Which of the following statements are true?

[removed]

Exponential smoothing with trend requires selection of two smoothing constants.

[removed]

Judgmental forecasting methods have been developed to interpret statistical data.

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The sales force composite method is a top-down approach to forecasting.

[removed]

The moving-average forecasting method assigns unequal weights to each value in the average.

[removed]

None of the above

In order to increase the responsiveness of a forecast made using the moving-average method, the number of values in the average should be:

[removed]

multiplied by a larger alpha.

[removed]

multiplied by a smaller alpha.

[removed]

adjusted with smoothing.

[removed]

increased from the current value.

[removed]

None of the above

Which of the following statements is true with regard to linear regression?

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It can be used to calculate a trend line.

[removed]

It must have a slope coefficient.

[removed]

It relates a dependent variable to an independent variable.

[removed]

it assumes a linear relationship.

[removed]

All of the above

Two different forecasting methods are applied to the same data set. If the Mean absolute deviation is higher for the second method and the Mean square error is higher for the first method, what does this indicate?

[removed]

Both methods produce unacceptably high error rates.

[removed]

The first method is less accurate than the second method.

[removed]

The first method produces more extreme errors than the second method.

[removed]

The second method produces more extreme errors than the first method.

[removed]

None of the above

In exponential smoothing with trend, the forecast consists of:

[removed]

a moving-average and a trend factor

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the old forecast adjusted by a trend factor.

[removed]

the old forecast and a smoothed trend factor.

[removed]

a trend line built using a linear regression model.

[removed]

None of the above

Which of the following is an example of a seasonal pattern in data?

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The data values are continually increasing with time.

[removed]

The data exhibits an exponentially smoothed pattern.

[removed]

The data variation is erratic in most quarters.

[removed]

The volume of calls is dependent upon the product release schedule.

[removed]

None of the above

The president of State University wants to forecast student enrollment for this academic year based on the following historical data:

Year

Enrollments

5 years ago

25,000

4 years ago

26,000

3 years ago

28,000

2 years ago

30,000

Last year

32,000

What is the forecast for this year using exponential smoothing with alpha = 0.5, if the forecast for two years ago was 28,000?

[removed]

28,000

[removed]

28,500

[removed]

29,000

[removed]

30,500

[removed]

None of the above

Gradual, long-term movement in time-series values is called:

[removed]

seasonal variation.

[removed]

cyclical movement.

[removed]

random variation.

[removed]

linear regression.

[removed]

None of the above.

Using the following data, what is the moving-average forecast for the next period using a three period model?

[removed]

58

[removed]

60

[removed]

61

[removed]

62

[removed]

None of the above

Which of the following statements is true regarding the Mean absolute deviation measure and the Mean square error measure?:

[removed]

The values for both measures are unchanged if the forecasting method is changed.

[removed]

Both are measures of seasonal variation.

[removed]

Lower values of these measures reflect more accurate forecasting methods.

[removed]

The Mean absolute deviation measure places greater weight on large errors.

[removed]

None of the above.

The president of State University wants to forecast student enrollment for Year 3. Using exponential smoothing with trend, the forecast for the current year (Year 1) is 22,000. Assume the estimated trend is 2,500. What is the forecast for Year 3?

[removed]

24,500.

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27,000.

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29,500.

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32,000.

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34,500.

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