ch10 1.
Forecasts are rarely perfect. True False
2.
Once accepted by managers, forecasts should not be overridden. True False
3.
Statistical models to forecast economic trends are called econometric models. True False
4.
The difference between a forecast and what turns out to be the true value is called the mean absolute deviation. True False
5.
The mean absolute deviation is the sum of the absolute value of forecasting errors divided by the number of forecasts. True False
6.
The mean square error is the square of the mean of the absolute deviations. True False
7.
The mean absolute deviation is more sensitive to large deviations than the mean square error. True False
8.
The seasonal factor for any period of a year measures how that period compares to the same period last year. True False
9.
Removing the seasonal component from a time-series can be accomplished by dividing each value by its appropriate seasonal factor. True False
10. The last-value forecasting method requires a linear trend line. True False 11. The last-value forecasting method is most useful when conditions are stable over time. True False 12. The averaging method uses all the data points in the time-series. True False 13. A moving-average forecast tends to be more responsive to changes in the time-series data when more values are included in the average. True False 14. The moving-average forecasting method assigns equal weights to each value that is represented by the average. True False 15. The moving-average forecasting method is a very good one when conditions remain pretty much the same over the time period being considered. True False 16. An advantage of the exponential smoothing forecasting method is that more recent experience is given more weight than less recent experience. True False
17. A smoothing constant of 0.1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a value of 0.3 will. True False 18. If significant changes in conditions are occurring relatively frequently, then a smaller smoothing constant is needed. True False 19. Exponential smoothing with trend requires selection of two smoothing constants. True False 20. Exponential smoothing with trend was designed for time-series that have great variability both up and down. True False 21. Forecasting techniques such as moving-average, exponential smoothing, and the last-value method all represent averaged values of time-series data. True False 22. In exponential smoothing, an will an of 0.2. True False
of 0.3 will cause a forecast to react more quickly to a large error than
23. The goal of time-series forecasting methods is to estimate the mean of the underlying probability distribution of the next value of the time-series as closely as possible. True False 24. If a time-series has exactly the same distribution for each and every time period, then the averaging forecasting method provides the best estimate of the mean. True False 25. A time-series is said to be smooth if its underlying probability distribution usually remains the same from one period to the next. True False 26. Causal forecasting obtains a forecast for a dependent variable by relating it directly to one or more independent variables. True False 27. Linear regression can be used to approximate the relationship between independent and dependent variables. True False 28. Judgmental forecasting methods have been developed to interpret statistical data. True False 29. The sales force composite method is a top-down approach to forecasting. True False 30. The Delphi method involves the use of a series of questionnaires to achieve a consensus forecast. True False 31. When statistical forecasting methods are used, it is no longer necessary to use judgmental methods as well. True False
32. Forecasts can help a manager to: A. anticipate the future. B. develop strategies. C. make staffing decisions. D. All of these. E. None of these. 33. In business, forecasts are the basis for: A. sales planning. B. inventory planning. C. production planning. D. budgeting. E. All of these. 34. Which of the following are costs of an inaccurate forecast? A. Lost sales. B. Inventory. C. An understaffed office. D. Lower profits. E. All of these. 35. Time-series data may exhibit which of the following behaviors? A. Trend. B. Seasonality. C. Cycles. D. Irregularities. E. All of these. 36. Gradual, long-term movement in time-series values is called: A. seasonal variation. B. trend. C. cycles. D. irregular variation. E. random variation. 37. The last-value forecasting method: A. is quick and easy to prepare. B. is easy for users to understand. C. ignores all values except one. D. All of these. E. None of these. 38. Using the latest value in a sequence of data to forecast the next period is: A. a moving-average forecast. B. a last-value forecast. C. an exponentially smoothed forecast. D. a causal forecast. E. None of these.
39. What is the last-value forecast for the next period? A. 58. B. 62. C. 60. D. 61. E. None of these. 40. What is the moving-average forecast for the next period based on the last three periods. A. 58. B. 62. C. 60. D. 61. E. None of these. 41. In order to increase the responsiveness of a forecast made using the moving-average method, the number of values in the average should be: A. decreased. B. increased. C. multiplied by a larger . D. multiplied by a smaller . E. None of these. 42. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a last-value forecast? A. 0. B. 0.01. C. 0.1. D. 0.5. E. 1. 43. Given an actual latest demand of 59, a previous forecast of 64, and for the next period using the exponential smoothing method? A. 36.9. B. 57.5. C. 60.5. D. 62.5. E. 65.5.
= 0.3, what would be the forecast
44. Given an actual latest demand of 105, a previous forecast of 97, and for the next period using the exponential smoothing method? A. 80.8. B. 93.8. C. 100.2. D. 101.8. E. 108.2.
= 0.4, what would be the forecast
45. Which of the following possible values of quickly to forecast errors? A. 0. B. 0.01. C. 0.05. D. 0.1. E. 0.15.
would cause exponential smoothing to respond the most
46. In exponential smoothing with trend, the forecast consists of: A. an exponentially smoothed forecast and a smoothed trend factor. B. the old forecast adjusted by a trend factor. C. the old forecast and a smoothed trend factor. D. a moving-average and a trend factor. E. None of these. 47. The mean absolute deviation is used to: A. estimate the trend line. B. eliminate forecast errors. C. measure forecast accuracy. D. seasonally adjust the forecast. E. All of these. 48. Given forecast errors of 4, 8, and –3, what is the mean absolute deviation? A. 3. B. 4. C. 5. D. 6. E. 9. 49. Given forecast errors of 4, 8, and –3, what is the mean square error? A. 5 B. 9 C. 25 D. 29.67 E. 89 50. Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation? A. 1. B. 2. C. 2.5. D. 3. E. 12. 51. Given forecast errors of 5, 0, -4, and 3, what is the mean square error? A. 3. B. 4. C. 12. D. 12.5. E. 50. 52. Given the following historical data, what is the moving-average forecast for period 6 based on the last
three periods? A. 67. B. 68. C. 69. D. 100. E. 115.