Mgmt | Operations Management homework help
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.
