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    Multiple Choice Questions : Time Series Analysis

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    1. Time series methods
    a. discover a pattern in historical data and project it into the future.
    b. include cause-effect relationships.
    c. are useful when historical information is not available.
    d. All of the alternatives are true.

    2. Gradual shifting of a time series over a long period of time is called
    a. periodicity.
    b. cycle.
    c. regression.
    d. trend.

    3. Seasonal components
    a. cannot be predicted.
    b. are regular repeated patterns.
    c. are long runs of observations above or below the trend line.
    d. reflect a shift in the series over time.

    4. Short-term, unanticipated, and nonrecurring factors in a time series provide the random variability known as
    a. uncertainty.
    b. the forecast error.
    c. the residuals.
    d. the irregular component.

    5. The focus of smoothing methods is to smooth
    a. the irregular component.
    b. wide seasonal variations.
    c. significant trend effects.
    d. long range forecasts.

    6. Forecast errors
    a. are the difference in successive values of a time series
    b. are the differences between actual and forecast values
    c. should all be non-negative
    d. should be summed to judge the goodness of a forecasting model

    7. To select a value for alpha for exponential smoothing
    a. use a small alpha when the series varies substantially.
    b. use a large alpha when the series has little random variability.
    c. use any value between 0 and 1
    d. All of the alternatives are true.

    8. Linear trend is calculated as Tt = 28.5 + .75t. The trend projection for period 15 is
    a. 11.25
    b. 28.50
    c. 39.75
    d. 44.25

    9. The multiplicative model
    a. uses centered moving averages to smooth the trend fluctuations.
    b. removes trend before isolating the seasonal components.
    c. depersonalizes a time series by dividing the values by the appropriate seasonal index.
    d. provides a unique seasonal index for each observation of the time series.

    10. Causal models
    a. should avoid the use of regression analysis.
    b. attempt to explain a time series' behavior.
    c. do not use time series data.
    d. All of the alternatives are true.

    11. A qualitative forecasting method that obtains forecasts through "group consensus" is known as the
    a. Autoregressive model
    b. Delphi approach
    c. mean absolute deviation
    d. None of these alternatives is correct.

    12. The trend component is easy to identify by using
    a. moving averages
    b. exponential smoothing
    c. regression analysis
    d. the Delphi approach

    13. The forecasting method that is appropriate when the time series has no significant trend, cyclical, or seasonal effect is
    a. moving averages
    b. mean squared error
    c. mean average deviation
    d. qualitative forecasting methods

    14. If data for a time series analysis is collected on an annual basis only, which component may be ignored?
    a. trend
    b. seasonal
    c. cyclical
    d. irregular

    15. One measure of the accuracy of a forecasting model is the
    a. smoothing constant
    b. trend component
    c. mean absolute deviation
    d. seasonal index

    1. A hospital records the number of floral deliveries its patients receive each day. For a two week period, the records show
    15, 27, 26, 24, 18, 21, 26, 19, 15, 28, 25, 26, 17, 23
    Use exponential smoothing with a smoothing constant of .4 to forecast the number of deliveries.

    2. In order to forecast the attendance at an annual tennis tournament, a model has been developed which uses attendance from the previous year and the amount spent for advertising this year. From the years shown in the table, forecast the attendance for years 2-5 and calculate the forecast error.

    Attendance Advertising Expenditure
    1 8363 750
    2 9426 1250
    3 9318 3200
    4 10206 4500
    5 11018 5600

    The multiple regression model is Attendance = 6738 + .23($) + .25 (Attlag)

    Note: 'Attlag' means last-year's actual attendance. So in forecasting the second year's attendance, we'd use the first year's actual attendance value, and so forth.

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    Solution Summary

    This solution provides answers to multiple choice question from time series analysis