The most commonly used measures of forecast accuracy are the:

A) mean absolute deviation and the sum of squares for forecast errors
B) sum of squares for forecast error and seasonal indexes
C) seasonal indexes and the percentage of trend
D) all of these choices are correct

The mean absolute deviation (MAD) and the sum of squares for forecast errors (SSE) are the most commonly used measures of forecast accuracy because they are easy to calculate and understand. MAD measures the average absolute deviation of the forecast from the actual values, while SSE measures the variance of the forecast errors. Both provide a measure of how accurate the forecast is, and can be used to compare different forecasting methods or models. Seasonal indexes and percentage of trend are also important measures, but are typically used in conjunction with MAD and SSE to provide a more complete picture of forecast accuracy.