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Q:
Given the following data, compute the mean absolute deviation (MAD).
Q:
Given the following data, compute the mean squared error (deviation).
Q:
Given the following data, compute the total error (sum of the error terms).
Q:
A ____________ index is a weighted aggregate price index. It is accurate in its calculation of periodic prices. However, when using this index, it is difficult to compare the prices in different time periods.
A. Paasche
B. first-order autocorrelation
C. Laspeyres
D. cyclical (seasonal)
Q:
A ___________ index is a weighted aggregate price index that uses the base period quantities as weights in all succeeding time periods.
A. Paasche
B. first-order autocorrelation
C. Laspeyres
D. cyclical (seasonal)
Q:
The Consumer Price Index and the Producer Price Index are both calculated using the ___________ index formula.
A. Paasche
B. weighted aggregate
C. Laspeyres
D. cyclical (seasonal)
Q:
The Laspeyres index and the Paasche index are both examples of ____________ aggregate price indexes.
A. irregular
B. cyclical
C. trend
D. weighted
Q:
A simple index is computed by using the values of one time series, while a(n) ___________ index is based on a "market basket" consisting of more than one time series.
A. weighted
B. aggregate
C. cyclical
D. trend
Q:
A positive autocorrelation implies that negative error terms will be followed by _________ error terms.
A. negative
B. positive
C. either negative or positive
D. irregular
Q:
In the multiplicative decomposition method, the centered moving averages provide an estimate of a trend's ____________.
A. index
B. cycle
C. seasonal variation
D. irregular variation
Q:
A _______________ index is most useful if the base quantities provide a reasonable representation of consumption patterns in succeeding time periods.
A. Paasche
B. weighted aggregate
C. Laspeyres
D. cyclical (seasonal)
Q:
When preparing a price index based on multiple products, if the price of each product is weighted by the quantity of the product purchased in a given period of time, the resulting index is called a ___________ price index.
A. Paasche
B. weighted aggregate
C. Laspeyres
D. cyclical (seasonal)
Q:
A simple index is obtained by dividing the current value of a time series by the value of a time series in the _____________ time period and by multiplying this ratio by 100.
A. base
B. final
C. current
D. shortest
Q:
If a time series exhibits increasing seasonal variation, one approach is to first use a(n) ______________ transformation that produces a transformed time series that exhibits constant seasonal variation. Then, _________ variables can be used to model the time series with constant seasonal variation.
A. autocorrelation, dummy
B. fractional power, dummy
C. fractional power, constant
D. autocorrelation, constant
Q:
When deseasonalizing a time series observation, we divide the actual time series observation by its ___________.
A. irregular factor
B. cyclical factor
C. seasonal factor
D. weighted aggregate factor
Q:
When using simple exponential smoothing, the more recent the time series observation, the ____________ its corresponding weight.
A. larger
B. smaller
C. more irregular
D. more cyclical
Q:
The basic difference between MAD and MSE is that MSE, unlike MAD, penalizes a forecasting technique much more for _________ errors than for _________ errors.
A. large, small
B. small, large
C. small, zero
D. zero, large
Q:
Periodic patterns in time series that repeat themselves within a calendar year or less are referred to as _____________.
A. constant variations
B. cyclical variations
C. seasonal variations
D. regular variations
Q:
The Durbin-Watson statistic is used to detect _____________.
A. first-order autocorrelation
B. exponential smoothing
C. multiplicative decomposing
D. irregular variation
Q:
Weighting in exponential smoothing is accomplished by using _____________.
A. first-order autocorrelation
B. smoothing constants
C. the Durbin-Watson method
D. multiplicative decomposing
Q:
The purpose behind moving averages and centered moving averages is to eliminate __________________.
A. constant variation
B. cyclical variation
C. seasonal variation
D. regular variation
Q:
The upward or downward movement that characterizes a time series over a period of time is referred to as _____________.
A. seasonal variation
B. cyclical variation
C. a trend
D. irregular variation
Q:
A forecasting method that weights recent observations more heavily is called _____________.
A. time series analysis
B. first-order autocorrelation
C. multiplicative decomposition
D. exponential smoothing
Q:
The recurring up-and-down movement of a time series around trend levels that last more than one calendar year is called ____________.
A. constant variation
B. cyclical variation
C. seasonal variation
D. irregular variation
Q:
The Holt-Winters double exponential smoothing method is used to forecast time series data with ___________.
A. autocorrelation
B. a linear trend
C. cyclical patterns
D. moving averages
Q:
The ___________ test is a test for first-order positive autocorrelation.
A. Durbin-Watson
B. MSD
C. MAD
D. multiplicative Winters
Q:
When there is first-order autocorrelation, the error term in period t is related to the error term in period ______.A. tB. t + 1C. t - 1D. t - 2
Q:
When there is _______________ seasonal variation, the magnitude of the seasonal swing does not depend on the level of the time series.
