| 3 | 185 | 201 | 193 | 164.25 | 1.175 |
| 4 | 160 | 174 | 167 | 164.25 | 1.017 |
(b)
| Quarter | Forecast |
| 1 | 200 * .932 = 186.00 |
| 2 | 200 * .877 = 175.34 |
| 3 | 200 * 1.175 = 235.01 |
| 4 | 200 * 1.017 = 203.35 |
Diff: Difficult
Topic: ADJUSTING FOR SEASONAL VARIATIONS
LO: 5.6: Manipulate data to account for seasonal variations.
AACSB: Analytical thinking
Classification: Application
114) Given the following data and seasonal index:
| Month | Sales | |
| Year 1 | Year 2 | |
| Jan | 8 | 8 |
| Feb | 7 | 9 |
| Mar | 5 | 6 |
| Apr | 10 | 11 |
| May | 9 | 12 |
| June | 12 | 16 |
| July | 15 | 20 |
| Aug | 20 | 25 |
| Sept | 4 | 4 |
| Oct | 3 | 2 |
| Nov | 8 | 7 |
| Dec | 9 | 9 |
(a) Compute the seasonal index using only year 1 data.
(b) Determine the deseasonalized demand values using year 2 data and year 1’s seasonal indices.
(c) Determine the trend line on year 2’s deseasonalized data.
(d) Forecast the sales for the first 3 months of year 3, adjusting for seasonality.
Answer:
(a) and (b)
| Month | Year 1 | Year 2 | ||
| Sales | Seasonal index | Sales | Deseasonalized data | |
| Jan | 8 | .87 | 8 | 6.98 |
| Feb | 7 | .76 | 9 | 6.87 |
| Mar | 5 | .55 | 6 | 3.27 |
| Apr | 10 | 1.09 | 11 | 12.00 |
| May | 9 | .98 | 12 | 11.78 |
| June | 12 | 1.31 | 16 | 20.94 |
| July | 15 | 1.64 | 20 | 32.73 |
| Aug | 20 | 2.18 | 25 | 54.54 |
| Sept | 4 | .44 | 4 | 1.75 |
| Oct | 3 | .33 | 2 | 0.65 |
| Nov | 8 | .87 | 7 | 6.11 |
| Dec | 9 | .98 | 9 | 8.84 |
(c) y = 11.96 + .29X
(d) Jan = [11.96 + .29 (13)] * .87 = 13.69
Feb = [11.96 + .29 (14)] * .67 = 12.18
Mar = [11.96 + .29 (15)] * .55 = 8.97
Diff: Difficult
Topic: ADJUSTING FOR SEASONAL VARIATIONS
LO: 5.6: Manipulate data to account for seasonal variations.
AACSB: Analytical thinking
Classification: Application
115) Wick’s Ski Shop is looking to forecast ski sales on a quarterly basis based on the historical data listed in the table below:
| Year | ||||
| 1 | 2 | 3 | 4 | |
| Q1 | 500 | 520 | 550 | 595 |
| Q2 | 160 | 140 | 170 | 180 |
| Q3 | 80 | 85 | 90 | 95 |
| Q4 | 220 | 250 | 260 | 290 |
Use the steps to develop a forecast using the decomposition method to answer the following questions:
(a) Using the CMAs, calculate the seasonal indices for Q1, Q2, Q3, and Q4.
(b) Find the equation for the trend line using deseasonalized data.
(c) Find the year 5 quarterly forecasts.
Answer:
(a) Q1 – 2.1174, Q2 – 0.6129, Q3 – 0.3320, Q4 – 0.9324
(b) y = 227.73 + 4.32X
(c) Q1 forecast – 637.66, Q2 forecast – 187.22, Q3 forecast – 102.85, Q4 forecast – 292.88
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: Application
116) The following table represents the actual vs. forecasted number of new customers acquired by a major credit card company:
| Month | Actual | Forecast |
| Jan | 1024 | 1010 |
| Feb | 1057 | 1025 |
| March | 1049 | 1141 |
| April | 1069 | 1053 |
| May | 1065 | 1059 |
(a) What is the tracking signal?
(b) Based on the answer in part (a), comment on the accuracy of this forecast.
Answer:
| Month | Actual | Forecast | Error | RSFE | |E| |
| Jan | 1024 | 1010 | 14 | 14 | 14 |
| Feb | 1057 | 1025 | 32 | 46 | 32 |
| March | 1049 | 1141 | -92 | -46 | 92 |
| April | 1069 | 1053 | 16 | -30 | 16 |
| May | 1065 | 1059 | 6 | -24 | 6 |
(a) RSFE/MAD = -24/32 = -0.75 MAD
(b) The answer in part (a) indicates an accurate forecast, one where overall, the actual number of new customers was slightly less than the forecast.
