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MGMT E – 5070     DATA MINING AND FORECAST MANAGEMENT

1st EXAMINATION , ( Forecast Error, Time Series Models, Tracking Signals )

 

 

True or False

 

  1.   T  F   According to the textbook, a short-term forecast typically covers a 1-year time

                  horizon.

 

  2.   T  F   Regression is always a superior forecasting method to exponential smoothing.

                 

  3.   T  F   The 3 categories of forecasting models are time series, quantitative, and

                  qualitative. 

 

  4.   T  F   Time-series models attempt to predict the future by using historical data.

 

  5.   T  F   A moving average forecasting method is a causal forecasting method.

 

  6.   T  F   An exponential forecasting method is a time-series forecasting method.

 

  7.   T  F   The Delphi method solicits input from customers or potential customers

                   regarding their future purchasing plans.

 

  8.   T  F   The naïve forecast for May, for example, is the actual value observed in April.

 

  9.   T  F   Mean absolute deviation ( MAD ) is simply the sum of the forecast errors.

 

10.   T  F   Four components of time series are trend, moving average, exponential

                  smoothing, and seasonality.

 

11.   T  F   In a weighted moving average, the weights assigned must sum to “ 1 “.

 

12.   T  F   A scatter diagram for a time series may be plotted on a two-dimensional graph

                  with the horizontal axis representing the variable to be forecast ( such as

                  sales ).

 

13.   T  F   An advantage of exponential smoothing over a simple moving average is that

                  exponential smoothing requires one to retain less data.

 

 

 

 

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14.   T  F   Time-series models rely on judgement in an attempt to incorporate qualitative

                  or subjective factors into the forecasting model.

 

15.   T  F   When the smoothing coefficient “ α “ equals “1”, the exponential smoothing

                  model is equivalent to the naïve forecasting model.

 

16.   T   F   Bias is the average error of a forecast model.

 

17.   T   F   Scatter diagrams can be useful in spotting trends or cycles in data over time.

 

18.   T   F   A trend-projection forecasting method is a causal forecasting method.

 

19.   T   F   A scatter diagram is useful to determine if a relationship exists between two 

                   variables.

 

20.   T   F   Qualitative models attempt to incorporate judgemental or subjective factors

                    into the forecasting model.

 

21.   T   F   Time-series models enable the forecaster to include specific representations

                   of  various qualitative and quantitative factors.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MGMT E – 5070    DATA MINING AND FORECAST MANAGEMENT

Professor Vaccaro

1st EXAMINATION   ( Forecast Error, Time Series Models, Tracking Signals )

 

NAME________________________________________________________

 

 

Multiple Choice

 

22.    Which of the following is not classified as a qualitative forecasting model?

 

          a.  exponential smoothing.

          b.  Delphi.

          c.  jury of executive opinion.

          d.  sales force composite.

          e.  consumer market survey.

 

 

23.    A graphical plot with sales on the Y axis and time on the X axis is a :

 

         a.  scatter diagram.

         b.  trend projection.

         c.  radar chart.

         d.  line graph.

         e.  bar chart.

 

 

24.   Which of the following is a technique used to determine forecast accuracy?

 

         a.  exponential smoothing.

         b.  moving average.

         c.  regression.

         d. Delphi method.

         e. mean absolute percent error.

 

 

25 .   According to the textbook, a medium-term forecast is considered to cover what  

         length of time?

 

         a.  2 to 4 weeks.

         b.  1 month to 1 year.

         c.  2 to 4 years.

         d.  5 to 10 years.

         e.  20 years.

 

 

 

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26.   Which of the following methods tells whether the forecast tends to be too high or

         too low?

 

         a.  MAD

         b.  MSE

         c.  MAPE

         d.  Decomposition.

         e.  Bias

 

 

27.   Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16,

        15, 12, 18, 14, 12, 13, 15 ( listed from oldest to most recent ). Forecast sales for the

         next day using a two-day moving average.

 

         a.  14

         b.  13

         c.  15

         d.  28

         e.  12.5  

 

 

28.   As one increases the number of periods used in the calculation of a moving average:

 

        a.  greater emphasis is placed on more recent data.

        b.  less emphasis is placed on more recent data.

        c.  the emphasis placed on more recent data remains the same.

        d.  it requires a computer to automate the calculations.

        e.  one is usually looking for a long-term prediction.

 

 

29.   Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16,

       15, 12, 18, 14, 12, 13, 15 ( listed from oldest to most recent ). Forecast sales for the

        next day using a three-day weighted moving average where the weights are 3, 1, and

        1  ( the highest weight is for the most recent number ).

 

        a.  12.8

        b.  13.0

        c.  70.0

        d.  14.0

 

 

 

 

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30.   Which of the following is not considered one of the components of a time series?

 

        a.  trend.

        b.  seasonality.

        c.  variance.

        d.  cycles.

        e.  random variations.

 

 

31.   Enrollment in a particular class for the last four semesters has been 120, 126, 110,

        and 130 ( listed from oldest to most recent ). Develop a forecast of enrollment next

        semester using exponential smoothing with an alpha ( α ) = 0.2 . Assume that an

        initial forecast for the first semester was 120 ( so the forecast and the actual were

        the same ).

 

        a.  118.96

        b.  121.17

        c.  130

        d.  120

        e.  none of the above

 

 

32.   A tracking signal was calculated for a particular set of demand forecasts. This

        tracking signal was positive. This would indicate that:

 

        a.  demand is greater than the forecast.

        b.  demand is less than the forecast.

        c.  demand is equal to the forecast.

        d.  the MAD is negative.

        e.  none of the above.

 

 

 

 

 

 

 

 

 

 

 

 

 

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33.   Enrollment in a particular class for the last four semesters has been 120, 126, 110,

        and 130 ( listed from oldest to most recent ). Suppose a one-semester moving

        average was used to forecast enrollment ( this is sometimes referred to as a naïve

        forecast ). Thus, the forecast for the second semester would be 120, for the third

        semester it would be 126, and for the last semester it would be 110. What would

        the MSE be for this situation?

 

        a.  196.00    

        b.  230.67

        c.  100.00

        d.  42.00

        e.  none of the above.

 

34.  Which of the following methods gives an indication of the percentage of forecast

        error?

 

        a.   MAD

        b.   MSE

        c.   MAPE

        d.   Decomposition

        e.   Bias

 

35.   Enrollment in a particular class for the last four semesters has been 122, 128, 100,

        and 155 ( listed from oldest to most recent ). The best forecast of enrollment next

        semester, based on a three-semester moving average, would be:

 

        a.   116.7

        b.   126.3

        c.   168.3

        d.   135.0

        e.   127.7

 

36.   Which of the following is not a characteristic of trend projections?

 

         a.   the variable being predicted is the ‘Y’ variable.

         b.   time is the ‘X’ variable.

         c.   it is useful for predicting the value of one variable based on time trend.

         d.   a negative Y-intercept always implies that the dependent variable is

               decreasing over time.

         e.   they are often developed using linear regression.

 

 

 
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