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SE = standard error; β = standardized path coefficient; SEE = standard error of estimate (estimated standard deviation of the residual variable).
The studentized residual is the division of raw residual by its estimated standard deviation.
Fig. 10 Estimated standard deviation vs. true standard deviation for Gaussian noise.
Hence, the programme tests whether the estimated standard deviation lies between 0 and 1, characterising an unacceptable bimodal distribution.
Studentized residuals in Fig. 1 are the residuals divided by the estimated standard deviation of that residual.
We define the error on distance parameter as two times the estimated standard deviation of the logarithmic distance frequency distribution.
The estimated standard deviation (σd) of ln(Dp) within each sample ranges from 0 to 1, with most values lying between 0.5 and 1.
However SRE2 required 4.5 times more to be equivalent or better than COL. Figure 11 shows the estimated standard deviation w.r.t. the number of flops.
Figure 9 Error on distance (defined as two times the estimated standard deviation of the logarithmic distance frequency distribution) for the sending nodes on the third floor.
Figure 10 shows the estimated standard deviation of the focal length error and principal point obtained from the three estimates w.r.t.
A sample size of 12 produces a two-sided 95%% confidence interval with a margin of error of 2 mm when the estimated standard deviation is 3.000.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com