Ch3 Extrapolation 1. Moving Averages And Verified Test Bank

Forecasting and Predictive Analytics with Forecast X, 7e (Keating)

Chapter 3 Extrapolation 1. Moving Averages and Exponential Smoothing

1) What factors do the data smoothing techniques presented in Chapter Three have in common?

A) They all use only past observations of the data.

B) They all fail to forecast cyclical reversals in the data.

C) They all smooth short-term noise by averaging data.

D) They all produce serially correlated forecasts.

E) All of the options are correct.

2) Time series smoothing techniques work best for applications where

A) little historical data are available to the forecaster.

B) there is a large amount of historical data available.

C) the forecast horizon is the distant future.

D) only periodic forecasts for untimely events are required.

E) All of the options are correct.

3) Time-series smoothing techniques attempt to

A) suppress short-term variability in the data.

B) identify long-term trends or cycles in the data.

C) remove seasonality in the data.

D) suppress data noise while extracting trend.

E) All of the options are correct.

4) A simple-centered 3-point moving average of the time-series variable Xt is given by:

A) (Xt-1 + Xt-2 + Xt-3)/3.

B) (Xt + Xt-1 + Xt-2)/3.

C) (Xt+1 + Xt + Xt-1)/3.

D) None of the options are correct.

5) Which of the following is not a problem with moving-average forecasting?

A) It produces serially correlated forecasts.

B) It removes short-term variability by averaging nearby data.

C) It cannot predict reversals in trends.

D) It cannot model non-stationary data.

E) All of the options are correct.

6) With which type of time-series data should moving-average smoothing methods produce the best forecasts?

A) Seasonal.

B) Stationary.

C) Trending.

D) Cyclical.

E) All of the options are correct.

7) In using moving-average smoothing to generate forecasts, a three-month moving average will be preferred to a six-month moving average

A) if the true data cycle is three months.

B) if it has a lower RMSE.

C) if it has a lower mean-squared error.

D) if we have very little data to work with.

E) All of the options are correct.

8) Moving-average smoothing may lead to misleading inference when applied to

A) stationary data.

B) forecasting trend reversal in the stock market.

C) small and limited data sets.

D) large and plentiful data sets.

E) None of the options are correct.

9) Which method uses an arithmetic mean to forecast the next period?

A) Naïve.

B) Moving averages.

C) Exponential smoothing.

D) Adaptive filtering.

E) None of the options are correct.

10) Some drawbacks to using centered moving-average smoothing models include

A) loss of data at each end of the original time series.

B) introduction of autocorrelation into the forecasts.

C) inability to forecast turning points in the data.

D) All of the options are correct.

11) Which forecasting model assumes that the pattern exhibited by historical data can best be represented by an arithmetic average of nearby observations?

A) Simple exponential smoothing.

B) Naïve methods.

C) Moving average smoothing.

D) Holt's smoothing.

E) None of the options are correct.

12) Which method is used to develop a simple model that assumes that weighted averages of past periods are the best predictors of the future?

A) Naïve.

B) Moving averages.

C) Exponential smoothing.

D) Naïve model squared.

E) None of the options are correct.

13) Simple-exponential smoothing models are useful for data which have

A) a downward time trend.

B) an upward time trend.

C) neither an upward or downward time trend.

D) pronounced seasonality.

E) All of the options are correct.

14) Simple-exponential smoothing models differ from moving average models in that

A) moving average models use weighted averages of the data whereas simple exponential smoothing models use simple averages.

B) simple exponential smoothing models use weighted averages of the data whereas moving average models use simple averages.

15) Which of the following is a factor in the decision to use exponential smoothing rather than moving-average smoothing to forecast a given time series?

A) Amount of data available.

B) Importance of recent past versus distant past.

C) Forecast horizon.

D) Expertise of the forecast manager.

E) None of the options are correct.

16) The term 'exponential' in the exponential smoothing method refers to

A) weights on past data that increase exponentially into the past.

B) weights on past data that decrease exponentially into the past.

C) calculation uses a weighted average.

D) using a non-weighted polynomial on past data.

