The overall model was also significant (p <0.001, adjusted R = 0.37).> -wrap-foot> *A change in the standardized coefficient by the standard error of the regression parameter changes the dependent variable (support for CMV prevention behavior score) by 1 standard error of the dependent variable.
Meanwhile the standardized estimates of differential expression (y i ) are adjusted for all other sources of variation fitted in the within-study model (i.e. dye and array effects), and standardized by the standard error of the estimate.
Because beta coefficient is potentially affected by the standard error of the variable, the standardized beta adjusted for this error is considered.
The standardized effects (T) are obtained by dividing the regression coefficients by the standard error coefficient (Alimi et al. 2014).
The proposed models are validated by the standard error of estimate and observation versus estimation graph.
The results are broken down by land classes; the accuracy is quantified by the standard error.
Furthermore, to investigate if the regression coefficients of the predictor variables differed between models, we used the approach by Cohen32 in which the difference between the predictors' regression weights is divided by the standard error of the difference and the resulting z-score is tested for significance.
As outlined above, the z-statistic equals the estimate minus the value of interest, all divided by the standard error.
The z-score of a sample mean equals the sample mean minus the population mean, all divided by the standard error of the sample mean.
Small white circles indicate a difference that is outside the error bars defined by the standard error of the mean.
Because estimates are inherently heteroskedastic, it is appropriate to divide Equation (1) by the standard error to yield [20, 21].
