The mean absolute deviation is the sum of the absolute value of forecasting errors divided by the number of forecasts.
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Q5: Exponential smoothing with trend was designed for
Q6: Forecasts are rarely perfect.
Q7: The last-value forecasting method requires a linear
Q8: The moving-average forecasting method assigns equal weights
Q9: A smoothing constant of 0.1 will cause
Q11: When no historical sales data is available,
Q12: The averaging method uses all the data
Q13: The difference between a forecast and what
Q14: The mean absolute deviation is more sensitive
Q15: Removing the seasonal component from a time-series
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