E) None of the options are correct.

17) The error-correction form of the simple exponential smoothing model states that if the current forecast

A) error was zero, the current forecast could be used to forecast next period's level.

B) overstated the actual level, the forecast of the level next period will be revised downward.

C) understated the actual level, the forecast of the level next period will be revised upward.

D) All of the options are correct.

18) Which of the following is not correct concerning choosing the appropriate size of the level smoothing constant (α or alpha) in the simple exponential smoothing model?

A) Select values close to zero if the series has a great deal of random variation.

B) Select values close to one if you wish the forecast values to depend strongly on recent changes in the actual values.

C) Select a value that minimizes RMSE.

D) Select a value that maximizes mean-squared error.

E) All of the options are correct.

19) The simple exponential smoothing model can be expressed as

A) a simple average of past values of the data.

B) an expression combining the most recent forecast and actual data value.

C) a weighted average, where the weights sum to zero.

D) a weighted average, where the weights sum to the sample size.

E) None of the options are correct.

20) The same benefits/criticisms apply to moving average and exponential smoothing with the exception of

A) amount of data required.

B) ease of calculation.

C) ability to model trend.

D) ability to forecast cyclical reversals.

E) None of the options are correct.

21) Choosing the appropriate size of the level smoothing constant (α) in the simple exponential smoothing model

A) is equivalent to asking, "How much weight should be given in revising our forecast for next period to this period's forecast error?"

B) can best be determined by subjective means.

C) is simple if the data are stationary since α should be zero.

D) is simple if the data are nonstationary since α should be one.

22) The smoothing constant in the exponential smoothing model

A) completely determines the weight structure in exponential smoothing.

B) can be interpreted as the revision of this period's forecast to today's forecast error.

C) cannot be equal to 0 or 1.

D) must lie between 0 and 1.

E) All of the options are correct.

23) Which of the following is not a major problem with exponential smoothing?

A) It requires a large amount of data and time to generate forecasts.

B) It requires that the forecaster choose, on some basis, the smoothing constant.

C) It produces forecasts that are serially correlated.

D) It employs only past data in making forecasts of the future.

E) All of the options are correct.

24) Which of the following is not considered a smoothing model?

A) Naïve.

B) Moving averages.

C) Exponential smoothing.

D) Adaptive-Response-Rate Single Exponential Smoothing.

E) None of the options are correct.

25) Simple Smoothing

If a smoothing constant of .3 is used, what is the exponentially smoothed forecast for period 4?

A) 106.6.

B) 103.0.

C) 115.0.

D) 112.6.

E) 104.4.

26) Simple Smoothing

If a smoothing constant of .3 is used, what is the exponentially smoothed forecast for period 4?

A) −3.

B) −12.

C) −10.

D) −7.

E) +7.

27) Simple Smoothing

If a three-month moving-average model is used, what is the forecast for period 4?

A) 104.4.

B) 106.6.

C) 107.1.

D) 108.3.

E) 110.2.

28) If the smoothing constant were chosen to be unity, the exponential smoothing model would equal

A) moving average smoothing.

B) Holt's exponential smoothing.

C) the simple naïve model.

D) Winter's exponential smoothing.

E) moving average smoothing with a one-year lag.

29) What do moving-average smoothing and exponential smoothing have in common?

A) They both require only a limited amount of data.

B) They both are simple to use.

C) They both are simple to understand.

D) They both have no ability to adjust for trend in the data.

E) All of the options are correct.

30) The level smoothing constant (α) of the simple exponential smoothing model

A) should have a value close to one if the underlying data is relatively erratic.

B) should have a value close to zero if the underlying data is relatively smooth.

C) should have a value closer to zero, the greater the revision in the current forecast given the current forecast error.

D) should have a value closer to one, the greater the revision in the current forecast given the current forecast error.

31) In the Holt's two-parameter smoothing model, the trend smoothing parameter Gamma

A) should be close to one when the data has a relatively smooth trend.

B) should be close to zero when the data has a relatively smooth trend.

C) should be close to one when α is close to one.

D) should be close to one when α is one.

