Louisiana State University

ISDS 3115

Chapter 4 True/False

1)A naive forecast for September sales of a product would be equal to the forecast for August.

 

2. The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product.

 

3. Demand (sales) forecasts serve as inputs to financial, marketing, and personnel planning.

 

4. Forecasts of individual products tend to be more accurate than forecasts of product families.

 

5. Most forecasting techniques assume that there is some underlying stability in the system.

 

6. The sales force composite forecasting method relies on salespersons’ estimates of expected sales.

 

7. A time-series model uses a series of past data points to make the forecast.

 

8. The quarterly "make meeting" of Lexus dealers is an example of a sales force composite forecast.

 

9. Cycles and random variations are both components of time series.

 

10. A naive forecast for September sales of a product would be equal to the sales in August.

 

11. One advantage of exponential smoothing is the limited amount of record keeping involved.

 

12. The larger the number of periods in the simple moving average forecasting method, the greater the method's responsiveness to changes in demand.

 

13. Forecast including trend is an exponential smoothing technique that utilizes two smoothing constants: one for the average level of the forecast and one for its trend.

 

14. Mean Squared Error and Coefficient of Correlation are two measures of the overall error of a forecasting model.

 

15. In trend projection, the trend component is the slope of the regression equation.

 

16. In trend projection, a negative regression slope is mathematically impossible.

 

17. Seasonal indexes adjust raw data for patterns that repeat at regular time intervals.

 

18. If a quarterly seasonal index has been calculated at 1.55 for the October-December quarter, then raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compared to other quarters.

 

19. The best way to forecast a business cycle is by finding a leading variable.

 

20. Linear-regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables.

 

21. The larger the standard error of the estimate, the more accurate the forecasting model.

 

22. A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of time that passes.

 

23. In a regression equation where Y is demand and X is advertising, a coefficient of determination (R2) of .70 means that 70% of the variance in advertising is explained by demand.

 

24. Demand cycles for individual products can be driven by product life cycles.

 

25. If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.

 

26. Focus forecasting tries a variety of computer models and selects the best one for a particular application.

 

27. Many service firms use point-of-sale computers to collect detailed records needed for accurate short-term forecasts.

 

Multiple Choice

 

28. What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts regarding the six Orlando parks?

1. yesterday’s forecasted attendance and yesterday’s actual attendance
2. yesterday’s actual attendance and today’s forecasted attendance
3. yesterday’s forecasted attendance and today’s forecasted attendance
4. yesterday’s actual attendance and last year’s actual attendance
5. yesterday’s forecasted attendance and the year-to-date average daily forecast error

 

29. Using an exponential smoothing model with smoothing constant α = .20, how much weight would be assigned to the 2nd most recent period?

a. .16

b. .20

c. .04

d. .09

e. .10

 

30. Forecasts
    1. become more accurate with longer time horizons
    2. are rarely perfect
    3. are more accurate for individual items than for groups of items
    4. all of the above
    5. none of the above

 

31. One use of short-range forecasts is to determine
    1. production planning
    2. inventory budgets
    3. research and development plans
    4. facility location
    5. job assignments

 

32. Forecasts are usually classified by time horizon into three categories
    1. short-range, medium-range, and long-range
    2. finance/accounting, marketing, and operations
    3. strategic, tactical, and operational
    4. exponential smoothing, regression, and time series
    5. departmental, organizational, and industrial

 

33. A forecast with a time horizon of about 3 months to 3 years is typically called a
    1. long-range forecast
    2. medium-range forecast
    3. short-range forecast
    4. weather forecast
    5. strategic forecast

 

34. Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a
    1. short-range time horizon
    2. medium-range time horizon
    3. long-range time horizon
    4. naive method, because there is no data history
    5. all of the above

 

35. The three major types of forecasts used by business organizations are
    1. strategic, tactical, and operational
    2. economic, technological, and demand
    3. exponential smoothing, Delphi, and regression
    4. causal, time-series, and seasonal
    5. departmental, organizational, and territorial

 

36. Which of the following is not a step in the forecasting process?
    1. Determine the use of the forecast.

1. 2. Eliminate any assumptions.
   3. Determine the time horizon.
   4. Select forecasting model.
   5. Validate and implement the results.

