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  2. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    Thus to compare residuals at different inputs, one needs to adjust the residuals by the expected variability of residuals, which is called studentizing. This is particularly important in the case of detecting outliers, where the case in question is somehow different from the others in a dataset. For example, a large residual may be expected in ...

  3. Observational error - Wikipedia

    en.wikipedia.org/wiki/Observational_error

    When either randomness or uncertainty modeled by probability theory is attributed to such errors, they are "errors" in the sense in which that term is used in statistics; see errors and residuals in statistics. Every time a measurement is repeated, slightly different results are obtained.

  4. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    These deviations are called residuals when the calculations are performed over the data sample that was used for estimation (and are therefore always in reference to an estimate) and are called errors (or prediction errors) when computed out-of-sample (aka on the full set, referencing a true value rather than an estimate). The RMSD serves to ...

  5. R-factor (crystallography) - Wikipedia

    en.wikipedia.org/wiki/R-factor_(crystallography)

    Random experimental errors in the data contribute to even for a perfect model, and these have more leverage when the data are weak or few, such as for a low-resolution data set. Model inadequacies such as incorrect or missing parts and unmodeled disorder are the other main contributors to R {\displaystyle R} , making it useful to assess the ...

  6. Approximation error - Wikipedia

    en.wikipedia.org/wiki/Approximation_error

    For absolute errors, the opposite is true: are sensitive to multiplication by constants, ... Errors and residuals in statistics; Experimental uncertainty analysis;

  7. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    The general regression model with n observations and k explanators, the first of which is a constant unit vector whose coefficient is the regression intercept, is = + where y is an n × 1 vector of dependent variable observations, each column of the n × k matrix X is a vector of observations on one of the k explanators, is a k × 1 vector of true coefficients, and e is an n× 1 vector of the ...

  8. Category:Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Category:Errors_and_residuals

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  9. Probability of error - Wikipedia

    en.wikipedia.org/wiki/Probability_of_error

    This statistics -related article is a stub. You can help Wikipedia by expanding it.