A. cyclical
B. constant
C. irregular
D. increasing
Q:
The demand for a product for the last six years has been 15, 15, 17, 18, 20, and 19. The manager wants to predict the demand for this time series using the following simple linear trend equation: trt = 12 + 2t. Use this equation to forecast the demand for this product, and then calculate the MAD.
A. MAD = 1.333
B. MAD = 1.6
C. MAD = 2.0
D. MAD = 2.333
E. MAD = 2.5
Q:
The demand for a product for the last six years has been 15, 15, 17, 18, 20, and 19. The manager wants to predict the demand for this time series using the following simple linear trend equation: trt = 12 + 2t. Use this equation to forecast the demand for this product, and then calculate the MSD.
A. MSD = 6
B. MSD = 3.3333
C. MSD = 7.0
D. MSD = 2
E. MSD = 2.4
Q:
The demand for a product for the last six years has been 15, 15, 17, 18, 20, and 19. The manager wants to predict the demand for this time series using the following simple linear trend equation: trt = 12 + 2t. What are the forecast errors for the 5th and 6th years?A. 0, -3B. 0, +3C. +2, +5D. -2, -5E. -1, -4
Q:
When using simple exponential smoothing, the value of the smoothing constant cannot beA. negative.B. greater than zero.C. greater than 1.D. .99.E. negative or greater than 1.
Q:
XYZ Company, Annual Data Based on the information given in the table above, what is the MSD?
A. 1.3333
B. 1.6667
C. 2.5
D. 3.3333
E. 4.5
Q:
XYZ Company, Annual Data Based on the information given in the table above, what is the average forecast error?
A. 1.3333
B. 1.6667
C. 2.5
D. 3.3333
E. 4.5
Q:
XYZ Company, Annual Data Based on the information given in the table above, what is the MAD?
A. 1.3333
B. 1.6667
C. 2.5
D. 3.3333
E. 4.5
Q:
XYZ Company, Annual Data Based on the information given in the table above, we can conclude that, in general,
A. the forecasting method is underestimating demand.
B. the forecasting method is overestimating demand.
C. we cannot determine whether the predictions are underestimating or overestimating demand.
Q:
As you probably know, in a given week, the NYSE (New York Stock Exchange) is generally open from Monday through Friday. If we wanted to use the multiple regression method with dummy variables to study the impact of the day of the week on stock market performance, we would need ____ dummy variables.
A. 5
B. 52
C. 4
D. 3
E. 365
Q:
Seasonal variations are periodic patterns in a time series that are repeated over time. For which one of the following time series variables is it not possible to recognize seasonal variations?
A. quarters of the year
B. months of the year
C. days of the week
D. hours of the day
E. years
Q:
A sustained long-term change in the level of the variable that is being forecasted per unit of time is
A. a trend.
B. a time series.
C. seasonality.
D. a change due to business cycles.
Q:
Assume that the current date is February 1, 2003. The linear regression model was applied to a monthly time series based on the last 24 months' sales (from January 2000 through December 2002). The following partial computer output summarizes the results. At a significance level of .05, what is the value of the rejection point in testing the slope for significance?
A. 1.717
B. 1.96
C. 2.074
D. 1.645
E. 2.064
Q:
Assume that the current date is February 1, 2003. The linear regression model was applied to a monthly time series based on the last 24 months' sales (from January 2000 through December 2002). The following partial computer output summarizes the results. Determine the predicted sales for this month.
A. 45.9
B. 42.7
C. 44.3
D. 109.1
E. 113.4
Q:
In the multiplicative decomposition method, the centered moving averages provide an estimate of
A. trend seasonal.
B. trend cycle.
C. seasonal cycle.
D. trend irregular.
E. seasonal irregular.
Q:
When the magnitude of the seasonal swing does not depend on the level of a time series, we call this _________ variation.
A. increasing seasonal
B. cyclical seasonal
C. constant seasonal
D. decreasing seasonal
E. no seasonal
Q:
Which of the following time series forecasting methods would not be used to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns?
A. dummy variable regression
B. linear trend regression
C. Holt-Winters double exponential smoothing
D. multiplicative Winters method
E. both dummy variable regression and multiplicative Winters method
Q:
Which of the following time series forecasting methods would not be used to forecast seasonal data?
A. dummy variable regression
B. simple exponential smoothing
C. time series decomposition
D. multiplicative Winters method
Q:
Since a(n) ____________ index employs the base-period quantities in all succeeding periods, it allows for ready comparisons for identical quantities of goods purchased between the base period and all succeeding periods.
A. simple
B. aggregate
C. Laspeyres
D. Paasche
E. quantity
Q:
A major drawback of the aggregate price index is that
A. it does not take into account the fact that some items in the market basket are purchased more frequently than others.