 

37. The two general approaches to forecasting are
    1. qualitative and quantitative
    2. mathematical and statistical
    3. judgmental and qualitative
    4. historical and associative
    5. judgmental and associative

 

38. Which of the following uses three types of participants: decision makers, staff personnel, and respondents?
    1. executive opinions
    2. sales force composites
    3. the Delphi method
    4. consumer surveys
    5. time series analysis

 

39. The forecasting model that pools the opinions of a group of experts or managers is known as the
    1. sales force composition model
    2. multiple regression
    3. jury of executive opinion model
    4. consumer market survey model
    5. management coefficients model

 

40. Which of the following is not a type of qualitative forecasting?
    1. executive opinions
    2. sales force composites
    3. consumer surveys
    4. the Delphi method
    5. moving average

 

41. Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?
    1. associative models
    2. exponential smoothing
    3. weighted moving average
    4. simple moving average
    5. time series

 

42. Which of the following statements about time series forecasting is true ?
    1. It is based on the assumption that future demand will be the same as past demand.
    2. It makes extensive use of the data collected in the qualitative approach.
    3. The analysis of past demand helps predict future demand.
    4. Because it accounts for trends, cycles, and seasonal patterns, it is more powerful than causal forecasting.
    5. All of the above are true.

43. Time series data may exhibit which of the following behaviors?
    1. trend
    2. random variations
    3. seasonality
    4. cycles
    5. They may exhibit all of the above.

 

44. Gradual, long-term movement in time series data is called
    1. seasonal variation
    2. cycles
    3. trends
    4. exponential variation
    5. random variation

 

45. Which of the following is not present in a time series?
    1. seasonality
    2. operational variations
    3. trend
    4. cycles
    5. random variations

 

46. The fundamental difference between cycles and seasonality is the
    1. duration of the repeating patterns
    2. magnitude of the variation
    3. ability to attribute the pattern to a cause
    4. all of the above
    5. none of the above

 

47. In time series, which of the following cannot be predicted?
    1. large increases in demand
    2. technological trends
    3. seasonal fluctuations
    4. random fluctuations
    5. large decreases in demand

 

48. What is the approximate forecast for May using a four-month moving average?

 

Nov. Dec. Jan. Feb. Mar. April 39        36       40       42       48    46

1. 1. 38
   2. 42
   3. 43
   4. 44
   5. 47

 

49. Which time series model below assumes that demand in the next period will be equal to the most recent period's demand?
    1. naive approach
    2. moving average approach
    3. weighted moving average approach
    4. exponential smoothing approach

1. 5. none of the above

 

50. John’s House of Pancakes uses a weighted moving average method to forecast pancake sales.  It assigns a weight of 5 to the previous month’s demand, 3 to demand two months ago, and 1 to demand three months ago. If sales amounted to 1000 pancakes in May, 2200 pancakes in June, and 3000 pancakes in July, what should be the forecast for August?

a. 2400

b. 2511

c. 2067

d. 3767

e. 1622

 

51. A six-month moving average forecast is better than a three-month moving average forecast if demand
    1. is rather stable
    2. has been changing due to recent promotional efforts
    3. follows a downward trend
    4. follows a seasonal pattern that repeats itself twice a year
    5. follows an upward trend

 

52. Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of
    1. manager understanding
    2. accuracy
    3. stability
    4. responsiveness to changes
    5. All of the above are diminished when the number of periods increases.

 

53. Which of the following statements comparing the weighted moving average technique and exponential smoothing is true ?
    1. Exponential smoothing is more easily used in combination with the Delphi method.
    2. More emphasis can be placed on recent values using the weighted moving average.
    3. Exponential smoothing is considerably more difficult to implement on a computer.
    4. Exponential smoothing typically requires less record keeping of past data.
    5. Exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted moving averages does not.

 

54. Which time series model uses past forecasts and past demand data to generate a new forecast?
    1. naive
    2. moving average
    3. weighted moving average
    4. exponential smoothing
    5. regression analysis

 

55. Which is not a characteristic of exponential smoothing?
    1. smoothes random variations in the data
    2. easily altered weighting scheme
    3. weights each historical value equally
    4. has minimal data storage requirements
    5. none of the above; they are all characteristics of exponential smoothing

56. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
    1. 0
    2. 1 divided by the number of periods

c. 0.5

d. 1.0

e. cannot be determined

 

57. Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would be

a. 94.6

b. 97.4

c. 100.6

d. 101.6

e. 103.0

 

58. A forecast based on the previous forecast plus a percentage of the forecast error is a(n)
    1. qualitative forecast
    2. naive forecast
    3. moving average forecast
    4. weighted moving average forecast
    5. exponentially smoothed forecast

