The phrase "a common variance" is correct and usable in written English.
It can be used in contexts related to statistics, finance, or any field where variance is discussed, indicating a shared or typical level of variance among a group.
Example: "In our analysis, we found that a common variance exists among the data sets, suggesting similar patterns in the results."
Alternatives: "a shared variance" or "a typical variance".
In other words, we estimate 502 separate lognormal distributions for the 502 different job categories, each of which has a separate mean and a common variance parameter.
A common variance (CV) design is proposed for collecting the data so that all the uncommon parameters are estimated with as similar variances as possible in all models.
The estimate of a common variance from two independent samples is obtained by "pooling," which is simply the weighted mean of the two individual variance estimates with the weights being the degrees of freedom for each variance.
A new property Pg v1,…,vg) of designs is introduced in this context to least squares estimate the uncommon parameters in g groups of models so that the estimates of vi such parameters in the ith group have a common variance (CV), where g is an integer satisfying 1≤g≤(m2), i= 1,…,g, v1+⋯+vg="(m2).
complex Gaussian random variables with zero mean and a common variance δi*.
Various methods have been derived that are designed to test the hypothesis that two dependent variables have a common variance.
The prior distributions on the random effects for instructor and institution, respectively, were mean-zero normal distributions with a common variance.
Note that the noise terms of ARX models are always assumed to follow an independent normal distribution with a common variance.
Without loss of generality, we assume that all the entries of (mathbf {x}_{i}in {mathcal {C}}^{N}) are identically and independently distributed random variables with zero mean and a common variance.
Given that A=|A|e jϕ is a complex-valued constant, and N= N R + j N I is a complex-valued random variable which real and imaginary parts, N R and N I, are independent Gaussian distributed with zero mean and a common variance σ2, then it can be shown that [38]: 1.
Attributing a common variance to all the genes is clearly not a solution, even when sample sizes are small.
