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Discover LudwigThe phrase "an element of variance" is correct and usable in written English.
It can be used when discussing variability or differences within a set of data or a particular context.
Example: "In our analysis, we found that there is an element of variance in the results, indicating that external factors may be influencing the outcomes."
Alternatives: "a factor of variability" or "a degree of variation".
Exact(1)
Inevitably there would be an element of variance in these variables and also an element of recall bias from interview data.
Similar(59)
Yet, it is an element of the variance that can be measured very precisely and will play an important role in the design of future studies.
Include an element of water.
V i is constructed as V i = ϕ B i 1 / 2 R α B i 1 / 2, where B i = diag b ″ θ i t is a diagonal matrix with main diagonal elements of variance function, b ″ θ i t, and R is a correlation matrix.
zero-mean, circular-symmetric complex Gaussian-distributed elements of variance.
zero-mean, circular-symmetric complex Gaussian-distributed elements of variance, (vi) is the channel matrix from the source to destination with i.i.d.i.d
zero-mean, circular-symmetric complex Gaussian-distributed elements of variance, is the channel matrix from the source to destination with i.i.d.i.d
zero-mean, circular-symmetric complex Gaussian-distributed elements of variance (i.e., the real and imaginary parts of each elements are independent zero-mean Gaussian random variables with the same variance), is the noise vector with i.i.d.i.d
zero-mean, circular-symmetric complex Gaussian-distributed elements of variance (i.e., the real and imaginary parts of each elements are independent zero-mean Gaussian random variables with the same variance), (v) is the noise vector with i.i.d.i.d
Relationships between genotyped animals (1,989 diagonal and 3,954,132 off-diagonal elements) Estimates of variance components obtained with the full data set are in Table 2, and estimates from the subset are in Table 3.
The ijth element of the variance-covariance matrix V (the covariance between xi and xj when i≠j and Vii is the variance of xi) is calculated as V i j = max Q i j L, where, Q i j L = 1 2 n 2 ∑ a ∑ b x i a - x i b ′ L x j a - x j b, Where, L is an orthogonal matrix (orthogonal transformation) [ 30], in our case L = 1.
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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