Examples of using Standard errors in English and their translations into Indonesian
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Colloquial
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Ecclesiastic
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Computer
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Ecclesiastic
Standard Errors from mean.
Bars represent estimated standard errors.
Standard errors around estimates are not available.
Bars represent estimated standard errors.
Estimating standard errors in finance panel data sets: Comparing approaches.
Bars represent estimated standard errors.
Standard errors provide simple measures of uncertainty in a value and are often used because.
Bars represent the calculated standard errors.
They are standard errors, which are much lower than the corresponding standard deviations.
Data are presented as means±standard errors(SE).
Secondly, the standard errors are already calculated for us, making it straightforward to calculate confidence intervals.
The data are expressed as means±standard errors(SE).
Similarly, they can see if the published standard errors or standard deviations- expressed as error bars- are adequately reflective of the raw data.
In all three models the numbers in parentheses are standard errors.
The effects of non-normality on estimates, standard errors, and tests of model fit are well known.
EViews optionally reports generalized linear model or QML standard errors.
We noted earlier that the OLS estimators and their standard errors are sensitive to small changes in the data.
The consequences of multicollinearity are as follows: If there is perfect collinearity among the X's,their regression coefficients are indeterminate and their standard errors are not defined.
We noted earlier that the OLS estimators and their standard errors are sensitive to small changes in the data.
The consequences of multicollinearity are as follows: If there is perfect collinearity among the X's,their regression coefficients are indeterminate and their standard errors are not defined.
I have also output the aysmptotic variance so that we can calculate standard errors for the parameters and produce confidence intervals.
Standard errors are calculated by dividing the standard deviations by the square root of the sample sizes for each trajectory point(which the authors do not provide either), so they go up with age since progressively smaller numbers of individuals reach advanced ages.
If collinearity is high but not perfect,estimation of regression coefficients is possible but their standard errors tend to be large.
Results of the statistical testing, and also parameter estimates and standard errors for the numerical coefficients in the linear equations are reported.
If collinearity is high but not perfect,estimation of regression coefficients is possible but their standard errors tend to be large.
They point out that later studies, which had more data and lower standard errors, did not show the expected increase in t-statistic(almost all the studies had a t-statistic of about two, just above the level of statistical significance at the .05 level).
If 95% confidence intervals for these two means are calculated(approximately)by adding or subtracting two standard errors, the intervals do not overlap, so the difference in means is statistically very significant.
Unlike a sample, when carrying out statistical analysis on a population,there is no standard errors to report- that is because such errors inform analysts using a sample how far their estimate may deviate from the true population value; but since you are working with the true population you already know the true value.
Unlike a sample, when carrying out statistical analysis on a population,there are no standard errors to report- that is, because such errors inform analysts using a sample how far their estimate may deviate from the true population value.