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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 ...
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For absolute errors, the opposite is true: are sensitive to multiplication by constants, ... Errors and residuals in statistics; Experimental uncertainty analysis;
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 ...
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.
non-constant variation across the data: scatter plots of residuals versus predictors; for data collected over time, also plots of residuals against time; drift in the errors (data collected over time): run charts of the response and errors versus time; independence of errors: lag plot; normality of errors: histogram and normal probability plot
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Pages in category "Errors and residuals" The following 12 pages are in this category, out of 12 total. ... Statistics; Cookie statement; Mobile view; Search. Search.