# Standard error

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Related to Standard error: variance

## Standard error

In statistics, a measure of the possible error in an estimate. Plus or minus 2 standard errors usually provides a 95% confidence interval.

## Standard Error

An estimate of the standard deviation of a data set. See also: Confidence interval.
References in periodicals archive ?
Mean bias, average standard error, and empirical standard error of nonparametric and parametric estimation are presented in Tables A1-A6.
The top panel of Table 2 contains the estimation bias, standard error, and coverage rates for [[beta].sub.1] by method, averaged across the other study variables.
7 statistical results are with a mean of 2.75, standard error 0.13 and standard deviation 1.347.
The API for each subject was calculated and an average value of 14.8 (CI 95 % 14.75 to 14.86) was obtained, with a standard error of the mean of 0.03, a variance of 0.18 and a standard deviation of 0.42.
In the second phase, parameter estimations and standard errors are obtained from m completed data sets.
Their formulation of the asymptotic standard error of [??] is obtained while fixing [chi] and N and allowing [N.sup.*] to approach [infinity].
For purposes of the preceding discussion of the role of standard error of measurement, and other factors affecting the interpretation of IQ scores, we have assumed that "significantly subaverage intellectual functioning" is operationalized as scoring 70 or below on a specific IQ test.
As shown in the Table 1, both the classic and semi-parametric models yielded extremely large estimates that are un-interpretable, impossibly large standard errors, and type I errors close to 1.
The relative standard error of a natural gas volume estimate is the square root of the variance of the estimate
As a result, the November CPI release, which is based upon October data, will have a much bigger standard error due to the smaller sample.
(12) the effect of missing data and differential item functioning on latent trait estimates from two polytomous Rasch models and different imputation methods (complete-case analysis, mean substitution, hot-decking, and multiple imputation based on multivariate normal) were compared using the MCAR mechanism, and they found that the presence of data associated with missingness increases the standard error of latent trait estimates but does not impact the bias in theta estimates in the MCAR scenario.
This not only has an effect on the estimates of the fixed effects, but also on their standard error. This research investigates this phenomenon on a selected SCD, and compares the results with the analyses of other people on the panel.
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