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Evaluate trendless data; forecasts to improve communciation

1. How can trendless data be evaluated?

How does a trailing-moving average compare to a centered-moving average?

When should exponential smoothing be used for data? Explain with an example.

In exponential smoothing, what type of smoothing constant should be chosen for little smoothing compared with moderate smoothing?

Justify your answers using examples and reasoning.

2. How can forecasts improve communication in an organization?

Why do forecasts typically go wrong?

What can a researcher do to increase the chances that a forecast will be effective?

Are more complicated forecasting models, such as autoregressive integrated moving average (ARIMA) and autoregressive (AR), typically better at forecasting than less complicated models? Explain.

Justify your answers using examples and reasoning.

Solution Preview

Answers with explanations are included in 421875.doc file attached below.

1. How can trendless data be evaluated?
We can use exponential smoothing to evaluate trendless data. Exponential smoothing provides us with a way of weighing previous observations. Each smoothed value is a weighted average of the current observation and the smoothed value of the previous observation. The weights decline exponentially as we go back over time.

How does a trailing-moving average compare to a centered-moving average?

First of all trailing-moving average (or moving average) refers to a certain time period up until the present. For example, a 12-month trailing period would refer to the last 12 months up until this month. A 12-month trailing-moving average for a company's income would be the average monthly income over the last 12 months. Trailing-moving average can help smooth out fluctuating or cyclical data series. On the other hand the centered moving averages are the arithmetic means of the successive moving ...

Solution Summary

The solution assists in evaluating trendlessness data and forecasts to improve communication.

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