standard error

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standard error

n.
A generalized notion of standard deviation, used to characterize the dispersion of a statistic (such as the sample mean) calculated from randomly sampled data.

standard error

n
(Statistics) statistics the estimated standard deviation of a parameter, the value of which is not known exactly
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2003) have shown that the estimated standard errors produced by S-Plus can systematically underestimate the true variability of fitted linear parameters in semiparametric GAMs.
For the new data on union and nonunion workers presented here, standard errors for each estimate were computed.
0481) Notes: Figuren in parenthesis are standard errors which have been indirectly estimated from the variance-covariance matrix.
linear regression, logistic regression, and analysis of variance) do not properly account for the nested structure of such data, and can yield biased parameter estimates, and incorrect standard errors (Bryk & Raudenbush, 2001).
In the second phase, parameter estimations and standard errors are obtained from m completed data sets.
Third, a fixed cut-off score would ignore the fact that different IQ tests have different standard errors of measurement (SEM).
In the table, values in the column titled 'mean' are the averaged estimates of each parameter over 1000 Monte Caro replications; the 'asymptotic standard error' is the model-based standard error; the 'empirical standard error' is the standard errors of the 1000 estimates of each parameter; and the 'type I error' is the percent of times the null hypothesis--that the estimated parameter is equal to the true parameter--is rejected.
2009) demonstrated how neglecting to incorporate complex sample design information results in substantially smaller standard errors and an increased risk of Type I errors.
In this issue, Siu and Morash do not use standard deviations at all, but instead use standard errors (sometimes called standard error of the mean).
The standard errors for monthly natural gas volume and price estimates by State are given in Table C1.
We can estimate how much bigger by weighing the potential size of the October sample against typical sample sizes, historical standard errors, and the likely inflation rate.

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