Introduction to Mangement Science Chapter 10

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 of 0.3 will cause a forecast to react more quickly to a large error than
will an of 0.2.
True False
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