Quantitative Analysis For Management 13th by Barry Render -Test Bank

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?

1. A) exponential smoothing
2. B) Delphi method
3. C) jury of executive opinion
4. 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

1. A) exponential smoothing.
2. B) the Delphi method.
3. C) jury of executive opinion.
4. 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

 

 

35) Which of the following is considered a causal method of forecasting?

1. A) exponential smoothing
2. B) moving average
3. C) linear regression
4. 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

1. A) scatter diagram.
2. B) trend projection.
3. C) radar chart.
4. 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?

1. A) Time is always plotted on the y-axis.
2. B) It can depict the relationship among three variables simultaneously.
3. C) It is helpful when forecasting with qualitative data.
4. 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?

1. A) exponential smoothing
2. B) regression
3. C) Delphi method
4. 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

 

39) When is the exponential smoothing model equivalent to the naïve forecasting model?

1. A) α = 0
2. B) α = 0.5
3. C) α = 1
4. 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?

196. A) 196.00
197. B) 230.67
198. C) 100.00
199. 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?

1. A) MAD
2. B) MSE
3. C) MAPE
4. D) bias

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

1. A) the third method is the best.
2. B) the second method is the best.
3. C) method two is the least preferred.
4. 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?

1. A) MAD
2. B) MSE
3. C) MAPE
4. 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.

1. A) 14
2. B) 13
3. C) 15
4. 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

 

 

45) As one increases the number of periods used in the calculation of a moving average

1. A) greater emphasis is placed on more recent data.
2. B) less emphasis is placed on more recent data.
3. C) the emphasis placed on more recent data remains the same.
4. 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

116. A) 116.7.
117. B) 168.3.
118. C) 135.0.
119. 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?

1. A) MAD
2. B) MSE
3. C) MAPE
4. D) decomposition

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

1. A) regression.
2. B) decomposition.
3. C) smoothing.
4. 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?

1. A) 0
2. B) 5
3. C) 7
4. 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?

1. A) 0
2. B) 5
3. C) 7
4. D) 54

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).

12. A) 12.8
13. B) 13.0
14. C) 70.0
15. 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.

14. A) 14.5
15. B) 13.5
16. C) 14
17. 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?

1. A) trend
2. B) seasonality
3. C) variance
4. 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

 

 

54) Which of the following statements about the decomposition method is false?

1. A) The process of “deseasonalizing” involves multiplying by a seasonal index.
2. B) Dummy variables are used in a regression model as part of an additive approach to seasonality.
3. C) Computing seasonal indices is the first step of the decomposition method.
4. 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).

118. A) 118.96
119. B) 121.17
120. C) 130
121. 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)?

1. A) 123
2. B) 107
3. C) 100
4. 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

 

 

57) A TIME SERIES forecasting model in which the forecast for the next period is the actual value for the current period is the

1. A) Delphi model.
2. B) Holt’s model.
3. C) naïve model.
4. 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

1. A) produces a nice-looking curve.
2. B) equals the utility level that matches with our degree of risk aversion.
3. C) produces values which compare well with actual values based on a standard measure of error.
4. 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?

1. A) compute seasonal indices using CMAs
2. B) find the equation of the trend line using the deseasonalized data
3. C) forecast for future periods using the trend line
4. 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

 

 

60) A method to measure how well predictions fit actual data is

1. A) decomposition
2. B) smoothing
3. C) tracking signal
4. 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?

1. A) 170
2. B) 185
3. C) 215
4. 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

1. A) new forecast.
2. B) Y-axis intercept.
3. C) independent variable.
4. 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

 

 

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?

23. A) 23.2
24. B) 25
25. C) 27
26. 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

1. A) exponential smoothing.
2. B) adaptive smoothing.
3. C) trend projections.
4. 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?

1. A) The variable being predicted is the Y
2. B) Time is the X
3. C) It is useful for predicting the value of one variable based on time trend.
4. 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

 

66) A tracking signal was calculated for a particular set of demand forecasts. This tracking signal was positive. This would indicate that

1. A) demand is greater than the forecast.
2. B) demand is less than the forecast.
3. C) demand is equal to the forecast.
4. 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.

1. A) 10
2. B) 0.5
3. C) 0
4. 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?

1. A) 0.4286
2. B) 2.3333
3. C) 5
4. 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

 

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?

1. A) 0.01
2. B) 0.06
3. C) 0.09
4. 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	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?

1. A) 0.60
2. B) 0.65
3. C) 0.70
4. 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

 

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?

1. A) 0.369
2. B) 0.468
3. C) 0.573
4. 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	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?

2. A) 2.92%
3. B) 3.08%
4. C) 3.17%
5. 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

 

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)?

1. A) -0.833
2. B) -1.333
3. C) 0.833
4. 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.

 

#	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?

1. A) Observation #5
2. B) Observation #7
3. C) Observation #4
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

 

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?

1. A) Observation #5
2. B) Observation #7
3. C) Observation #4
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

 

 

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?

3. A) 3.99
4. B) 3.93
5. C) 3.86
6. 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

 

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?

5. A) 5.92%
6. B) 6.02%
7. C) 6.12%
8. 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

 

 

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?

1. A) -0.5
2. B) 0
3. C) 1.33
4. 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

 

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?

14. A) 14.38
15. B) 2.88
16. C) 11.28
17. 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

 

 

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?

14. A) 14.38
15. B) 2.88
16. C) 11.28
17. 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

 

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?

181. A) 181.7
182. B) 183.9
183. C) 185.1
184. 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

 

 

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?

151. A) 151.7
152. B) 153.9
153. C) 155.1
154. 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.

1. A) 90
2. B) 88
3. C) 94
4. 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

 

 

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

1. A) 92
2. B) 88
3. C) 94
4. 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.

3. A) 3.9
4. B) 4.1
5. C) 4.3
6. 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

 

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

 

30. A) 30.18
31. B) 30.62
32. C) 30.96
33. 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	Donut Sales
January	32
February	33
March	28
April	39

 

29. A) 29.93
30. B) 29.17
31. C) 30.22
32. 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

 

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.

58. A) 58.57
59. B) 63.23
60. C) 52.25
61. 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	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.

58. A) 58.57
59. B) 63.23
60. C) 52.25
61. 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

 

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?

1. A) -1.86
2. B) -1.04
3. C) 1.04
4. 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

 

 

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?

1. A) -0.75
2. B) -2.67
3. C) 0.75
4. 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	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?

2. A) 2.17
3. B) 2.66
4. C) 3.29
5. 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

 

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?

1. A) -0.17
2. B) -0.33
3. C) -0.55
4. 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?

1. A) 0
2. B) 1
3. C) -1
4. 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

 

 

95) The tracking signal is the running sum of the forecast errors divided by the

1. A) MAD.
2. B) MSE.
3. C) RSFE.
4. 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

3. A) ±3.
4. B) ±4.
5. C) ±8.
6. 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

3. A) ±3.
4. B) ±4.
5. C) ±8.
6. 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

 

98) One MAD is equivalent to approximately

1. A) 0.8.
2. B) 1.2.
3. C) 1.6.
4. 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

 

 

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.

2. Deseasonalize the data by dividing each number by its seasonal index.
3. Find the equation of a trend line using the deseasonalized data.
4. Forecast for future periods using the trend line.
5. Multiply the trend line forecast by the appropriate seasonal index.

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