32) Holt's forecasted values

A) contain no estimate of trend in the underlying series.

B) are superior when the underlying data has pronounced seasonality.

C) for periods into the future lie along a straight line.

D) are simple centered moving averages.

E) None of the options are correct.

33) The error-correction representation of Holt's algorithm shows

A) how both the level and slope forecasts are revised for current forecast errors.

B) that no adjustment is made to this period's forecasts when the current forecast error is zero.

C) how seasonality estimates are revised for current forecast errors.

D) All of the options are correct.

34) The Holt's forecasting model uses

A) naïve methods.

B) moving averages.

C) exponential smoothing.

D) adaptive filtering.

E) None of the options are correct.

35) Holt's smoothing is best applied to data that are

A) nonseasonal.

B) nonstationary.

C) deseasonalized with a trend.

D) nonstationary and nonseasonal.

E) All of the options are correct.

36) Holt's model accounts for any growth factor present in a time series by

A) use of a linear trend.

B) smoothing the most recent trend by last period's smoothed trend.

C) adding trend estimates to level forecasts.

D) using simple exponential smoothing to estimate a trend factor that is then combined in a linear fashion with the level forecast.

E) All of the options are correct.

37) Winter's exponential smoothing

A) is appropriate for data with both trend and seasonal components.

B) models account for seasonality in a multiplicative manner.

C) models have three smoothing parameters.

D) models use only past observations of a time series.

E) All of the options are correct.

38) Which of the following is not an aspect of the Winter's exponential smoothing model?

A) Holt's model extended to deseasonalized data

B) Simple exponential smoothing applied to nonstationary data

C) Seasonality estimates that are themselves smoothed

D) Trend estimates that are themselves smoothed

E) All of the options are correct.

39) As an example of how Winter's smoothing model deals with seasonality, how would actual quarter-four sales of a retail firm be deseasonalized?

A) It would be divided by a seasonal factor.

B) It would be multiplied by a seasonal factor.

C) It would be added with a seasonal factor.

D) It would be subtracted from a seasonal factor.

E) None of the options are correct.

40) Which of the following is not correct? Winter's exponential smoothing model adjusts for data seasonality by

A) deseasonalizing the data in an additive fashion.

B) deseasonalizing the data in a multiplicative fashion.

C) use of a smoothing constant applied to seasonality estimates.

D) linear smoothing of seasonality estimates.

E) All of the options are correct.

41) If the time series of interest is highly random, the seasonal smoothing constant (Beta) of the Winter's model should be set

A) equal to zero.

B) at a small positive value.

C) at a large positive value but less than unity.

D) at unity.

E) None of the options are correct.

42) How many parameters must the forecaster (or the software) set using Winter's exponential smoothing?

A) 0.

B) 1.

C) 2.

D) 3.

E) None of the options are correct.

43) How many parameters must the forecaster (or the software) set using Adaptive-Response-Rate Single Exponential Smoothing?

A) 0.

B) 1.

C) 2.

D) 3.

E) None of the options are correct.

44) In the Adaptive-Response-Rate Single Exponential Smoothing model, the smoothing parameter

A) is not a constant.

B) varies from period to period.

C) is determined by the ratio of the absolute value of the smoothed error divided by the absolute smoothed error.

D) is the ratio of two smoothed error measures.

E) All of the options are correct.

45) The Adaptive-Response-Rate Single Exponential Smoothing model is best applied to time series data that are

A) nonstationary.

B) seasonal.

C) seasonal and nonstationary.

D) stationary and seasonal.

E) stationary and nonseasonal.

46) The Adaptive-Response-Rate Single Exponential Smoothing model is termed adaptive because

A) it responds to changes in the pattern of data.

B) the smoothing parameter changes each period.

C) it has the ability to model changes in the mean of time series.

D) it can virtually take care of itself in generating forecasts.

E) All of the options are correct.

47) The Adaptive-Response-Rate Single Exponential Smoothing model can be amended to handle seasonal data by

A) first deseasonalizing, then reseasonalizing the data.

B) deseasonalizing the data.

