**Sources of Error**

Errors can come from a variety of sources. One common source of which many forecasters' are unaware is caused by the projection of pa t trends into the future. For example, when we talk about statistical errors in regression analysis. we are referring to the deviations of observation from our regression line. It is common to attach a confidence band to the regression

line to reduce the unexplained error. However. when we subsequently use this regression line as a forecasting device by projecting it into the future. the error may not be correctly defined by the projected confidence band. This is because the confidence interval is based on past data; consequently it mayor may not be totally valid for projected data points and therefore cannot be used with the same confidence. In fact experience has shown that the actual errors tend to be greater than those predicted from forecasting models.

Errors can be classified as either bias or random. Bias errors occur when a consistent mistake is made, that is. the forecast is always too high or always too low. Sources of bias include (a) failing to include the right variables. (b) using the wrong relationships among variables. (c) employing the wrong trend line. (d) mistakenly shifting the seasonal demand from where it normally occurs. and (e) the existence of some undetected trend. Random errors can be defined simply a those that cannot be explained by the forecast model being used. These random errors are often referred to as "noise" in the model.