Unformatted text preview: can average the entries in that column. But you should realize that b/c the negatives and positives cancelled each other out, this average is not very representatives of the accuracy of the model. Afterall, an error is an error, whether the forecast is higher or lower than the actual value. In the column where absolute values are reported, you ignore the direction of the error and report the absolute value of error. So when you average that column, you call that measure of accuracy: MAD (Mean Absolute Deviation). MAD is described in one of the notes and comments sections of the chapter. In the other column, the error is squared to ignore the direction of error. Of course, by squaring each error term, we end up with big entries in that column b/c the squaring process magnifies the error terms. But again, you can average them as a measure of forecast accuracy which is called MSE. This has been demosntrated in the book and in the PPTs. Dr. J....
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 Spring '08
 Nabavi
 Regression Analysis, Deviation, Errors and residuals in statistics, Time series analysis, Absolute deviation

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