Ne use a moving average forecast method to develop foreceasts of sales/demand. Based on an analysis of outliers in our sales data, we surmise that most outliers were caused y random events. Should we go 'long' or 'short' (take more or fewer periods) in our determination of the \# of periods to use for computing our moving average forecasts? Go 'short' - Use fewer periods since we wish to stay include outliers while developing our forecasts Doesn't make a difference since the outliers are random in occurence. Go 'long' - Use more periods since we do not want to include outliers while developing our forecasts