Diff: Difficult
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Application
117) What is the basic additive decomposition model (in regression terms)?
Answer: = a + b1X1 + b2X2 + b3X3 + b4X4
Where X1 = time period; X2 = 1 if quarter 2, 0 otherwise; X3 = 1 if quarter 3, 0 otherwise; X4 = 1 if quarter 4, 0 otherwise.
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: Application
118) In general terms, describe what causal forecasting models are.
Answer: Causal forecasting models incorporate variables or factors that might influence the quantity being forecasted.
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
119) In general terms, describe what qualitative forecasting models are.
Answer: Qualitative forecasting models attempt to incorporate judgmental or subjective factors into the model.
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
120) Briefly describe the structure of a scatter diagram for a time series.
Answer: A scatter diagram for a time series may be plotted on a two-dimensional graph with the horizontal axis representing the time period, while the variable to be forecast (such as sales) is placed on the vertical axis.
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
121) Briefly describe the jury of executive opinion forecasting method.
Answer: The jury of executive opinion forecasting model uses the opinions of a small group of high-level managers, often in combination with statistical models, and results in a group estimate of demand.
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
122) Briefly describe the consumer market survey forecasting method.
Answer: It is a forecasting method that solicits input from customers or potential customers regarding their future purchasing plans.
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
123) Describe the naïve forecasting method.
Answer: The forecast for the next period is the actual value observed in the current period.
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
124) Briefly describe why the scatter diagram is helpful.
Answer: Scatter diagrams show the relationships between model variables.
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
125) Explain, briefly, why most forecasting error measures use either the absolute or the square of the error.
Answer: A deviation is equally important whether it is above or below the actual. This also prevents negative errors from canceling positive errors that would understate the true size of the errors.
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
126) List four measures of historical forecasting errors.
Answer: MAD, MSE, MAPE, and Bias
Diff: Moderate
Topic: MEASURES OF FORECAST ACCURACY
LO: 5.3: Calculate measures of forecast accuracy.
AACSB: Analytical thinking
Classification: Concept
127) In general terms, describe what TIME SERIES forecasting models are.
Answer: forecasting models that make use of historical data
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
128) List four components of TIME SERIES data.
Answer: trend, seasonality, cycles, and random variations
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
129) Explain, briefly, why the larger the number of periods included in a moving average forecast, the less well the forecast identifies rapid changes in the variable of interest.
Answer: The larger the number of periods included in the moving average forecast, the less the average is changed by the addition or deletion of a single number.
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
130) State the mathematical expression for exponential smoothing.
Answer: Ft + 1 = Ft + α(Yt – Ft)
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
131) Explain, briefly, why, in the exponential smoothing forecasting method, the larger the value of the smoothing constant, α, the better the forecast will be in allowing the user to see rapid changes in the variable of interest.
Answer: The larger the value of α, the greater is the weight placed on the most recent values.
Diff: Moderate
Topic: FORECASTING MODELS–RANDOM VARIATIONS ONLY
LO: 5.4: Apply forecast models for random variations.
AACSB: Analytical thinking
Classification: Concept
132) In exponential smoothing, discuss the difference between α and β.
Answer: α is a weight applied to adjust for the difference between last period actual and forecasted value. β is a trend smoothing constant.
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
133) In general terms, describe the difference between a general linear regression model and a trend projection.
Answer: A trend projection is a regression model where the independent variable is always time.
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
134) In general terms, describe a centered moving average.
Answer: An average of the values centered at a particular point in time. This is used to compute seasonal indices when trend is present.
Diff: Moderate
Topic: ADJUSTING FOR SEASONAL VARIATIONS
LO: 5.6: Manipulate data to account for seasonal variations.
AACSB: Analytical thinking
Classification: Concept
135) The decomposition approach to forecasting (using trend and seasonal components) may be helpful when attempting to forecast a TIME SERIES. Could an analogous approach be used in multiple regression analysis? Explain briefly.
Answer: An analogous approach would be possible using time as one independent variable and using a set of dummy variables to represent the seasons.
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
136) List the steps to develop a forecast using the decomposition method.
Answer: 1. Compute seasonal indices using CMAs.
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
137) What is one advantage of using causal models over time series or qualitative models?
Answer: Use of the causal model requires that the forecaster gain an understanding of the relationships, not merely the frequency of variation; i.e., the forecaster gains a greater understanding of the problem than the other methods.
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
138) Discuss the use of a tracking signal.
Answer: A tracking signal measures how well predictions fit actual data. By setting tracking limits, a manager is signaled to reevaluate the forecasting method.
Diff: Moderate
Topic: MONITORING AND CONTROLLING FORECASTS
LO: 5.8: Explain how to monitor and control forecasts.
AACSB: Analytical thinking
Classification: Concept
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
