How to Calculate Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE)
Question:
Can you explain how to calculate Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) using the given data?
Answer:
The MAD (Mean Absolute Deviation) value can be calculated by taking the average of the differences between the actual demand and the average demand. For example, for the actual demand values of 2480, 2520, and 2700, the average demand is calculated as (2480 + 2520 + 2700) / 3 = 2573.33. The MAD value is then calculated as the average of the absolute differences, which is (|2480 - 2573.33| + |2520 - 2573.33| + |2700 - 2573.33|) / 3 = 100 units.
The MAPE (Mean Absolute Percent Error) value can be calculated by taking the average of the absolute percent error between the actual demand and the average demand. This is calculated by taking the absolute difference between each actual demand value and the average demand, dividing it by the average demand, and then averaging these values. For the given data, the MAPE is calculated as (|2480 - 2573.33| / 2573.33 + |2520 - 2573.33| / 2573.33 + |2700 - 2573.33| / 2573.33) / 3 = 3.89%.
The OMAD (Ordinary Mean Absolute Deviation) value is calculated by taking the absolute difference between the actual demand and the forecasted demand. In this case, it is calculated as (|2480 - 2520| + |2520 - 2700|) / 2 = 50 units. The OMAPE (Ordinary Mean Absolute Percent Error) value is then calculated by taking the absolute percentage difference between the actual demand and the forecasted demand, which is (|2480 - 2520| / 2520 + |2520 - 2700| / 2700) / 2 = 7.89%.
Therefore, the correct values for MAD, MAPE, OMAD, and OMAPE are:MAD: 100 units
MAPE: 3.89%
OMAD: 50 units
OMAPE: 7.89%
Understanding Mean Absolute Deviation and Mean Absolute Percent Error
Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) are important metrics used in statistical analysis to measure the accuracy of forecasts and predictions.
MAD represents the average of the absolute differences between forecasted values and actual values. It provides a measure of how spread out the data points are from the average. A lower MAD indicates a more accurate forecast.
MAPE, on the other hand, measures the average percentage difference between actual and forecasted values. It gives a relative error percentage, making it easier to compare accuracy across different datasets. A lower MAPE indicates a more accurate forecast.
By calculating MAD and MAPE, companies can evaluate the performance of their forecasting models and make necessary adjustments to improve accuracy and efficiency in predicting demand or sales.