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Continual review and updating in light of new data is a forecasting technique called second-guessing.

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Cyclical influences on demand are often expressed graphically as a linear function that is either upward or downward sloping.

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Cyclical influences on demand may come from occurrences such as political elections, war, or economic conditions.

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Trend lines are usually the last things considered when developing a forecast.

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Time series forecasting models make predictions about the future based on analysis of past data.

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In the weighted moving average forecasting model the weights must add up to one times the number of data points.

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In a forecasting model using simple exponential smoothing the data pattern should remain stationary.

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In a forecasting model using simple moving average, the shorter the time span used for calculating the moving average, the closer the average follows volatile trends.

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In the simple exponential smoothing forecasting model you need at least 30 observations to set the smoothing constant alpha.

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Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model.

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Bayesian analysis is the simplest way to choose weights for the weighted moving average forecasting model.

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The weighted moving average forecasting model uses a weighting scheme to modify the effects of individual data points. This is its major advantage over the simple moving average model.

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A central premise of exponential smoothing is that more recent data is less indicative of the future than data from the distant past.

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The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast.

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Exponential smoothing is always the best and most accurate of all forecasting models.

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In exponential smoothing, it is desirable to use a higher smoothing constant when forecasting demand for a product experiencing high growth.

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The value of the smoothing constant alpha in an exponential smoothing model is between 0 and 1.

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Exponential smoothing forecasts always lag behind the actual occurrence but can be corrected somewhat with a trend adjustment.

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Because the factors governing demand for products are very complex, all forecasts of demand contain error.

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