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  2. Experimental uncertainty analysis - Wikipedia

    en.wikipedia.org/wiki/Experimental_uncertainty...

    Systematic errors in the measurement of experimental quantities leads to bias in the derived quantity, the magnitude of which is calculated using Eq(6) or Eq(7). However, there is also a more subtle form of bias that can occur even if the input, measured, quantities are unbiased; all terms after the first in Eq(14) represent this bias.

  3. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    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 ...

  4. Burst error-correcting code - Wikipedia

    en.wikipedia.org/wiki/Burst_error-correcting_code

    Proof. We need to prove that if you add a burst of length to a codeword (i.e. to a polynomial that is divisible by ()), then the result is not going to be a codeword (i.e. the corresponding polynomial is not divisible by ()).

  5. BCH code - Wikipedia

    en.wikipedia.org/wiki/BCH_code

    Over () (i.e. with binary BCH codes), this process is indistinguishable from appending a cyclic redundancy check, and if a systematic binary BCH code is used only for error-detection purposes, we see that BCH codes are just a generalization of the mathematics of cyclic redundancy checks.

  6. Heteroskedasticity-consistent standard errors - Wikipedia

    en.wikipedia.org/wiki/Heteroskedasticity...

    An alternative to explicitly modelling the heteroskedasticity is using a resampling method such as the wild bootstrap. Given that the studentized bootstrap, which standardizes the resampled statistic by its standard error, yields an asymptotic refinement, [13] heteroskedasticity-robust standard errors remain nevertheless useful.

  7. Symmetric mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Symmetric_mean_absolute...

    In contrast to the mean absolute percentage error, SMAPE has both a lower and an upper bound. Indeed, the formula above provides a result between 0% and 200%. Indeed, the formula above provides a result between 0% and 200%.

  8. Technique for human error-rate prediction - Wikipedia

    en.wikipedia.org/wiki/Technique_for_human_error...

    For each step of the task, possible errors are considered by the analyst and precisely defined. The possible errors are then considered by the analyst, for each task step. Such errors can be broken down into the following categories: Errors of omission – leaving out a step of the task or the whole task itself

  9. Error function - Wikipedia

    en.wikipedia.org/wiki/Error_function

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