Your English writing platform
Discover LudwigSuggestions(5)
Exact(9)
In (5), the correlation coefficient represents the correlation between and and the coefficient denotes the correlation between and.
where (sigma ^{2}_{DL_{M}}, sigma ^{2}_{DL_{j}}) are the variances of the daily liquidity measure of the market and stock j, respectively, and ρ denotes the correlation between D L M and D L j.
The coefficient R j, i = E h d, j ∗ h p, i denotes the correlation between the channels at the j th data symbol and the i th pilot symbol positions.
end{aligned} (14 where (hbox {cor}(hat{mathbf {s}}^{i}, hat{mathbf {s}}^{j})) denotes the correlation between (hat{mathbf {s}}^{i}) and (hat{mathbf {s}}^{j}, bar{phi }_{i} = int _{mathbb {R}^{d}} s |phi _{i}|^{2} ds / int _{mathbb {R}^{d}} |phi _{i}|^{2} ds) defines de center of mass of (phi _{i}) and (l_{i}) is the scale index of (phi _{i}) in the wavelet decomposition.
More precisely element cp denotes the correlation between the population firing evoked by pattern p and the normalized instantaneous population firing r.
To correct for repeated measurements and the use of the baseline measurement as a covariate, we multiplied the required sample size by the design factor 1 + ρ / 2 − ρ 0 2, where ρ denotes the correlation between the post-treatment HADS measurements, and ρ0 denotes the correlation between the baseline HADS with the post-treatment HADS measurements.
Similar(51)
The nonparametric Rho scores in the table denote the correlation between the binding potential values provided by the methods; *statistically significant correlations (two-tailed tests, Bonferroni correction for multiple comparisons, p ≤ 0.0016).
denotes the correlation degree between and active cluster member for.
The second tabular column denotes the correlation factors between downward drift and the corresponding F2-layer peak data.
(Psi _{mathrm {h}{overline {text {y}}}^{(E)}}) denotes the correlation matrix between h and ({overline {text {y}}}^{(E)}).
The parameter ρ in Equation 1 denotes the correlation coefficient between the probability of a pixel being a true edge and being detected as edge.
Write better and faster with AI suggestions while staying true to your unique style.
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