1. Does a moving average forecast become more or less responsive to changes in a

Question

1. Does a moving average forecast become more or less responsive to changes in a data series when more data points are included in the average?

2. If a forecast is too high when compared to an actual outcome, will that forecast error be positive or negative?

3. Are quantitative forecasting models generally used for shorter-term or longer-term decision making when compared to qualitative approaches?

4. The South Florida Water Management District (SFWMD) must develop a linear regression model that can be used to estimate the fresh water needs of various com- munities. SFWMD has collected data on 50 communities, noting each community’s population and total annual fresh water consumption. Using this data, you have calculated the following regression equation: Y = 200.12 + 24.9X

Explanation / Answer

1. ANS: false

The moving average approach considers the recent (previous) “n” periods’ actual demand figures and then, calculates the average demand over the “n” periods and uses this average as a forecast for the next period’s demand. With smaller value of n or only recent period data is considered the moving average will track shifts in the time series more quickly, thus it becomes more responsive when recent periods are considered. If large value of n or more past data is considered, the moving average forecasting averages out the random variation, thus it is less responsive to the recent changes in time series.

2. Negative

Forecast error is given as difference between the actual value and forecast value.

Error = Actual – forecast

Thus, if forecast is less than actual, the error is positive. When forecast is greater than actual the error is negative.

3. Long-term decisions

Qualitative methods consist mainly of subjective inputs, often of non-numerical description. They are based on opinions and judgmental estimation. These methods are most applied for the long-term decisions for which certain data is not known.

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