The measure of forecast error where the absolute amount of error of each forecast is averaged is

A) mean squared error (MSE) .
B) mean absolute deviation (MAD) .
C) mean absolute percentage error (MAPE) .
D) bias.

Mean absolute deviation (MAD) measures the average absolute difference between the forecasted values and the actual values, making it the best choice for the described scenario. Choice A, mean squared error (MSE), squares the errors before averaging them, which may lead to large errors being overweighted. Choice C, mean absolute percentage error (MAPE), may be useful when comparing forecasts of different scales, but it cannot be used when actual values are equal to zero. Choice D, bias, measures the average deviation of forecast values from actual values, but it does not take into account the absolute values of the errors.