Question: A marketing manager thought that 70 people would turn up for the promotional event but instead 80 did. Calculate Percentage Absolute Error (PAE)
There are various methods for testing statistical accuracy in a given forecast. Some of them are simple and inexpensive; others are quite complex and difficult. This testing is needed to avoid/reduce the margin of forecasting error and thereby to improve the decision-making. The ‘absolute level of forecasting error’ is equal to the difference between the actual value and the forecast value. Graphically, it is measured by the vertical distance between the forecast value curve and the 45 degree line (showing perfect accuracy due to coincidence of forecast value and realised value) for a particular period. If forecasts are made for more than one year, then average absolute level of error is found out by taking the arithmetic mean of the absolute values of forecasting errors of different periods. However, Percentage Absolute Error (PAE) test is better, which is mathematically shown as follows:
[|70 – 80|/80] × 100 = (10/80) × 100 = 12.5%
Hence, there was an error of 12.5% in judgement.
PAE = 12.5%