In picking the smoothing constant for an exponential smoothing model, we should look for a value

a. produces a nice-looking curve.
b. produces values which compare well with actual values based on a standard measure of error.
c. causes the least computational effort.
d. equals the utility level that matches with our degree of risk aversion.
e. none of the above

Answer is the one which produces values which compare well with actual values based on a standard measure of error.

Explanation:

Exponential smoothing is one means of preparing short-term sales forecasts on a routine basis. To use exponential smoothing, however, one must decide the proper values for the smoothing constants in the forecasting model. One method for selecting the smoothing constants involves conducting a grid search to evaluate a wide range of possible values.

Exponential smoothing forecasting methods use constants that assign weights to current demand and previous forecasts to arrive at new forecasts.   Their values influence the responsiveness of forecasts to actual demand and hence influence forecast error. Considerable effort has focused on finding the appropriate values to use.

One approach is to use smoothing constants that minimize some function of forecast error. Thus, in order to select the right constants for forecasting, different values are tried out on past time series, and the ones that minimize an error function like Mean Absolute    Deviation (MAD) or Mean Squared Error (MSE) are the ones used for forecasting

answer; the correct answer to the above statement is (true);