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0.84 0.994 457 883 210: 0.9995 3.290 526 731 492: 0.95 1.644 853 626 951: 0.99995 3.890 591 886 413: 0.975 1.959963984540: 0.999995 4.417 173 413 469: 0.99 2.326 347 874 041: 0.9999995 4.891 638 475 699: 0.995
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Percentage error; Mean absolute percentage error; Mean squared error; Mean squared prediction error; Minimum mean-square error; Squared deviations; Peak signal-to-noise ratio; Root mean square deviation; Errors and residuals in statistics
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EVM is generally expressed in percent by multiplying the ratio by 100. [ 1 ] The ideal signal amplitude reference can either be the maximum ideal signal amplitude of the constellation, or it can be the root mean square (RMS) average amplitude of all possible ideal signal amplitude values in the constellation.
The MSE of an estimator ^ with respect to an unknown parameter is defined as [1] (^) = [(^)]. This definition depends on the unknown parameter, but the MSE is a priori a property of an estimator.
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.
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]