C) reseasonalizing the data.

D) smoothing the data trend first.

E) None of the options are correct.

48)

The simple equation above represents

A) a Logistics function.

B) a Croston intermittent function.

C) a Probit function.

D) a Gompertz function.

49)

The simple equation above represents

A) a Logistics function.

B) a Croston intermittent function.

C) a Probit function.

D) a Gompertz function.

50) Growth models like those used in ForecastX usually model situations well where a process grows

A) at a more or less constant rate.

B) until reaching saturation.

C) in a linear fashion.

D) at an exponential rate.

51) The growth models used in ForecastX are sometimes called

A) exponential models.

B) smoothing models.

C) event models.

D) diffusion models.

52) The "L" independent variable in the growth models we examined represents

A) the upper limit of the "Y" variable.

B) the number of observations in the original data set.

C) the growth rate of the dependent variable.

D) the lower limit of the dependent variable.

53) When using a growth model under the assumption that constant improvement becomes harder to achieve as growth takes place, the best model to use is

A) an Event model.

B) a Logistics Model.

C) a Gompertz model.

D) a Croston intermittent model.

54) The logistics model

A) looks like an exponential function that is concave upward.

B) looks like an exponential function that is concave downward.

C) looks like an "S" curve.

D) approximates a straight line.

55) When using growth curves such as the Gompertz model or the Logistics model,

A) it does not matter which model is selected; they are equivalent.

B) it is necessary to have a large data set.

C) only short term forecasts are possible.

D) it is customary to specify a saturation point.

56)

The above equation is used to estimate a Gompertz curve. The "L" in the equation refers to

A) the growth rate of Y.

B) the growth rate of X.

C) the maximum value of Y.

D) the maximum value of X.

57) The Gompertz growth model

A) is best used when it is harder to achieve constant improvement as a maximum value is approached.

B) is best used when there are factors that assist in maintaining improvements as the maximum value is approached.

C) should not be used to estimate new product sales.

D) is always preferred to the Logistics model.

58) "Event Models" as used in ForecastX

A) are a form of exponential smoothing.

B) are a type of growth model.

C) are a type of simple regression.

D) are a type of moving average.

59) "Events" in an Event model could include

A) seasonality, trends, and cyclicality.

B) advertising campaigns, sale prices, and couponing.

C) audit dates and forecasting deadlines.

D) the first sale date, last sale date, and growth rate for an item.

60) Smoothing 2

Consider the ForecastX printout above. This is the forecast for a manufactured product.

A) This is a Winter's Exponential Smoothing model.

B) This is a Holt's Smoothing model.

C) This is an Event model.

D) This is a Simple Smoothing model.

61) Smoothing 2

Consider the ForecastX printout above.

A) There is little trend in the data.

B) There is clear seasonality in the data.

C) The event indices show some (but small) promotional effect.

D) All of the options are correct.

62) Smoothing 2

Consider the ForecastX printout above. The seasonal index 4 has a value of 1.45. This indicates

A) that sales in period 4 are usually below average.

B) that sales in period 4 are usually above average.

C) that sales in period 4 are usually quite close to the period average.

D) that sales in period 4 have no seasonal effect.

63) Smoothing 2

The Trend factor above is given as 0.00.

A) This indicates that there is little (or no) seasonality.

B) This indicates that there is little (or no) trend.

C) This indicates that the events have little (or no) effect on sales.

D) This indicates that the model has little (or no) explanatory power.

64) Smoothing 2

In the ForecastX model presented above,

A) all of the events appear to contribute positively to sales.

B) some of the events appear to contribute negatively to sales.

C) none of the events appear to contribute negatively to sales

D) none of the events appear to contribute positively to sales.

65) In event models,

A) events are analogous to seasons in a seasonal model.

B) events need not be defined in the forecast period.

C) the researcher is unable to specify the underlying model.

D) "load" and "deload" factors are never used.

66) Winters

Consider the Audit Trail statistics for a Winters model above.

A) The Trend value of 0.00 indicates trend is present.

B) The Trend value of 0.00 indicates seasonality is present.

