ISDS 3115 Exam 1 Chapter 4
95. __________ forecasting tries a variety of computer models and selects the best one for a particular application.
Focus
82. __________ forecasts are projections of demand for a company's products or services
Demand
81. _________ forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.
Economic
90. _____________ is a measure of overall forecast error for a model.
MAD or Mean Absolute Deviation
83. __________ forecasts employ one or more mathematical models that rely on historical data and/or causal variables to forecast demand.
Quantitative
84. ___________ is a forecasting technique based upon salespersons' estimates of expected sales.
Sales force composite
85. __________ forecasts use a series of past data points to make a forecast.
Time-series
92. ____________ is a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts.
Trend projection
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
a. 0.10
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? a. 640 units b. 798.75 units c. 800 units d. 1000 units e. cannot be calculated with the information given
a. 640 units
41. Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand? a. associative models b. exponential smoothing c. weighted moving average d. simple moving average e. time series
a. associative models
46. The fundamental difference between cycles and seasonality is the a. duration of the repeating patterns b. magnitude of the variation c. ability to attribute the pattern to a cause d. all of the above e. none of the above
a. duration of the repeating patterns
51. A six-month moving average forecast is better than a three-month moving average forecast if demand a. is rather stable b. has been changing due to recent promotional efforts c. follows a downward trend d. follows a seasonal pattern that repeats itself twice a year e. follows an upward trend
a. is rather stable
49. Which time series model below assumes that demand in the next period will be equal to the most recent period's demand? a. naive approach b. moving average approach c. weighted moving average approach d. exponential smoothing approach e. none of the above
a. naive approach
37. The two general approaches to forecasting are a. qualitative and quantitative b. mathematical and statistical c. judgmental and qualitative d. historical and associative e. judgmental and associative
a. qualitative and quantitative
32. Forecasts are usually classified by time horizon into three categories a. short-range, medium-range, and long-range b. finance/accounting, marketing, and operations c. strategic, tactical, and operational d. exponential smoothing, regression, and time series e. departmental, organizational, and industrial
a. short-range, medium-range, and long-range
88. Linear regression is known as a(n) _____________ because it incorporates variables or factors that might influence the quantity being forecast.
associative model
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
b. 0.684
36. Which of the following is not a step in the forecasting process? a. Determine the use of the forecast. b. Eliminate any assumptions. c. Determine the time horizon. d. Select forecasting model. e. Validate and implement the results
b. Eliminate any assumptions.
50. Which of the following is not a characteristic of simple moving averages? a. It smoothes random variations in the data. b. It has minimal data storage requirements. c. It weights each historical value equally. d. It lags changes in the data. e. It smoothes real variations in the data.
b. It has minimal data storage requirements.
77. Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of a. exponential smoothing including trend b. adaptive smoothing c. trend projection d. focus forecasting e. multiple regression analysis
b. adaptive smoothing
67. In trend-adjusted exponential smoothing, the forecast including trend (FIT) consists of a. an exponentially smoothed forecast and an estimated trend value b. an exponentially smoothed forecast and a smoothed trend factor c. the old forecast adjusted by a trend factor d. the old forecast and a smoothed trend factor e. a moving average and a trend factor
b. an exponentially smoothed forecast and a smoothed trend factor
30. Forecasts a. become more accurate with longer time horizons b. are rarely perfect c. are more accurate for individual items than for groups of items d. all of the above e. none of the above
b. are rarely perfect
35. The three major types of forecasts used by business organizations are a. strategic, tactical, and operational b. economic, technological, and demand c. exponential smoothing, Delphi, and regression d. causal, time-series, and seasonal e. departmental, organizational, and territorial
b. economic, technological, and demand
33. A forecast with a time horizon of about 3 months to 3 years is typically called a a. long-range forecast b. medium-range forecast c. short-range forecast d. weather forecast e. strategic forecast
b. medium-range forecast
45. Which of the following is not present in a time series? a. seasonality b. operational variations c. trend d. cycles e. random variations
b. operational variations
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
c. 100.6
63. Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation? a. 2 b. 3 c. 4 d. 8 e. 16
c. 4
59. Given an actual demand of 61, a previous forecast of 58, and an 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
c. 58.9
42. Which of the following statements about time series forecasting is true? a. It is based on the assumption that future demand will be the same as past demand. b. It makes extensive use of the data collected in the qualitative approach. c. The analysis of past demand helps predict future demand. d. Because it accounts for trends, cycles, and seasonal patterns, it is more powerful than causal forecasting. e. All of the above are true.
c. The analysis of past demand helps predict future demand.
29. Which of the following statements regarding Tupperware's forecasting is false? a. Tupperware's fifty profit centers generate the basic set of projections. b. Tupperware uses at least three quantitative forecasting techniques. c. Tupperware uses only quantitative forecasting techniques. d. "Sales per active dealer" is one of three key forecasting variables (factors). e. "Jury of executive opinion" is the ultimate forecasting tool used at Tupperware.
c. Tupperware uses only quantitative forecasting techniques.
