Standard error

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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.
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exact" that fits GAMs and uses this alternative method to estimate standard errors for the fitted parameters.
Using the estimates and standard errors from the union and nonunion data, confidence intervals were formed to determine whether differences between the estimates met guidelines for statistical significance.
Although they have been shown to be quite useful, multilevel models are susceptible to outliers occurring at each level of the data, leading to parameter estimation bias and inflated standard errors (e.
In Table 2, the regression coefficients and standard errors obtained from GEE and mixed model analysis applied to MAR, MCAR and complete data sets for the continuous result variable (linoleic acid) are almost same.
By failing to take into account the standard error of measurement, [a bright-line cutoff] not only contradicts the test's own design but also bars an essential part of a sentencing court's inquiry into adaptive functioning.
As shown in Table 1, in the absence of outliers, all three methods performed well, with the averaged estimates all nearly identical to the true value 1, the asymptotic standard errors all close to their empirical counterparts, and the type I error rate all close to the nominal level [alpha] = 0.
2009) focused on the standard errors associated with weighted descriptive estimates of population parameters.
Just as I described the differences between standard deviation and standard error in this editorial, I plan to help readers navigate this increasingly complex range of statistical testing being reported in results sections in JVIB in future Statistical Sidebars.
The standard error statistic is a measurement of sampling error.
The standard error of a statistic can be used to construct confidence intervals, or likely ranges for the true value given the specific estimate obtained from the sample.
If the null hypothesis of exogeneity is rejected, OLS estimates are inconsistent and conclusions about causal effects should be based on IV estimates; failure to reject the null means that OLS estimates may be interpreted as causal effects are preferable because of their smaller standard errors.
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