C) The Trend value of 0.00 indicates no trend is present.

D) The Trend value of 0.00 indicates no seasonality is present.

67) Winters

In the Winters smoothing model above,

A) the Seasonal value of 1.00 indicates a high degree of seasonality is present.

B) the Seasonal value of 1.00 indicates a low degree of seasonality is present.

C) the Seasonal value of 1.00 indicates that the trend is positive.

D) None of the options are true.

68) Winters

In the Winters model shown above, index 1 refers to calendar month 1 in the data.

A) Thus, calendar month 3 is a below average month.

B) Thus, calendar month 3 is an above average month.

C) Thus, calendar month 3 is an average month.

D) Nothing can be deduced about calendar month 3.

69) Winters

In the Winters model above, "Decomposition Type"

A) refers to the type of trend calculation used in the model.

B) refers to the type of seasonality calculation used in the model.

C) refers to the calculation method used to estimate the Level factor.

D) refers to the calculation method used to estimate the MAPE.

70) Winters

The Winters model above

A) could reasonably be used to forecast 4 months into the future.

B) should only be used to forecast one month into the future.

C) is considered a long-range forecasting model.

D) is quite inaccurate and probably should not be used for forecasting.

71) Growth

Consider the growth model Audit Trail statistics shown above. The "Maximum" shown here as 1,200.00

A) is a value calculated to be the largest value the model may achieve.

B) is a value set to be the largest value the model may achieve.

C) is a value representing the maximum growth rate possible over the forecast period.

D) is a value representing the square of the maximum growth rate possible over the forecast period.

72) Growth

In the growth model Audit Trail shown above, a Gompertz Curve was probably selected because

A) it was harder to achieve constant improvement as the maximum value was approached.

B) it was easier to achieve constant improvement as the maximum value was approached.

C) a "bell shaped" function was expected.

D) the trend was nonlinear.

73) Growth

In the growth model Audit Trail shown above, the saturation point is

A) 100 percent.

B) 15.55.

C) 1,200.

D) 66.28.

74) A Logistics Model assumes

A) it is harder to achieve constant improvement as the maximum value was approached.

B) it is easier to achieve constant improvement as the maximum value was approached.

C) a "bell shaped" function is expected.

D) the trend is linear.

75) In the Bass Model, the p coefficient (as used in ForecastX)

A) is the coefficient of imitation.

B) will be little effected by purchasing power.

C) will tend to be lower if the product exhibits significant network effects.

D) will tend to be higher as more disposable income makes it easier to adopt innovations.

76) The Bass Model

A) is a type of diffusion model.

B) is a form of exponential smoothing.

C) is used for short-range forecasting.

D) does not require a limiting value like the logistics model.

77) When forecasting the adoption of cellular telephones with the Bass Model,

A) we should expect little impact from the choice of a market potential.

B) we should expect turning points to be predicted accurately.

C) we should expect relatively high r values because of the nature of the product.

D) we should expect relatively low p values because of the nature of the product.

78) medfly

The first 23 observations in a data set involving the mortality of medflies is shown above. The column titled "living" indicates the number of living flies in each day of the experiment.

Consider that you wish to predict the outcome of the experiment only ten days into the experiment. That is, you wish to forecast when the last medfly will expire. You do so with the model shown above.

What method was used to fit the model to the original ten data points?

A) A Smoothing Model

B) A Moving Average Model

C) A Diffusion Model

D) A Winters Model

E) None of the options are correct.

79) medfly

The first 23 observations in a data set involving the mortality of medflies is shown above. The column titled "living" indicates the number of living flies in each day of the experiment.

Consider that you wish to predict the outcome of the experiment only ten days into the experiment. That is, you wish to forecast when the last medfly will expire. You do so with the model shown above.

On approximately what date is the medfly population living expected to reach zero?

A) September 27th

B) October 11th

C) November 8th

D) December 27th

80) medfly

The first 23 observations in a data set involving the mortality of medflies is shown above. The column titled "living" indicates the number of living flies in each day of the experiment.

