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This page was last edited on 21 December 2024, at 20:13 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
This page was last edited on 21 December 2024, at 20:12 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
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Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters.
M. Margin of error; Market risk; McKay's approximation for the coefficient of variation; Mean absolute difference; Mean absolute error; Mean absolute percentage error; Mean absolute scaled error; Mean square quantization error; Mean squared displacement; Mean squared error; Mean squared prediction error; Medcouple; Median absolute deviation ...
Common metrics of forecast error, such as mean absolute error, geometric mean absolute error, and mean squared error, have shortcomings related to dependence on scale of data and/or handling zeros and negative values within the data.
This page was last edited on 19 December 2024, at 01:32 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.