72. The percent of variation in the dependent variable that is explained by the regression equation is measured by the a. mean absolute deviation b. slope c. coefficient of determination d. correlation coefficient e. intercept
c. coefficient of determination
71. A fundamental distinction between trend projection and linear regression is that a. trend projection uses least squares while linear regression does not b. only linear regression can have a negative slope c. 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 d. linear regression tends to work better on data that lack trends e. trend projection uses two smoothing constants, not just one
c. 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
66. For a given product demand, the time series trend equation is 53 - 4 X. The negative sign on the slope of the equation a. is a mathematical impossibility b. is an indication that the forecast is biased, with forecast values lower than actual values c. is an indication that product demand is declining d. implies that the coefficient of determination will also be negative e. implies that the RSFE will be negative
c. is an indication that product demand is declining
39. The forecasting model that pools the opinions of a group of experts or managers is known as the a. sales force composition model b. multiple regression c. jury of executive opinion model d. consumer market survey model e. management coefficients model
c. jury of executive opinion model
34. Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a a. short-range time horizon b. medium-range time horizon c. long-range time horizon d. naive method, because there is no data history e. all of the above
c. long-range time horizon
62. The primary purpose of the mean absolute deviation (MAD) in forecasting is to a. estimate the trend line b. eliminate forecast errors c. measure forecast accuracy d. seasonally adjust the forecast e. all of the above
c. measure forecast accuracy
38. Which of the following uses three types of participants: decision makers, staff personnel, and respondents? a. executive opinions b. sales force composites c. the Delphi method d. consumer surveys e. time series analysis
c. the Delphi method
44. Gradual, long-term movement in time series data is called a. seasonal variation b. cycles c. trends d. exponential variation e. random variation
c. trends
55. Which is not a characteristic of exponential smoothing? a. smoothes random variations in the data b. easily altered weighting scheme c. weights each historical value equally d. has minimal data storage requirements e. none of the above; they are all characteristics of exponential smoothing
c. weights each historical value equally
93. The ______________________ measures the strength of the relationship between two variables.
coefficient of correlation
74. If two variables were perfectly correlated, the correlation coefficient r would equal a. 0 b. less than 1 c. exactly 1 d. -1 or +1 e. greater than 1
d. -1 or +1
56. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? a. 0 b. 1 divided by the number of periods c. 0.5 d. 1.0 e. cannot be determined
d. 1.0
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
d. 40.0
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 a. 38 b. 42 c. 43 d. 44 e. 47
d. 44
53. Which of the following statements comparing the weighted moving average technique and exponential smoothing is true? a. Exponential smoothing is more easily used in combination with the Delphi method. b. More emphasis can be placed on recent values using the weighted moving average. c. Exponential smoothing is considerably more difficult to implement on a computer. d. Exponential smoothing typically requires less record keeping of past data. e. Exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted moving averages does not.
d. Exponential smoothing typically requires less record keeping of past data.
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 a. qualitative methods b. adaptive smoothing c. slope d. bias e. trend projection
d. bias
73. The degree or strength of a linear relationship is shown by the a. alpha b. mean c. mean absolute deviation d. correlation coefficient e. RSFE
d. correlation coefficient
54. Which time series model uses past forecasts and past demand data to generate a new forecast? a. naive b. moving average c. weighted moving average d. exponential smoothing e. regression analysis
d. exponential smoothing
28. Tupperware's use of forecasting a. involves only a few statistical tools b. concentrates on the low-level dealer, and is not aggregated at the company level c. relies on the fact that all of its products are in the maturity phase of the life cycle d. is a major source of its competitive edge over its rivals e. takes inputs from sales, marketing, and finance, but not from production
d. is a major source of its competitive edge over its rivals
47. In time series, which of the following cannot be predicted? a. large increases in demand b. technological trends c. seasonal fluctuations d. random fluctuations e. large decreases in demand
d. random fluctuations
76. The tracking signal is the a. standard error of the estimate b. running sum of forecast errors (RSFE) c. mean absolute deviation (MAD) d. ratio RSFE/MAD e. mean absolute percentage error (MAPE)
d. ratio RSFE/MAD
52. Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of a. manager understanding b. accuracy c. stability d. responsiveness to changes e. All of the above are diminished when the number of periods increases.
d. responsiveness to changes
68. Which of the following is true regarding the two smoothing constants of the Forecast Including Trend (FIT) model? a. One constant is positive, while the other is negative. b. They are called MAD and RSFE. c. Alpha is always smaller than beta. d. One constant smoothes the regression intercept, whereas the other smoothes the regression slope. e. Their values are determined independently.
e. Their values are determined independently.
43. Time series data may exhibit which of the following behaviors? a. trend b. random variations c. seasonality d. cycles e. They may exhibit all of the above
e. They may exhibit all of the above
79. Taco Bell's unique employee scheduling practices are partly the result of using a. point-of-sale computers to track food sales in 15 minute intervals b. focus forecasting c. a six-week moving average forecasting technique d. multiple regression e. a and c are both correct
e. a and c are both correct
78. Many services maintain records of sales noting a. the day of the week b. unusual events c. weather d. holidays e. all of the above
e. all of the above
58. A forecast based on the previous forecast plus a percentage of the forecast error is a(n) a. qualitative forecast b. naive forecast c. moving average forecast d. weighted moving average forecast e. exponentially smoothed forecast
e. exponentially smoothed forecast
31. One use of short-range forecasts is to determine a. production planning b. inventory budgets c. research and development plans d. facility location e. job assignments
e. job assignments
40. Which of the following is not a type of qualitative forecasting? a. executive opinions b. sales force composites c. consumer surveys d. the Delphi method e. moving average
e. moving average
87. The smoothing constant is a weighting factor used in ______________.
exponential smoothing
91. When one constant is used to smooth the forecast average and a second constant is used to smooth the trend, the forecasting method is __________________.
exponential smoothing with trend adjustment or trend-adjusted smoothing or second-order smoothing
80. Tupperware uses several forecasting techniques, but the final forecasts make use of the technique known as _________.
jury of executive opinion
89. A measure of forecast error that does not depend on the magnitude of the item being forecast is the ___________.
mean absolute percent error or MAPE
86. A(n) ______________ forecast uses an average of the most recent periods of data to forecast the next period.
moving average
94. If a barbershop operator noted that Tuesday's business was typically twice as heavy as Wednesday's, and that Friday's business was typically the busiest of the week, business at the barbershop is subject to ____________.
seasonal variations