Consider that you wish to predict the outcome of the experiment only ten days into the experiment. That is, you wish to forecast when the last medfly will expire. You do so with the model shown above.

The model chosen for this estimation was probably chosen because

A) there may be an offsetting factor such that growth is more difficult to maintain as the endpoint is approached.

B) there is no offsetting factor hindering the attainment of the endpoint.

C) declining values are always estimated using this type of model.

D) growth can never be negative.

E) None of the options are correct.

81) medfly

The first 23 observations in a data set involving the mortality of medflies is shown above. The column titled "living" indicates the number of living flies in each day of the experiment.

Consider that you wish to predict the outcome of the experiment only ten days into the experiment. That is, you wish to forecast when the last medfly will expire. You do so with the model shown above.

When specifying the model used above, some limits were probably set by the forecaster. These would probably have been

A) a minimum value of 1203646 and a maximum value of some "very high number."

B) a minimum value of 0 and a maximum value of 1,197,093.36.

C) a minimum value of 0 and a maximum value of some "very high number."

D) left to be determined statistically by the forecasting software.

82) medfly

The first 23 observations in a data set involving the mortality of medflies is shown above. The column titled "living" indicates the number of living flies in each day of the experiment.

Consider that you wish to predict the outcome of the experiment only ten days into the experiment. That is, you wish to forecast when the last medfly will expire. You do so with the model shown above.

The Model used to estimate the above medfly model was probably

A)

B)

C)

D)

E) None of the options are correct.

83) In an Event Model, the term "load"

A) probably refers to a period of reduced prices.

B) probably refers to a period of increased prices.

C) probably refers to the period immediately following a promotion.

D) probably refers to the value of the alpha factor for the event.

E) None of the options are correct.

84) Smoothing 3

In running an exponential smoothing model, the following results were obtained:

The Seasonal value listed above (in Smoothing 3) indicates that the model

A) is probably unreliable for forecasting.

B) has a very high level smoothing constant.

C) exhibits a rather high degree of trend.

D) exhibits a rather high degree of seasonality.

E) None of the options are correct.

85) Smoothing 3

In the smoothing model listed above called Smoothing 3 (assuming calendar quarter 1 is the first quarter in the data set),

A) the greatest seasonal variation appears in calendar quarter 1.

B) the greatest seasonal variation appears in calendar quarter 4.

C) there appears to be little seasonal variation between quarters.

D) the greatest seasonal variation appears in calendar quarter 3.

E) None of the options are correct. The data is monthly.

86) Smoothing 3

In the smoothing model above called Smoothing 3, the Trend coefficient reported

A) indicates a high degree of seasonality.

B) indicates some trend in the data.

C) indicates an almost stationary data set.

D) is statistically insignificant.

E) None of the options are correct.

87) Smoothing 3

For the smoothing model shown above called Smoothing 3, the product that is modeled is probably most like which of the following products in terms of its yearly sales pattern? Assume Index 1 refers to the quarter containing January, February, and March.

A) New housing sales

B) Clothing sales (like The Gap)

C) Mustard sales (like French's Yellow Mustard)

D) Human insulin sales

E) None of these products would be similar to the sales pattern exhibited by the smoothing model above.

88) Consider the smoothing model results shown in the following graph of actual and predicted sales:

The darker line above is the actual data, and the lighter line is the fitted data.

Which of the following would be a likely set of parameters to see in this exponential smoothing estimate?

A) Level = 0.37, Seasonal = 0.22, Trend = 0.01

B) Level = 0.05, Seasonal = 0.00, Trend = 0.37

C) Level = 0.37, Trend = 0.01

D) Level = 0.44

89) Consider the Bass model results shown below:

This model predicts percentage of adoptions over time for a particular product. The results show

A) the product is only poorly forecast with a Bass model.

B) the product has a relatively high innovation rate characteristic.

C) the product has a relatively high imitation rate characteristic.

D) the product exhibits no standard growth pattern.

E) None of the options are correct.

90) Which of the following statements about any moving-averages series is correct?

A) A moving-averages series can lie consistently above or below the original data, namely, when they are growing or declining exponentially.