 

59. Given an actual demand of 61, a previous forecast of 58, and an α of .3, what would the forecast for the next period be using simple exponential smoothing?

a. 45.5

b. 57.1

c. 58.9

d. 61.0

e. 65.5

 

60. Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors?

a. 0.10

b. 0.20

c. 0.40

d. 0.80

e. cannot be determined

 

61. A forecasting method has produced the following over the past five months. What is the mean absolute deviation?

 

Actual Forecast Error |Error|

10	11	-1	1
8	10	-2	2
10	8	2	2
6	6	0	0
9	8	1	1
a. -0.2 b. -1.0 c.  0.0 d.  1.2

e.  8.6

 

62. The primary purpose of the mean absolute deviation (MAD) in forecasting is to
    1. estimate the trend line
    2. eliminate forecast errors
    3. measure forecast accuracy
    4. seasonally adjust the forecast
    5. all of the above

 

63. Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?
    1. 2
    2. 3
    3. 4
    4. 8
    5. 16

 

64. The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is
    1. 2

b. -10

c. 3.5

d. 9

e. 10.5

 

65. A time series trend equation is 25.3 + 2.1 X. What is your forecast for period 7? a. 23.2

b. 25.3

c. 27.4

d. 40.0

e. cannot be determined

 

66. For a given product demand, the time series trend equation is 53 - 4 X. The negative sign on the slope of the equation
    1. is a mathematical impossibility
    2. is an indication that the forecast is biased, with forecast values lower than actual values
    3. is an indication that product demand is declining
    4. implies that the coefficient of determination will also be negative
    5. implies that the RSFE will be negative

 

67. Yamaha manufacturers which set of products with complementary demands to address seasonal fluctuations?

1. golf clubs and skis
2. swimming suits and winter jackets
3. jet skis and snowmobiles
4. pianos and guitars
5. ice skates and water skis

 

68. Which of the following is true regarding the two smoothing constants of the Forecast Including Trend (FIT) model?
    1. One constant is positive, while the other is negative.

1. 2. They are called MAD and RSFE.
   3. Alpha is always smaller than beta.
   4. One constant smoothes the regression intercept, whereas the other smoothes the regression slope.
   5. Their values are determined independently.

 

69. Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25.

What is the seasonally-adjusted sales forecast for January?

1. 1. 640 units
   2. 798.75 units
   3. 800 units
   4. 1000 units
   5. cannot be calculated with the information given

 

70. A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July is

a. 0.487

b. 0.684

c. 1.462

d. 2.053

e. cannot be calculated with the information given

 

71. A fundamental distinction between trend projection and linear regression is that
    1. trend projection uses least squares while linear regression does not
    2. only linear regression can have a negative slope
    3. in trend projection the independent variable is time; in linear regression the independent variable need not be time, but can be any variable with explanatory power
    4. linear regression tends to work better on data that lack trends
    5. trend projection uses two smoothing constants, not just one

 

72. The percent of variation in the dependent variable that is explained by the regression equation is measured by the
    1. mean absolute deviation
    2. slope
    3. coefficient of determination
    4. correlation coefficient
    5. intercept

 

73. The degree or strength of a linear relationship is shown by the
    1. alpha
    2. mean
    3. mean absolute deviation
    4. correlation coefficient
    5. RSFE

 

74. If two variables were perfectly correlated, the correlation coefficient r would equal
    1. 0
    2. -1
    3. 1
    4. b or c

1. 5. none of the above

 

75. The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate
    1. qualitative methods
    2. adaptive smoothing
    3. slope
    4. bias
    5. trend projection

 

76. The tracking signal is the
    1. standard error of the estimate
    2. running sum of forecast errors (RSFE)
    3. mean absolute deviation (MAD)
    4. ratio RSFE/MAD
    5. mean absolute percentage error (MAPE)

 

77. Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of
    1. exponential smoothing including trend
    2. adaptive smoothing
    3. trend projection
    4. focus forecasting
    5. multiple regression analysis

 

78. Many services maintain records of sales noting
    1. the day of the week
    2. unusual events
    3. weather
    4. holidays
    5. all of the above

 

79. Taco Bell's unique employee scheduling practices are partly the result of using
    1. point-of-sale computers to track food sales in 15 minute intervals
    2. focus forecasting
    3. a six-week moving average forecasting technique
    4. multiple regression
    5. a and c are both correct