B. it is difficult to compute.
C. it is computed by using the values from a single time series or based on a single product.
D. percentage comparisons cannot be made to the base year.
Q:
Suppose that the unadjusted seasonal factor for the month of April is 1.10. The sum of the 12 months' unadjusted seasonal factor values is 12.18. The normalized (adjusted) seasonal factor value for April
A. is larger than 1.1.
B. is smaller than 1.1.
C. is equal to 1.1.
D. cannot be determined with the information provided.
Q:
Those fluctuations that are associated with climate, holidays, and related activities are referred to as __________ variations.
A. trend
B. seasonal
C. cyclical
D. irregular
Q:
A sequence of values of some variable or composite of variables taken at successive, uninterrupted time periods is called a
A. least squares (linear) trend line.
B. moving average.
C. cyclical component.
D. time series.
E. seasonal factor.
Q:
When the moving average method is used to estimate the seasonal factors with quarterly sales data, a ______ period moving average is used.
A. 2
B. 3
C. 4
D. 5
E. 8
Q:
In general, the number of dummy variables used to model constant seasonal variation is equal to the number of
A. seasons.
B. seasons minus 1.
C. seasons plus 1.
D. seasons minus 2.
E. seasons divided by two.
Q:
The ___________ component of a time series refers to the erratic time series movements that follow no recognizable or regular pattern.
A. trend
B. seasonal
C. cyclical
D. irregular
Q:
The ___________ component of a time series reflects the long-run decline or growth in a time series.
A. trend
B. seasonal
C. cyclical
D. irregular
Q:
The ___________ component of a time series consists of erratic and unsystematic fluctuations in the time series data.
A. trend
B. seasonal
C. cyclical
D. irregular
Q:
The ________ component of a time series measures the fluctuations in a time series due to economic conditions of prosperity and recession with a duration of approximately 2 years or longer.
A. trend
B. seasonal
C. cyclical
D. irregular
Q:
A restaurant has been experiencing higher sales during the weekends, compared to the weekdays. Daily restaurant sales patterns for this restaurant over a week are an example of a(n) _________ component of a time series.
A. trend
B. seasonal
C. cyclical
D. irregular
Q:
When a forecaster uses the ______________ method, she or he assumes that the time series components are changing slowly over time.
A. time series regression
B. exponential smoothing
C. index number
D. multiplicative decomposition
Q:
When a forecaster uses the _________________ method, she or he assumes that the time series components are changing quickly over time.
A. time series regression
B. simple exponential smoothing
C. Box-Jenkins
D. multiplicative decomposition
Q:
All of the following are forecasting methods except
A. Holt-Winters double exponential smoothing.
B. simple exponential smoothing.
C. time series regression.
D. MAD autocorrelation.
Q:
If the errors produced by a forecasting method for 3 observations are -1, -2, and -6, then what is the mean squared error (deviation)?A. 9B. -9C. 3D. 13.67
Q:
If the errors produced by a forecasting method for 3 observations are +3, +3, and -3, then what is the mean squared error?A. 9B. 0C. 3D. -3E. 2
Q:
If the errors produced by a forecasting method for 3 observations are +3, +3, and -3, then what is the mean absolute deviation?A. 9B. 0C. 3D. −3
Q:
In the Durbin-Watson test, if the calculated d-statistic is greater than the upper value of the d-statistic, then
A. we do not reject H0, which says the error terms are not autocorrelated.
B. we do reject H0, which says the error terms are not autocorrelated.
C. the test is inconclusive.
D. we do reject H0, which says the error terms are positively or negatively autocorrelated.
Q:
The no-trend time series model is given by
A. TRt = B0 + B1t.
B. TRt = B0.
C. TRt = B0 + B1t + B2t2.
D. TRt= B0 + Bln(t).
Q:
Which of the following is not a component of time series?
A. trend
B. seasonal
C. cyclical
D. irregular
E. smoothing constant
Q:
Three criteria used to compare two forecasting methods are the mean absolute deviation, the mean squared deviation, and the mean absolute percentage error.
Q:
The Box-Jenkins methodology can be used to identify what is called an autoregressive-moving average model.
Q:
Simple exponential smoothing is a forecasting method that applies equal weights to the time series observations.
Q:
The multiplicative Winters method is used to forecast time series when there are no seasonal factors that are part of the model.
Q:
Causal variables can be used in forecasting models.
Q:
Exponential smoothing is designed to forecast time series described by regular and seasonal components that are always changing over time.
Q:
The multiplicative Winters' method used to forecast time series applies a seasonal factor SNT to the forecasting model..
Q:
Random shock is a value that is assumed to have been randomly selected that is the same for each and every time period.
Q:
Multiplicative decompositions assumes that time series components remain essentially constant over time.
Q:
Seasonal variations are periodic patterns in a time series that must last at least one year.
Q:
Box-Jenkins methodology is a more sophisticated approach to forecasting a time series with components that might be changing over time.
Q:
The simple moving average method is primarily useful in determining the impact of trend on a time series.
Q:
A positive autocorrelation implies that negative error terms will be followed by negative error terms.
Q:
Cyclical variation exists when the magnitude of the seasonal swing does not depend on the level of a time series.