B) Such a series will anticipate or prolong changes in the original data and, thus, show a different timing of turning points.

C) Such a series will be extremely sensitive to unusually large or small values in the time series, as any average is bound to be.

D) All are correct.

91) Which of the following is the best general definition of exponential smoothing?

A) It is a forecasting procedure that produces self-correcting forecasts by means of a built-in adjustment mechanism that corrects for earlier forecasting errors: The technique produces a weighted average of all past time-series values with weights decreasing exponentially as one goes back in time, and the average so constructed serves as a forecast for the next period.

B) It is a procedure that constructs a series of numbers by successively averaging overlapping groups of two or more consecutive values in a time series and replacing the central value in each group by the group's average.

C) It is a procedure that produces artificial (and, therefore, misleading) waves in a moving- averages series, even when there are no waves in the original time series.

D) None of the options are correct.

92) An exponential smoothing technique that adds a trend smoothing constant to the single-parameter exponential smoothing technique is known as

A) two-parameter (or double) exponential smoothing.

B) three-parameter (or triple) exponential smoothing.

C) the easiest way to produce a seasonally adjusted time series.

D) the ratio-to-moving-average method.

93) The simple moving average technique

A) works better for long-range forecasts than short-range forecasts.

B) reacts well to random variations.

C) reacts well to variations that occur for a reason.

D) requires minimal amount of data.

94) Which of the following is true concerning the smoothing parameter (α) used in exponential smoothing?

A) α = 0.4 means the forecast for the next period is based on 40% older data and 60% recent data.

B) If α = 0, the forecast is equivalent to the naïve forecast.

C) The higher the value of α, the less the effect of smoothing.

D) The higher the value of α, the more the effect of smoothing.

95) Given demands, D1 = 20, D2 = 16, and D3 = 12, what is F5 using the naïve forecasting method?

A) F5 = 8

B) F5 = 12

C) F5 = 16

D) Inconclusive from the given data

96) The Gompertz model

A) cannot be used to forecast the sales of a new product.

B) results in an "L-shaped" curve.

C) was originally used to test for mortality of fruit flies.

D) is the inverse of the Logistics model.

97) The Department of Energy used the Bass model to forecast the adoption of solar batteries. In order to select p and q values,

A) they used a survey of home builders.

B) they set the qbar (or maximum) value and let the software optimize the p and q.

C) they selected p and q values of a similar, but older, product.

D) they used a jury of executive opinion technique.

98) The Slutsky-Yule effect

A) applies to exponential smoothing models only.

B) can increase the accuracy/fit of most models if used correctly.

C) explains the misleading "patterns" that are suggested by moving average models.

D) helps explain model outcomes that use cross-sectional data.

99) Which of the following is not true regarding simple exponential smoothing?

A) It requires a large amount of data (i.e., many variables).

B) It has no ability to adjust for seasonal variation.

C) It has no ability to predict "turning points" in the data.

D) It has no ability to account for secular trend.

100) When the level smoothing constant of an estimated simple exponential smoothing model is close to one,

A) the model is quite similar to a naïve model.

B) the model is weighting the most distant (in terms of time) observations the heaviest.

C) the model is weighting every observation equally.

D) there are too few data points to use the technique.

101) Holt Winters exponential smoothing model uses three smoothing constants. Which of the following is not one of those constants?

A) trend smoothing constant

B) seasonal smoothing constant

C) level smoothing constant

D) secular smoothing constant

102)

A portion of the estimate using ForecastX for forecasting GAP sales is shown. Keep in mind that the first quarter appearing in the data is calendar quarter 1. "Index 4" of 1.33 means

A) quarter 4 sales averaged 133% of sales in an average quarter.

B) sales at GAP are expected to rise about 33% for each quarter in the future.

C) that four quarters into the future, sales will have risen to 133% of average.

D) that approximately 1.33 times each previous month's sales will predict next month's sales.

103) "Index 1" in the GAP results must refer to what time period?

A) The first quarter of the year

B) The second quarter of the year

C) The third quarter of the year

D) The fourth quarter of the year