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 Copyright © 2018 Pearson Education, Inc. 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 Copyright © 2018 Pearson Education, Inc. 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 Copyright © 2018 Pearson Education, Inc. 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 Copyright © 2018 Pearson Education, Inc. 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