Sentence examples for standard deviation describes from inspiring English sources

The mean solution describes the average ability of the algorithm to find the global solution, and the standard deviation describes the variation in solution from the mean solution.

Since the standard deviation describes the polydispersity of primary particles but DLS measurements takes also agglomerates into account, in case of silica nanoparticles, the overestimation might be caused by the polydispersity arising from the agglomeration.

These data are presented in Table 1, where the standard deviation describes the day-to-day error in the measured diffusion coefficient of each of these proteins.

Standard deviation describes the spread of pixel values relative to the mean; high values of standard deviation correspond to a wider image histogram with a wide range of pixel intensities.

From the c(s) analysis, the weighted average sedimentation coefficients (s20,w) for Fas DD WT and Fas DD V254N were determined to be 1.5 ± 0.3 and 1.6 ± 0.4 S, respectively, where the standard deviation describes the broadness of the peak.

Single image texture measures such as the standard deviation described up to 57% of the variability in species richness.

We do not consider invalid cue trials because their small number precludes statistical analyses and also because the high values of individual standard deviation described above cast serious doubts on the meaningfulness of RT as a measure of performance on invalid cue trials.

When the standard deviation was not given in the methods section, we used the standard deviation described in the results section and verified the sample size using formulae for the Student's t test or for bioequivalence trials [ 10, 11].

Test-retest reliability, as applied to the overall summated DASS score, was assessed using the intra-class correlation coefficient, and also by summary statistics (mean, standard deviation) describing the differences between the DASS scores at the two time periods.

The linear drift and diffusion (φe) of the execution process is shown by the stochastic differential equation in Equation 3, accumulating with a mean rate of ve (i.e., drift rate) and a standard deviation described by the Wiener diffusion process (e.g., white noise) with diffusion constant  σ.

A typical functional form for a selection curve of this type is a Gaussian curve (Millar and Fryer 1999): (2) where r(l) and l are as above, μ is the length at which the curve peaks and σ is standard deviation describing the width of the curve about its peak.